Agriculture

29 pages, 7209 KiB

Open AccessArticle

A Methodology for the Feasibility Assessment of Using Crop Residues for Electricity Production Through GIS-MCD and Its Application in a Case Study

by Fernando Bruno Dovichi Filho, Laura Vieira Maia de Sousa, Electo Eduardo Silva Lora, José Carlos Escobar Palacio, Pedro Tavares Borges, Regina Mambeli Barros, René Lesme Jaen, Marcelo Risso Errera and Quelbis Roman Quintero

Agriculture 2025, 15(3), 334; https://doi.org/10.3390/agriculture15030334 (registering DOI) - 3 Feb 2025

Abstract

Over recent decades, human activities have essentially depended on fossil fuels. The last Intergovernmental Panel on Climate Change reports recommend a shift to renewables and a more energy-efficient economy. To fulfill the potential of bioenergy, tools are required to overcome the complexities of [...] Read more.

Over recent decades, human activities have essentially depended on fossil fuels. The last Intergovernmental Panel on Climate Change reports recommend a shift to renewables and a more energy-efficient economy. To fulfill the potential of bioenergy, tools are required to overcome the complexities of the decision-making processes for viable projects. This work presents a decision-making tool to select the most feasible biomass residues and a case study of the state of Minas Gerais, in Brazil. Among the 13 evaluated criteria, eucalyptus residues demonstrated the highest potential for electricity production, followed by sugarcane bagasse and coffee husks. The choice of Minas Gerais as a case study is important due to its diverse agricultural landscape and the potential for biomass residue generation. The presented methodology uses the Analytical Hierarchy Process (AHP), a multi-criteria decision-making method (MCDM). Thirteen criteria were required to enable the best choice of biomass residue alternatives for electricity generation, which experts in the bioenergy field evaluated. The technical criterion was shown to be the one with the highest degree of importance. The results of the study identified that CO_2eq emissions (11.46%) and electricity demand (ED) were the most relevant sub-criteria for prioritizing the viability of agricultural waste. Eucalyptus was ranked as the most promising biomass, followed by sugarcane bagasse and coffee husks. In addition, the use of GIS tools made it possible to map the regions with the greatest potential in Minas Gerais, providing a robust approach to identifying strategic sites for bioenergy. Full article

(This article belongs to the Special Issue Agricultural Biomass Generation and Utilization: Progress, Challenges and Prospects)

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30 pages, 1595 KiB

Open AccessArticle

Assessing Honey Quality: A Focus on Some Physicochemical Parameters of Honey from Iasi County (Romania)

by Aida Albu, Florin Dumitru Bora, Simona-Maria Cucu-Man, Vasile Stoleru, Cătălin-Emilian Nistor, Ioan Sebastian Brumă and Oana-Raluca Rusu

Agriculture 2025, 15(3), 333; https://doi.org/10.3390/agriculture15030333 (registering DOI) - 3 Feb 2025

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Abstract

The study of honey in Iasi County reveals its ecological, economic and health importance, emphasizing its unique properties, role in biodiversity and value in promoting sustainable beekeeping and regional identity. This study aimed to investigate the characteristics of honey from Iasi County, Romania, [...] Read more.

The study of honey in Iasi County reveals its ecological, economic and health importance, emphasizing its unique properties, role in biodiversity and value in promoting sustainable beekeeping and regional identity. This study aimed to investigate the characteristics of honey from Iasi County, Romania, analyzing 27 samples collected in 2020 and 2021. The samples include tilia (8 raw, 7 commercial), acacia (2 raw, 2 commercial), rapeseed (3 raw), sunflower (3 raw) and lavender (2 raw) honey. Analyses were carried out under Romanian/EU standards, assessing parameters such as color, electrical conductivity, moisture, total soluble solids (TSS), acidity (free, lactone, total), pH, hydroxymethylfurfural (HMF), ash and mineral composition (Na, K, Ca, Mg, Cu, Zn, Mn and Fe). The results revealed significant differences between raw and commercial honeys. Notably, in commercial tilia honey, higher values were found for color (38.58 mm Pfund vs. 24.14 mm Pfund), total acidity (25.93 meq·kg⁻¹ vs. 17.36 meq·kg⁻¹) and HMF levels (8.84 mg·kg⁻¹ vs. 3.68 mg·kg⁻¹). Conversely, water-insoluble solids (0.08% vs. 0.15%) and ash content (0.21% vs. 0.30%) were lower in commercial samples. Potassium was the most abundant mineral detected, while copper and zinc levels were the lowest. Significant correlations were observed between several parameters, including ash with electrical conductivity and HMF with acidity. This study underscores the impact of processing on honey quality and highlights the importance of understanding honey composition for consumers and producers alike. Full article

(This article belongs to the Special Issue Quality Assessment and Processing of Farm Animal Products)

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21 pages, 10337 KiB

Open AccessArticle

Efficient Deployment of Peanut Leaf Disease Detection Models on Edge AI Devices

by Zekai Lv, Shangbin Yang, Shichuang Ma, Qiang Wang, Jinti Sun, Linlin Du, Jiaqi Han, Yufeng Guo and Hui Zhang

Agriculture 2025, 15(3), 332; https://doi.org/10.3390/agriculture15030332 (registering DOI) - 2 Feb 2025

Viewed by 335

Abstract

The intelligent transformation of crop leaf disease detection has driven the use of deep neural network algorithms to develop more accurate disease detection models. In resource-constrained environments, the deployment of crop leaf disease detection models on the cloud introduces challenges such as communication [...] Read more.

The intelligent transformation of crop leaf disease detection has driven the use of deep neural network algorithms to develop more accurate disease detection models. In resource-constrained environments, the deployment of crop leaf disease detection models on the cloud introduces challenges such as communication latency and privacy concerns. Edge AI devices offer lower communication latency and enhanced scalability. To achieve the efficient deployment of crop leaf disease detection models on edge AI devices, a dataset of 700 images depicting peanut leaf spot, scorch spot, and rust diseases was collected. The YOLOX-Tiny network was utilized to conduct deployment experiments with the peanut leaf disease detection model on the Jetson Nano B01. The experiments initially focused on three aspects of efficient deployment optimization: the fusion of rectified linear unit (ReLU) and convolution operations, the integration of Efficient Non-Maximum Suppression for TensorRT (EfficientNMS_TRT) to accelerate post-processing within the TensorRT model, and the conversion of model formats from number of samples, channels, height, width (NCHW) to number of samples, height, width, and channels (NHWC) in the TensorFlow Lite model. Additionally, experiments were conducted to compare the memory usage, power consumption, and inference latency between the two inference frameworks, as well as to evaluate the real-time video detection performance using DeepStream. The results demonstrate that the fusion of ReLU activation functions with convolution operations reduced the inference latency by 55.5% compared to the use of the Sigmoid linear unit (SiLU) activation alone. In the TensorRT model, the integration of the EfficientNMS_TRT module accelerated post-processing, leading to a reduction in the inference latency of 19.6% and an increase in the frames per second (FPS) of 20.4%. In the TensorFlow Lite model, conversion to the NHWC format decreased the model conversion time by 88.7% and reduced the inference latency by 32.3%. These three efficient deployment optimization methods effectively decreased the inference latency and enhanced the inference efficiency. Moreover, a comparison between the two frameworks revealed that TensorFlow Lite exhibited memory usage reductions of 15% to 20% and power consumption decreases of 15% to 25% compared to TensorRT. Additionally, TensorRT achieved inference latency reductions of 53.2% to 55.2% relative to TensorFlow Lite. Consequently, TensorRT is deemed suitable for tasks requiring strong real-time performance and low latency, whereas TensorFlow Lite is more appropriate for scenarios with constrained memory and power resources. Additionally, the integration of DeepStream and EfficientNMS_TRT was found to optimize memory and power utilization, thereby enhancing the speed of real-time video detection. A detection rate of 28.7 FPS was achieved at a resolution of 1280 × 720. These experiments validate the feasibility and advantages of deploying crop leaf disease detection models on edge AI devices. Full article

(This article belongs to the Section Digital Agriculture)

19 pages, 19562 KiB

Open AccessArticle

Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing

by Qihong Da, Jixuan Yan, Guang Li, Zichen Guo, Haolin Li, Wenning Wang, Jie Li, Weiwei Ma, Xuchun Li and Kejing Cheng

Agriculture 2025, 15(3), 331; https://doi.org/10.3390/agriculture15030331 (registering DOI) - 2 Feb 2025

Viewed by 524

Abstract

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain [...] Read more.

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing. Full article

(This article belongs to the Section Digital Agriculture)

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15 pages, 7414 KiB

Open AccessArticle

Automated Fixed System Specifically Designed for Agrochemical Applications in Protected Crops

by Souraya Benalia, Antonio Mantella, Matteo Sbaglia, Lorenzo M. M. Abenavoli and Bruno Bernardi

Agriculture 2025, 15(3), 330; https://doi.org/10.3390/agriculture15030330 (registering DOI) - 2 Feb 2025

Viewed by 289

Abstract

Protected crops are intensive production systems characterized by high vegetation density, high temperatures, and high moisture, making them favorable environments for the development of pests and diseases. Consequently, these systems often require several interventions with agrochemicals to maintain profitable yields and high produce [...] Read more.

Protected crops are intensive production systems characterized by high vegetation density, high temperatures, and high moisture, making them favorable environments for the development of pests and diseases. Consequently, these systems often require several interventions with agrochemicals to maintain profitable yields and high produce quality. However, the application of plant protection products (PPPs) in such systems is not efficient and poses environmental concerns. This study aims at analysing spray behaviour, particularly in terms of foliar deposition and losses to the ground according to spraying equipment and foliage height, focusing on a specifically designed and developed system for agrochemical application in protected crops, and comparing it with a commonly used spraying system, namely, the cannon sprayer. Such a system consists in a fixed net of tubing and anti-drip nozzles positioned at the top of the greenhouse’s apex, connected to a pneumatic sprayer ‘Special Serre 2000’ outside the greenhouse. Findings revealed a significant effect of the spraying system (Kruskal–Wallis χ² = 12.239, df = 1, and p-value = 0.0004681) on normalized foliar deposition, with higher values obtained using the fixed spraying system. In addition, a simulation of the spatial distribution based on the principle of inverse distance weighting (IDW) was performed for qualitative spray assessment, confirming the heterogeneity of foliar deposition over the greenhouse with both of the used equipment. In addition, losses to the ground were affected by both spraying equipment and captor position. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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24 pages, 15745 KiB

Open AccessArticle

Research on the Sugarcane Stubble Chopping Mechanism of an Ultra-Deep Vertical Rotary Tillage Cutter Based on FEM-SPH Coupling Method

by Wang Yang, Huangsheng Lu, Xiong Xiao, Zhengkai Luo, Weilong Dai and Zhiheng Lu

Agriculture 2025, 15(3), 329; https://doi.org/10.3390/agriculture15030329 (registering DOI) - 2 Feb 2025

Viewed by 285

Abstract

After existing ultra-deep vertical rotary tillers work in sugarcane stubble fields, the stubble chopping performance is poor, and the reason for this is unknown. To solve this, this paper develops a simulation model of ultra-deep vertical rotary tillage (UDVRT) in a sugarcane stubble [...] Read more.

After existing ultra-deep vertical rotary tillers work in sugarcane stubble fields, the stubble chopping performance is poor, and the reason for this is unknown. To solve this, this paper develops a simulation model of ultra-deep vertical rotary tillage (UDVRT) in a sugarcane stubble field using the FEM-SPH coupling method and physical testing. The simulation model is used to investigate the rotary tillage process in the stubble field and the stubble chopping mechanism of the UDVRT cutter, identifying the causes of inadequate stubble chopping effectiveness. The results show that, when comparing the simulation with the field test, the magnitude and variation of the cutter’s torque curves are relatively consistent, the relative error of the topsoil fragmentation rate is 9.5%, the entire cultivated layer of soil fragmentation rate is 11.3%, and the average number of times the stubble stem was cut off is closer; thus, the modeling method of the simulation model is reasonable and accurate. When the cutter cuts the soil and the stubble simultaneously, the soil’s constraint on the stubble is gradually weakened, the velocity difference between the blade and the stem becomes smaller, the tilt of the stems becomes larger, and the number of times the blade can cut the stems reduces, leading to the poor chopping effect of stubble. The cutter cuts the stubble in the order of the blade from top to bottom, with the blade cutting the stem first and then the root, which is an effective measure to increase the stubble fragmentation rate. The findings of this paper can provide a reliable theoretical basis for the optimal design of a UDVRT cutter. Full article

(This article belongs to the Section Agricultural Technology)

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28 pages, 14134 KiB

Open AccessArticle

Optimization of Rotary Blade Wear and Tillage Resistance Based on DEM-MBD Coupling Model

by Zhiqiang Mao, Yang Zhang, Keping Zhang, Jiuxin Wang, Junqian Yang, Xiaobao Zheng, Shuaikang Chen, Zhongqing Yang and Biao Luo

Agriculture 2025, 15(3), 328; https://doi.org/10.3390/agriculture15030328 (registering DOI) - 2 Feb 2025

Viewed by 224

Abstract

To solve the problems of high tillage resistance and the rapid wear of the rotary blade during tillage, this study employed a coupled algorithm of the discrete element method (DEM) and multi-body dynamics (MBD) with Hertz–Mindlin with JKR particle contact theory to establish [...] Read more.

To solve the problems of high tillage resistance and the rapid wear of the rotary blade during tillage, this study employed a coupled algorithm of the discrete element method (DEM) and multi-body dynamics (MBD) with Hertz–Mindlin with JKR particle contact theory to establish a rotary blade–sandy soil model. The interaction between the rotary blade and sandy soil was analyzed. The results indicated that the lateral and horizontal resistances of the rotary blade reached the peak values near the maximum tilling depth, whereas the vertical resistance reached its peak earlier. Blade wear predominantly occurred on the side cutting edge, bending zone edge, and sidelong edge, with the most significant wear observed on the sidelong edge, followed by the bending zone edge and side cutting edge, which showed similar wear patterns. To reduce wear and tillage resistance, Box–Behnken optimization was applied to optimize the blade’s local parameters. The optimal parameters—the height of the tangent edge end face was 51 mm, the bending radius was 28 mm, and the bending angle was 116°—reduced wear by 22.4% and tillage resistance by 12%. A soil disturbance analysis demonstrated that the optimized blade performs better in terms of tillage width compared to the unoptimized blade. The optimized rotary blade achieves the effects of reduced resistance and wear, improves the lifespan of the blade, reducing material loss, and meeting the requirements of sustainable agricultural production. Full article

(This article belongs to the Section Agricultural Technology)

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36 pages, 16450 KiB

Open AccessArticle

Establishment of Whole-Rice-Plant Model and Calibration of Characteristic Parameters Based on Segmented Hollow Stalks

by Ranbing Yang, Peiyu Wang, Yiren Qing, Dongquan Chen, Lu Chen, Wenbin Sun and Kang Xu

Agriculture 2025, 15(3), 327; https://doi.org/10.3390/agriculture15030327 (registering DOI) - 2 Feb 2025

Viewed by 216

Abstract

To address the limitations of the current discrete element model of rice plants in terms of accurately reflecting structural differences and threshing characteristics, this study proposes a whole-rice-plant modeling method based on segmented hollow stalks and establishes a whole-rice-plant model that accurately represents [...] Read more.

To address the limitations of the current discrete element model of rice plants in terms of accurately reflecting structural differences and threshing characteristics, this study proposes a whole-rice-plant modeling method based on segmented hollow stalks and establishes a whole-rice-plant model that accurately represents the bending and threshing characteristics of the actual rice plant. Initially, based on the characteristics of the rice plant, the rice stalk was segmented into three sections of hollow stalks with distinct structures—namely, the primary stalk, the secondary stalk, and the tertiary stalk—ensuring that the model closely resembles actual rice plants. Secondly, the mechanical and contact parameters for each structure of the rice plant were measured and calibrated through mechanical and contact tests. Subsequently, utilizing the Hertz–Mindlin contact model, a multi-dimensional element particle arrangement method was employed to establish a discrete element model of the entire rice plant. The bending characteristics of the stalk and the threshing characteristics of the rice were calibrated using three-point bending tests and impact threshing tests. The results indicated calibration errors in the bending resistance force of 4.46%, 3.95%, and 2.51% for the three-section stalk model, and the calibration error for the rice model’s threshing rate was 1.86%, which can accurately simulate the bending characteristics of the stalk and the threshing characteristics of the rice plant. Finally, the contact characteristics of the model were validated through a stack angle verification test, which revealed that the relative error of the stacking angle did not exceed 7.52%, confirming the accuracy of the contact characteristics of the rice plant model. The findings of this study provide foundational models and a theoretical basis for the simulation of and analytical applications related to rice threshing and cleaning. Full article

(This article belongs to the Section Digital Agriculture)

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30 pages, 13223 KiB

Open AccessArticle

Precision Agriculture: Temporal and Spatial Modeling of Wheat Canopy Spectral Characteristics

by Donghui Zhang, Liang Hou, Liangjie Lv, Hao Qi, Haifang Sun, Xinshi Zhang, Si Li, Jianan Min, Yanwen Liu, Yuanyuan Tang and Yao Liao

Agriculture 2025, 15(3), 326; https://doi.org/10.3390/agriculture15030326 (registering DOI) - 1 Feb 2025

Viewed by 486

Abstract

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and [...] Read more.

This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models. Full article

(This article belongs to the Section Digital Agriculture)

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25 pages, 6694 KiB

Open AccessArticle

Detection of Aging Maize Seed Vigor and Calculation of Germ Growth Speed Using an Improved YOLOv8-Seg Network

by Helong Yu, Xi Ling, Zhenyang Chen, Chunguang Bi and Wanwu Zhang

Agriculture 2025, 15(3), 325; https://doi.org/10.3390/agriculture15030325 (registering DOI) - 1 Feb 2025

Viewed by 394

Abstract

Crop yields are influenced by various factors, including seed quality and environmental conditions. Detecting seed vigor is a critical task for seed researchers, as it plays a vital role in seed quality assessment. Traditionally, this evaluation was performed manually, which is time-consuming and [...] Read more.

Crop yields are influenced by various factors, including seed quality and environmental conditions. Detecting seed vigor is a critical task for seed researchers, as it plays a vital role in seed quality assessment. Traditionally, this evaluation was performed manually, which is time-consuming and labor-intensive. To address this limitation, this study integrates the ConvUpDownModule (a customized convolutional module), C2f-DSConv(C2f module with Integrated Dynamic Snake Convolution), and T-SPPF (the SPPF module integrated with the transformer multi-head attention mechanism) into the VT-YOLOv8-Seg network (the improved YOLOv8-Seg Network), an enhancement of the YOLOv8-Seg architecture. The ConvUpDownModule reduces the computational complexity and model parameters. The C2f-DSConv leverages flexible convolutional kernels to enhance the accuracy of maize germ edge segmentation. The T-SPPF integrates global information to improve multi-scale segmentation performance. The proposed model is designed for detecting and segmenting maize seeds and germs, facilitating seed germination detection and germination speed computation. In detection tasks, the VT-YOLOv8-Seg model achieved 97.3% accuracy, 97.9% recall, and 98.5% mAP50, while in segmentation tasks, it demonstrated 97.2% accuracy, 97.7% recall, and 98.2% mAP50. Comparative experiments with Mask R-CNN, YOLOv5-Seg, and YOLOv7-Seg further validated the superior performance of our model in both detection and segmentation. Additionally, the impact of seed aging on maize seed growth and development was investigated through artificial aging studies. Key metrics such as germination rate and germ growth speed, both closely associated with germination vigor, were analyzed, demonstrating the effectiveness of the proposed approach for seed vigor assessment. Full article

(This article belongs to the Section Digital Agriculture)

23 pages, 6217 KiB

Open AccessArticle

Forewarned Is Forearmed: Documentation on the Invasion Risk of Asclepias speciosa in Greece and Europe

by Nikos Krigas, Catherine Dijon, Ioulietta Samartza, Dimitrios N. Avtzis, Ioannis Anestis, Elias Pipinis and Zigmantas Gudžinskas

Agriculture 2025, 15(3), 324; https://doi.org/10.3390/agriculture15030324 (registering DOI) - 1 Feb 2025

Viewed by 393

Abstract

Biological invasions threaten biodiversity and agroecosystems, and early warning systems can minimise the spread of invasive alien species with limited resources. This study documents the presence of the alien plant Asclepias speciosa Torr., native to North America, that was first discovered in 2022 [...] Read more.

Biological invasions threaten biodiversity and agroecosystems, and early warning systems can minimise the spread of invasive alien species with limited resources. This study documents the presence of the alien plant Asclepias speciosa Torr., native to North America, that was first discovered in 2022 on Mount Vrontou, Central Macedonia, Northern Greece. This is the second European record of this alien species, after Lithuania, confirming its adaptability to contrasting European biogeographical regions. To enable future monitoring, this study provided new data on morphological traits of the species (above-ground parts), climatic tolerance (precipitation and temperature regimes), habitats with co-occurring species, pollinators, current reproductive potential, and seed germination at controlled temperatures (10 °C, 15 °C, and 20 °C). The high probability of misidentification with the highly invasive A. syriaca in European inventories supports the theory that A. speciosa may have been present in Europe long before it was officially reported. The lack of an EU-mandated reassessment of A. syriaca monitoring raises concerns regarding the potential invasion risk of A. speciosa in European natural and semi-natural areas or agricultural lands. Inspection mechanisms, early warning systems, and preventive measures are therefore essential to protect local biodiversity and agriculture from potential A. speciosa invasion, a risk that may be exacerbated by climate change. Full article

(This article belongs to the Special Issue Weed Biology, Ecological Problems and New Strategies for Weed Management and Control)

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19 pages, 3491 KiB

Open AccessArticle

Inversion and Fine Grading of Tidal Flat Soil Salinity Based on the CIWOABP Model

by Jin Zhu, Shuowen Yang, Shuyan Li, Nan Zhou, Yi Shen, Jincheng Xing, Lixin Xu, Zhichao Hong and Yifei Yang

Agriculture 2025, 15(3), 323; https://doi.org/10.3390/agriculture15030323 (registering DOI) - 1 Feb 2025

Viewed by 317

Abstract

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 [...] Read more.

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy (R² = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. The results enable the precise mapping of salinity levels, aiding salt-tolerant crop cultivation and sustainable agricultural management. This method offers a reliable framework for rapid salinity monitoring and precision farming in coastal regions. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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2 pages, 126 KiB

Open AccessEditorial

Impact of Plastics in Agriculture

by Douglas G. Hayes

Agriculture 2025, 15(3), 322; https://doi.org/10.3390/agriculture15030322 (registering DOI) - 1 Feb 2025

Viewed by 138

Abstract

Plastics are an integral part of crop cultivation, a practice often referred to as “plasticulture” [...] Full article

(This article belongs to the Special Issue Impact of Plastics on Agriculture)

27 pages, 590 KiB

Open AccessArticle

Breeding Motives and Attitudes Towards Stakeholders: Implications for the Sustainability of Local Croatian Breeds

by Marija Cerjak, Ivica Faletar, Gabriela Šmit and Ante Ivanković

Agriculture 2025, 15(3), 321; https://doi.org/10.3390/agriculture15030321 (registering DOI) - 31 Jan 2025

Viewed by 282

Abstract

Understanding how breeders of local breeds view different social actors can be of great importance to the process of local breed conservation. The same goes for the motives in farming local breeds. However, there is little research that provides insight into these perspectives. [...] Read more.

Understanding how breeders of local breeds view different social actors can be of great importance to the process of local breed conservation. The same goes for the motives in farming local breeds. However, there is little research that provides insight into these perspectives. The aim of this study was to investigate motives for farming and attitudes of Croatian breeders of two local cattle breeds (Istrian cattle and Buša), two local donkey breeds (Istrian donkey and Littoral Dinaric donkey), and one local horse breed (Croatian Posavina horse) towards consumers, the local population and the regional and national administration. In addition, the influence of motives, attitudes, and the socio-economic characteristics of the breeders on the planned scope of breeding over the next five years was investigated. The study was conducted on a sample of 204 breeders of selected local breeds. The results of the study show that the most important motive for keeping a local breed is the attractiveness (beauty) of the breed followed by its emotional and sentimental value. Around one-third of farmers have a relatively positive attitude towards all stakeholders, with the role of the local population and consumers being viewed most positively. Almost half of the farmers (49%) plan to increase the size of their herd and only 8% plan to reduce it or to stop farming. The planned farming volume over the next five years is significantly influenced by the importance of economic and traditional motives and the change in the number of animals over the last five years. This study represents a valuable contribution to understanding the views of farmers of local breeds towards key societal stakeholders, and the findings can be used in campaigns to promote the keeping of these valuable breeds. Full article

(This article belongs to the Section Farm Animal Production)

21 pages, 1716 KiB

Open AccessArticle

Analysis of the Distribution Pattern of Asparagus in China Under Climate Change Based on a Parameter-Optimized MaxEnt Model

by Qiliang Yang, Chunwei Ji, Na Li, Haixia Lin, Mengchun Li, Haojie Li, Saiji Heng and Jiaping Liang

Agriculture 2025, 15(3), 320; https://doi.org/10.3390/agriculture15030320 (registering DOI) - 31 Jan 2025

Viewed by 316

Abstract

Asparagus (Asparagus officinalis L.) has high health and nutritional values, but the lack of scientific and rational cultivation planning has resulted in a decline in asparagus quality and yield. Important soil, climatic, anthropogenic, and topographic environmental factors influencing the distribution of asparagus [...] Read more.

Asparagus (Asparagus officinalis L.) has high health and nutritional values, but the lack of scientific and rational cultivation planning has resulted in a decline in asparagus quality and yield. Important soil, climatic, anthropogenic, and topographic environmental factors influencing the distribution of asparagus cultivation were chosen for this study. The Kuenm package in the R language (v4.2.1) was employed to optimize the maximum entropy model (MaxEnt). Pearson’s correlation analysis, optimized MaxEnt, and geographic information spatial technology were then utilized to identify the main environmental factors that influence suitable habitats for asparagus in China. Potential distribution patterns, migration, and changes in trends concerning the suitability of asparagus in China under various historical and future climate scenarios were modeled and projected. Human activities and climate factors were found to be the primary environmental factors that influence the suitability distribution of asparagus cultivation in China, followed by soil and topographic factors. Historical suitable habitats covered 345.6 × 10⁵ km², accounting for 36% of China. These habitats are projected to expand considerably under future climatic conditions. This research offers a basis for the rational planning and sustainable development of asparagus cultivation. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

21 pages, 1740 KiB

Open AccessArticle

The Capacity of a Household Farming System with Women’s Decision and Action-Making Power: Rural Marginal Areas in Morocco

by Veronique Alary, Bruno Romagny, Dina Najjar, Mohammed Aderghal and Jean-Yves Moisseron

Agriculture 2025, 15(3), 319; https://doi.org/10.3390/agriculture15030319 (registering DOI) - 31 Jan 2025

Viewed by 396

Abstract

Nowadays, women’s contribution to society through their social and human involvement at the household level in terms of education, care, and nutrition, as well as their added value to economic functioning, is increasingly recognized. However, most of the related research highlights the relative [...] Read more.

Nowadays, women’s contribution to society through their social and human involvement at the household level in terms of education, care, and nutrition, as well as their added value to economic functioning, is increasingly recognized. However, most of the related research highlights the relative contributions of women and men. This paper proposes to analyze the link between women’s contribution to social, economic, and financial activities and the rural livelihood of the whole household farm. Based on a household survey that included a respondent section for women from over 285 families in the least rurally developed regions of Morocco, descriptive statistics and systemic analysis successively based on multiple factorial and clustering analyses were used to analyze the links between household adaptative capacity and women’s material and immaterial contributions. The results revealed that women play a crucial role in intergenerational knowledge transfer, which constitutes a critical factor in household capacities and reproduction, especially in the least endowed households. However, the women’s farm or off-farm activities did not guarantee their autonomy. So, the contribution of women to household farm livelihood through their know-how opens alternative pathways to reconsider their contribution to the overall goal of livelihood improvement. Full article

(This article belongs to the Topic Novel Studies in Agricultural Economics and Sustainable Farm Management)

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23 pages, 1774 KiB

Open AccessReview

Improvements in Tolerance to Heat Stress in Rice via Molecular Mechanisms and Rice Varieties

by He Liu, Yiting Wei, Saisai Xia, Wei Xie, Deyong Ren and Yuchun Rao

Agriculture 2025, 15(3), 318; https://doi.org/10.3390/agriculture15030318 (registering DOI) - 31 Jan 2025

Viewed by 414

Abstract

Global warming affects crop growth and development, threatening food security. As one of the essential food crops, rice is severely affected by high temperature stress, which compromises both its yield and quality. Therefore, gaining a deep understanding of the molecular mechanisms by which [...] Read more.

Global warming affects crop growth and development, threatening food security. As one of the essential food crops, rice is severely affected by high temperature stress, which compromises both its yield and quality. Therefore, gaining a deep understanding of the molecular mechanisms by which rice responds to heat stress and breeding rice varieties that are tolerant to such stress is crucial for maintaining food security. This review summarizes the impacts of heat stress on yield and quality-related traits at different growth and development stages of rice, the molecular mechanisms of rice perception and response to heat stress, and the improvement in and breeding of heat-tolerant rice varieties using existing superior alleles/QTLs. We also discuss the opportunities and challenges in creating highly heat-tolerant rice germplasm, providing new ideas and insights for the future breeding of heat-tolerant rice varieties. Full article

(This article belongs to the Special Issue The Role of Molecular Breeding in Improving Agronomic Traits of Rice)

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20 pages, 2220 KiB

Open AccessArticle

Early-Maturity Wheat as a Highly Valuable Feed Raw Material with Prebiotic Activity

by Besarion Meskhi, Viktor Pakhomov, Dmitry Rudoy, Tatyana Maltseva, Anastasiya Olshevskaya and Maria Mazanko

Agriculture 2025, 15(3), 317; https://doi.org/10.3390/agriculture15030317 - 31 Jan 2025

Viewed by 251

Abstract

This work is devoted to the study of the dynamics of changes in the composition of the heap of cereal crops during maturation and identifying the optimal stage at which the grain heap has a high feed value. We studied the grain heap [...] Read more.

This work is devoted to the study of the dynamics of changes in the composition of the heap of cereal crops during maturation and identifying the optimal stage at which the grain heap has a high feed value. We studied the grain heap of winter wheat of the Admiral variety, perennial winter wheat (Trititrigia) of the Pamyati Lyubimovoy variety, and gray wheatgrass of the Sova variety for the amino acid composition, and protein, moisture, iron, phosphorus, selenium, zinc, starch, and vitamin E contents. Cereal crops harvested at the hard wax ripeness stage demonstrated a 3–4% higher protein content, along with increased levels of certain amino acids and minerals such as iron and selenium. The grain heap of hard waxy ripeness wheat was studied for prebiotic properties. The study found that it increases the number of lactic acid bacteria in the intestinal microbiota and therefore is a promising prebiotic for agriculture. Based on this study, the recommended concentration of grain heap of waxy ripeness wheat as a feed additive is 1%. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

19 pages, 1916 KiB

Open AccessArticle

Effects of Different Biomass Types on Pellet Qualities and Processing Energy Consumption

by Yantao Yang, Lei Song, Yuanna Li, Yilin Shen, Mei Yang, Yunbo Wang, Hesheng Zheng, Wei Qi and Tingzhou Lei

Agriculture 2025, 15(3), 316; https://doi.org/10.3390/agriculture15030316 - 31 Jan 2025

Viewed by 254

Abstract

This work conducts a single-factor experiment to study the effects of biomass types on the relax density, volume expansion, durability, hydrophobicity, and processing energy consumption. We analyze the differences in the quality of the pellets, and optimize the compaction conditions suitable for different [...] Read more.

This work conducts a single-factor experiment to study the effects of biomass types on the relax density, volume expansion, durability, hydrophobicity, and processing energy consumption. We analyze the differences in the quality of the pellets, and optimize the compaction conditions suitable for different biomass types including straw, hardwood, shell, and herbaceous plant. The results indicated that with a compressing force of 60~1500 N, compressing time of 10 s, powder size of less than 0.5 mm, and moisture content of 10%, the relax densities of corn straw, rice straw, selenium-rich rice straw, weigela japonica branches, and camphor leaves range from 360 to 820 kg/m³, with a processing energy consumption of 17,360 to 28,740 J/kg; meanwhile, the relax densities of argy wormwood, forage grass, green grass, and peanut shells range from 340 to 840 kg/m³, with a processing energy consumption of 33,510 to 73,700 J/kg. Therefore, the compaction pretreatment effectively regulates the density of biomass pellets and reduces the processing energy consumption. This study analyzed the differences in the quality of pellets caused by the inherent characteristics of biomass, providing strong support for the directional depolymerization and enhanced pretreatment technology for the scaled production of biomass alcohol fuels. Full article

(This article belongs to the Section Agricultural Technology)

28 pages, 5603 KiB

Open AccessReview

Application of Discrete Element Method to Potato Harvesting Machinery: A Review

by Yuanman Yue, Qian Zhang, Boyang Dong and Jin Li

Agriculture 2025, 15(3), 315; https://doi.org/10.3390/agriculture15030315 - 31 Jan 2025

Viewed by 311

Abstract

The Discrete Element Method (DEM) is an innovative numerical computational approach. This method is employed to study and resolve the motion patterns of particles within discrete systems, contact mechanics properties, mechanisms of separation processes, and the relationships between contact forces and energy. Agricultural [...] Read more.

The Discrete Element Method (DEM) is an innovative numerical computational approach. This method is employed to study and resolve the motion patterns of particles within discrete systems, contact mechanics properties, mechanisms of separation processes, and the relationships between contact forces and energy. Agricultural machinery involves the interactions between machinery and soil, crops, and other systems. Designing agricultural machinery can be equivalent to solving problems in discrete systems. The DEM has been widely applied in research on agricultural machinery design and mechanized harvesting of crops. It has also provided an important theoretical research approach for the design and selection of operating parameters, as well as the structural optimization of potato harvesting machinery. This review first analyzes and summarizes the current global potato industry situation, planting scale, and yield. Subsequently, it analyzes the challenges facing the development of the potato industry. The results show that breeding is the key to improving potato varieties, harvesting is the main stage where potato damage occurs, and reprocessing is the main process associated with potato waste. Second, an overview of the basic principles of DEM, contact models, and mechanical parameters is provided, along with an introduction to the simulation process using the EDEM software. Third, the application of the DEM to mechanized digging, transportation, collection, and separation of potatoes from the soil is reviewed. The accuracy of constructing potato and soil particle models and the rationality of the contact model selection are found to be the main factors affecting the results of discrete element simulations. Finally, the challenges of using the DEM for research on potato harvesting machinery are presented, and a summary and outlook for the future development of the DEM are provided. Full article

(This article belongs to the Special Issue Feature Papers in Agriculture Technology—Using Computer Simulation for Agricultural Machinery Design and Development)

24 pages, 1278 KiB

Open AccessArticle

The Impact of Lifestyle on Individual’s Perception of Urban Agriculture

by Simona Gavrilaș, Oana Brînzan, Radu Lucian Blaga, Maria Sinaci, Eugenia Tigan and Nicoleta Mateoc-Sîrb

Agriculture 2025, 15(3), 314; https://doi.org/10.3390/agriculture15030314 - 31 Jan 2025

Viewed by 295

Abstract

Urban-farming activities can provide durability to an area, ensuring, among other benefits, environmental awareness, access to fresh food, individual health, and, potentially, an increased family income. The purpose of this study was to investigate the correlations between the following benefits of urban agriculture [...] Read more.

Urban-farming activities can provide durability to an area, ensuring, among other benefits, environmental awareness, access to fresh food, individual health, and, potentially, an increased family income. The purpose of this study was to investigate the correlations between the following benefits of urban agriculture perceived by the inhabitants of western Romanian towns: socialisation and recreation and the avoidance of food waste with their levels of education and financial situations. The data were collected through an online questionnaire, completed by 648 respondents, and processed in SPSS-IBM using an analysis of variance (ANOVA) and a Tukey interval test. The study results demonstrated that a high level of education leads to increased belief in the contributions that urban farming makes to improving the socialisation and recreation of city residents. The income level also significantly shapes opinions regarding the contribution of urban agriculture to the increase in recycling. This study reflected critical lifestyle perspectives that impact people’s perception of the benefits of urban agriculture. The findings are expected to provide new insights for regulators and decision-makers, enabling them to develop tailored methods, strategies, policies, and legal measures to achieve sustainable growth in the urban community. Full article

(This article belongs to the Special Issue Current Prospects of Social-Ecologically More Sustainable Agriculture and Urban Agriculture)

17 pages, 6161 KiB

Open AccessArticle

Efficient Triple Attention and AttentionMix: A Novel Network for Fine-Grained Crop Disease Classification

by Yanqi Zhang, Ning Zhang, Jingbo Zhu, Tan Sun, Xiujuan Chai and Wei Dong

Agriculture 2025, 15(3), 313; https://doi.org/10.3390/agriculture15030313 - 31 Jan 2025

Viewed by 272

Abstract

In the face of global climate change, crop pests and diseases have emerged on a large scale, with diverse species lasting for long periods and exerting wide-ranging impacts. Identifying crop pests and diseases efficiently and accurately is crucial in enhancing crop yields. Nonetheless, [...] Read more.

In the face of global climate change, crop pests and diseases have emerged on a large scale, with diverse species lasting for long periods and exerting wide-ranging impacts. Identifying crop pests and diseases efficiently and accurately is crucial in enhancing crop yields. Nonetheless, the complexity and variety of scenarios render this a challenging task. In this paper, we propose a fine-grained crop disease classification network integrating the efficient triple attention (ETA) module and the AttentionMix data enhancement strategy. The ETA module is capable of capturing channel attention and spatial attention information more effectively, which contributes to enhancing the representational capacity of deep CNNs. Additionally, AttentionMix can effectively address the label misassignment issue in CutMix, a commonly used method for obtaining high-quality data samples. The ETA module and AttentionMix can work together on deep CNNs for greater performance gains. We conducted experiments on our self-constructed crop disease dataset and on the widely used IP102 plant pest and disease classification dataset. The results showed that the network, which combined the ETA module and AttentionMix, could reach an accuracy as high as 98.2% on our crop disease dataset. When it came to the IP102 dataset, this network achieved an accuracy of 78.7% and a recall of 70.2%. In comparison with advanced attention models such as ECANet and Triplet Attention, our proposed model exhibited an average performance improvement of 5.3% and 4.4%, respectively. All of this implies that the proposed method is both practical and applicable for classifying diseases in the majority of crop types. Based on classification results from the proposed network, an install-free WeChat mini program that enables real-time automated crop disease recognition by taking photos with a smartphone camera was developed. This study can provide an accurate and timely diagnosis of crop pests and diseases, thereby providing a solution reference for smart agriculture. Full article

(This article belongs to the Section Digital Agriculture)

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12 pages, 819 KiB

Open AccessArticle

Profitability Analysis of the Robusta Coffee Value Chain in the Tshopo Province, Democratic Republic of Congo

by Louis Pasteur Bamenga Bopoko, Theodore Trefon, Jean-Pierre Mate and Baudouin Michel

Agriculture 2025, 15(3), 312; https://doi.org/10.3390/agriculture15030312 - 31 Jan 2025

Viewed by 308

Abstract

This article addresses the financial viability of agents in the robusta coffee sector. The objective is to calculate and analyze the profitability performance of the coffee sector in Tshopo in order to inform the subsequent development of business projects in the robusta coffee [...] Read more.

This article addresses the financial viability of agents in the robusta coffee sector. The objective is to calculate and analyze the profitability performance of the coffee sector in Tshopo in order to inform the subsequent development of business projects in the robusta coffee sector. Moreover, the analysis will assist decision-makers and investors in determining the optimal allocation of funds to the most profitable links in the robusta coffee sector in Tshopo. A cost-benefit analysis was conducted, employing a discounting methodology to evaluate the cash flows of agents engaged in the robusta coffee sector. This entailed the calculation of the net present value, internal rate of return, and payback period. The results demonstrate that the coffee sector is performing well, with agents’ cash flow sufficient to repay the initial investment. It can thus be concluded that, in consideration of the favorable profitability criteria, there is minimal risk in investing in the robusta coffee sector in Tshopo. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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20 pages, 4848 KiB

Open AccessArticle

A Model Combining Sensitive Vegetation Indices and Fractional-Order Differential Characteristic Bands for SPAD Value Estimation in Cd-Contaminated Rice Leaves

by Rongcai Tian, Bin Zou, Shenxin Li, Li Dai, Bo Zhang, Yulong Wang, Hao Tu, Jie Zhang and Lunwen Zou

Agriculture 2025, 15(3), 311; https://doi.org/10.3390/agriculture15030311 - 31 Jan 2025

Viewed by 279

Abstract

Rapid and nondestructive estimation of leaf SPAD values is crucial for monitoring the effects of cadmium (Cd) stress in rice. To address the issue of low estimation accuracy in leaf SPAD value models due to the loss of spectral information in existing studies, [...] Read more.

Rapid and nondestructive estimation of leaf SPAD values is crucial for monitoring the effects of cadmium (Cd) stress in rice. To address the issue of low estimation accuracy in leaf SPAD value models due to the loss of spectral information in existing studies, a new estimation model, which combines sensitive vegetation indices (VIss) and fractional order differential characteristic bands (FODcb), is proposed in this study. To validate the effectiveness of this new model, three scenarios, with no Cd contamination, 1.0 mg/kg Cd contamination, and 1.4 mg/kg Cd contamination, were set up. Leaf spectral reflectance and SPAD values were measured during the critical growth period of rice. Subsequently, 16 vegetation indices were constructed, and fractional order difference (FOD) transformation was applied to process the spectral data. The variable importance in projection (VIP) algorithm was employed to extract VIss and FODcb. Finally, the random forest (RF) algorithm was used to construct three models, VIss + FODcb-RF, FODcb-RF, and VIss-RF. The estimated leaf SPAD values for the three models showed that: (1) there was a significant difference between the leaf SPAD values with no Cd contamination and those treated with 1.4 mg/kg Cd contamination on the 31st and 87th days after transplanting; (2) the 400–773 nm spectral range was sensitive for estimating leaf SPAD values, with the Cd-contaminated scenario exhibiting higher reflectance in the visible wavelength range than the Cd-uncontaminated scenario; (3) compared with the individual FODcb-RF and Viss-RF models, the combined model (VIss + FODcb-RF) improved the estimation accuracy of the leaf SPAD values. Particularly, the Viss + FOD_1.2cb-RF model provided the best performance, with R²v, RMSEv, and RPDv values of 0.821, 2.621, and 2.296, respectively. In conclusion, this study demonstrates the effectiveness of combining VIss and FODcb for accurately estimating Cd-contaminated rice leaf SPAD values. This finding will provide a methodological reference for remote sensing monitoring of Cd contamination in rice. Full article

(This article belongs to the Section Digital Agriculture)

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46 pages, 3544 KiB

Open AccessArticle

Effects of Maternal Probiotics and Piglet Dietary Tryptophan Level on Gastric Function Pre- and Post-Weaning

by Dillon. P. Kiernan, John V. O’Doherty, Marion T. Ryan and Torres Sweeney

Agriculture 2025, 15(3), 310; https://doi.org/10.3390/agriculture15030310 - 30 Jan 2025

Viewed by 324

Abstract

Knowledge of how novel antigens or dietary stimuli affect stomach development and function in pigs remains limited. This study aimed to investigate stomach characteristics, parietal cell numbers, and the expression of genes essential to the functioning of the fundic and pyloric gland regions [...] Read more.

Knowledge of how novel antigens or dietary stimuli affect stomach development and function in pigs remains limited. This study aimed to investigate stomach characteristics, parietal cell numbers, and the expression of genes essential to the functioning of the fundic and pyloric gland regions at weaning compared to seven days post-weaning and to examine whether maternal probiotic supplementation or piglet dietary tryptophan (Trp) levels influence these stomach parameters. This study has a 2 × 3 factorial design, with 48 sows assigned to one of two diets: basal or basal supplemented with Bacillus subtilis and Bacillus amyloliquefaciens. Their litters received creep diets containing 0.22, 0.27, or 0.33% standardized ileal digestible (SID) Trp. In total, 96 pigs were sacrificed for gastric sampling, 48 on the day of weaning and 48 on day 7 post-weaning. At 7 days post-weaning, pigs had an increased number of parietal cells and expression of parietal cell activity and digestive enzyme (PGA5 and CHIA) genes in the fundic gland region (p < 0.05), although the expression of signaling molecules involved in the regulation of acid secretion was unchanged in the fundic gland region (p > 0.05) and reduced in the pyloric gland region (p < 0.05), compared to the day of weaning. Overall, maternal probiotic supplementation had a significant impact on gene expression in the fundic gland region of the offspring, elevating several genes related to parietal cell activity (CLIC6, HRH2, KCNE1, KCNQ1, CHRM3, CCKBR, and SSTR2) (p < 0.05). Additionally, there were time × maternal interactions, where certain acid secretion pathway (ATP4A and HDC), chitinase enzyme (CHIA), and ghrelin (GHRL) genes were increased in offspring from probiotic sows compared to control sows at weaning (p < 0.05), but not at 7 days post-weaning (p > 0.05). Maternal probiotic supplementation did not influence growth performance pre-weaning or during the 7-day post-weaning period. There was a limited effect of creep Trp level or maternal × creep interactions on performance, gene expression, or parietal cell counts. Low pre-weaning creep intake may have confounded this analysis. In conclusion, maternal probiotic supplementation accelerated the maturation of the offspring’s stomach, particularly in terms of the expression of genes linked to acid secretion from parietal cells. Full article

(This article belongs to the Section Farm Animal Production)

18 pages, 5755 KiB

Open AccessArticle

Unmanned-Aerial-Vehicle-Based Multispectral Monitoring of Nitrogen Content in Canopy Leaves of Processed Tomatoes

by Hao Zhang, Li Zhang, Hongqi Wu, Dejun Wang, Xin Ma, Yuqing Shao, Mingjun Jiang and Xinyu Chen

Agriculture 2025, 15(3), 309; https://doi.org/10.3390/agriculture15030309 - 30 Jan 2025

Viewed by 363

Abstract

Nitrogen serves as a critical nutrient influencing the yield and quality of processed tomatoes; however, traditional methods for assessing its levels are both labor-intensive and costly. This study aimed to explore an efficient monitoring approach by analyzing the relationship between leaf nitrogen content [...] Read more.

Nitrogen serves as a critical nutrient influencing the yield and quality of processed tomatoes; however, traditional methods for assessing its levels are both labor-intensive and costly. This study aimed to explore an efficient monitoring approach by analyzing the relationship between leaf nitrogen content (LNC) and canopy spectral reflectance characteristics throughout the growth stages of processed tomatoes at the Laolong River Tomato Base in Changji City, Xinjiang. The experimental design incorporated nine treatments, each with three replicates. LNC data were obtained using a dedicated leaf nitrogen content analyzer, while drones were utilized to capture multispectral images for the extraction of vegetation indices. Through Pearson correlation analysis, the optimal spectral variables were identified, and the relationships between LNC and spectral variables were established using models based on backpropagation (BP), multiple linear regression (MLR), and random forests (RFs). The findings revealed that the manually measured LNC data exhibited two peak values, which occurred during the onset of flowering and fruit setting stages, displaying a bimodal pattern. Among the twelve selected vegetation indices, ten demonstrated spectral sensitivity, passing the highly significant 0.01 threshold, with the Normalized Difference Chlorophyll Index (NDCI) showing the highest correlation during the full bloom stage. The combination of the NDCI and RF model achieved a prediction accuracy exceeding 0.8 during the full bloom stage; similarly, models incorporating multiple vegetation indices, such as RF, MLR, and BP, also reached prediction accuracies exceeding 0.8. Consequently, during the seedling establishment and initial flowering stages (vegetation coverage of <60%), the RF model with multiple vegetation indices was suitable for monitoring LNC; during the full bloom stage (vegetation coverage of 60–80%), both the RF model with the NDCI and the MLR model with multiple indices proved effective; and during the fruit setting and maturation stages (vegetation coverage of >80%), the BP model was more appropriate. This research provides a scientific basis for the cultivation management of processed tomatoes and the optimization of nitrogen fertilization within precision agriculture. It advances the application of precision agriculture technologies, contributing to improved agricultural efficiency and resource utilization. Full article

(This article belongs to the Section Digital Agriculture)

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18 pages, 3969 KiB

Open AccessArticle

An Automatic Irrigation System Based on Hourly Cumulative Evapotranspiration for Reducing Agricultural Water Usage

by Yongjae Lee, Seung-un Ha, Xin Wang, Seungyong Hahm, Kwangya Lee and Jongseok Park

Agriculture 2025, 15(3), 308; https://doi.org/10.3390/agriculture15030308 - 30 Jan 2025

Viewed by 581

Abstract

This study investigates the development and application of an automatic irrigation system based on hourly cumulative evapotranspiration (ET) to optimize cabbage growth while reducing agricultural water usage. Traditional irrigation methods often result in inefficient water use due to reliance on human judgment or [...] Read more.

This study investigates the development and application of an automatic irrigation system based on hourly cumulative evapotranspiration (ET) to optimize cabbage growth while reducing agricultural water usage. Traditional irrigation methods often result in inefficient water use due to reliance on human judgment or fixed schedules. To address this issue, the proposed system utilizes environmental data collected from a field sensor (FS), the Korea meteorological administration (KMA), and a virtual sensor based on a machine learning model (ML) to calculate the hourly ET and automate irrigation. The ET was calculated using the FAO 56 Penman–Monteith (P-M) ET_o. Experiments were conducted to compare the effectiveness of different irrigation levels, ranging from 40, 60, 80, and 100% crop evapotranspiration (ET_c), on plant growth and the irrigation water productivity (WP_I). During the 46-day experimental period, cabbage growth and WP_I were higher in the FS and KMA 60% ET_c levels compared to other irrigation levels, with water usage of 8.90 and 9.07 L/plant, respectively. In the ML treatment, cabbage growth and WP_I were higher in the 80% ET_c level compared to other irrigation levels, with water usage of 8.93 L/plant. These results demonstrated that irrigation amounts of approximately 9 L/plant provided the optimal balance between plant growth and water conservation over 46 days. This system presents a promising solution for improving crop yield while conserving water resources in agricultural environments. Full article

(This article belongs to the Section Agricultural Water Management)

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16 pages, 4641 KiB

Open AccessArticle

A Model for Diagnosing Mild Nutrient Stress in Facility-Grown Tomatoes Throughout the Entire Growth Cycle

by Yunpeng Yuan, Guoxiang Sun, Guangyu Chen, Qihua Zhang and Lingwei Liang

Agriculture 2025, 15(3), 307; https://doi.org/10.3390/agriculture15030307 - 30 Jan 2025

Viewed by 305

Abstract

The effective diagnosis of mild nutrient stress across the complete growth cycle of facility-grown tomatoes is challenging. This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose [...] Read more.

The effective diagnosis of mild nutrient stress across the complete growth cycle of facility-grown tomatoes is challenging. This study proposes a deep learning framework based on CNN + LSTM, using canopy near-infrared spectroscopy from different growth stages of tomatoes as input, to diagnose mild stress of nitrogen (N), potassium (K), and calcium (Ca) throughout the entire growth cycle of facility-grown tomatoes. The study compares the diagnostic performance of Random Forest (RF), Support Vector Machine (SVM), Partial Least Squares (PLS), Convolutional Neural Networks (CNNs), and CNN + Long Short-Term Memory (LSTM) models for detecting mild nutrient stress in facility-grown tomatoes. Firstly, the preprocessing method of spectral characteristic bands combined with Savitzky‒Golay (SG) + Standard Normal Variate (SNV) was determined. Subsequently, all sample data were divided into six groups: N-deficient, K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess. The aforementioned models were then used for classification prediction. The results show that RF and CNN + LSTM models demonstrated good predictive performance. Specifically, RF achieved accuracy rates of 70.14%, 90.81%, 88.59%, and 85.37% in the classification tasks of Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. The CNN + LSTM model achieved accuracy rates of 93.33%, 63.33%, 99.2%, 83.33%, and 98.52% in the classification tasks of K-deficient, Ca-deficient, N-excess, K-excess, and Ca-excess, respectively. Finally, in the Leave-One-Group-Out Validation (LOGOV) for validating the model’s generalisation performance, RF performed better in the N-deficient, K-deficient, and Ca-deficient tasks, achieving diagnostic accuracy rates of 80.19%, 81.43%, and 77.02%, respectively. The CNN + LSTM model showed a diagnostic accuracy rate of 66.72% in the N-excess classification task. The study concludes that, given complete training data, the CNN + LSTM model can effectively diagnose mild nutrient stress (N, K, and Ca) in facility-grown tomatoes in most scenarios. Full article

(This article belongs to the Special Issue The Application of Near-Infrared Spectroscopy in Agriculture—2nd Edition)

17 pages, 1624 KiB

Open AccessArticle

Fuzzy Extended State Observer-Based Sliding Mode Control for an Agricultural Unmanned Helicopter

by Suiyuan Shen, Jiyu Li, Yu Chen and Jia Lv

Agriculture 2025, 15(3), 306; https://doi.org/10.3390/agriculture15030306 - 30 Jan 2025

Viewed by 256

Abstract

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based [...] Read more.

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based sliding mode control (FESO-SMC) methodology aimed at enhancing the aircraft’s resilience against such disturbances. Initially, this study adopts a state expansion strategy to integrate both wind and structural disturbances into a comprehensive disturbance model applicable to the agricultural unmanned helicopter. Following this, a sliding mode control law is formulated with consideration for unknown total disturbances, employing specific sliding mode functions alongside exponential reaching laws. An extended state observer is simultaneously implemented within the sliding mode control framework to estimate and mitigate these disturbances, thereby augmenting the disturbance rejection capabilities of the flight control system. Additionally, the integration of fuzzy logic facilitates adaptive parameter adjustment for the extended state observer, leading to more accurate disturbance estimation. Consequently, a trajectory tracking control system tailored specifically for the agricultural unmanned helicopter has been developed, and its performance was evaluated through simulation experiments. The results indicate that, under certain disturbances, the attitude control error of the FESO-SMC controller is reduced to one-fifth that of traditional sliding mode controllers, while position control accuracy is enhanced more than twofold, thus demonstrating that the proposed FESO-SMC controller not only exhibits superior anti-disturbance capability and robustness but also achieves higher tracking accuracy compared to conventional sliding mode controller. Full article

(This article belongs to the Special Issue Application of UAVs in Precision Agriculture—2nd Edition)

23 pages, 2415 KiB

Open AccessArticle

Framework for Apple Phenotype Feature Extraction Using Instance Segmentation and Edge Attention Mechanism

by Zichong Wang, Weiyuan Cui, Chenjia Huang, Yuhao Zhou, Zihan Zhao, Yuchen Yue, Xinrui Dong and Chunli Lv

Agriculture 2025, 15(3), 305; https://doi.org/10.3390/agriculture15030305 - 30 Jan 2025

Viewed by 374

Abstract

A method for apple phenotypic feature extraction and growth anomaly identification based on deep learning and natural language processing technologies is proposed in this paper, aiming to enhance the accuracy of apple quality detection and anomaly prediction in agricultural production. This method integrates [...] Read more.

A method for apple phenotypic feature extraction and growth anomaly identification based on deep learning and natural language processing technologies is proposed in this paper, aiming to enhance the accuracy of apple quality detection and anomaly prediction in agricultural production. This method integrates instance segmentation, edge perception mechanisms, attention mechanisms, and multimodal data fusion to accurately extract an apple’s phenotypic features, such as its shape, color, and surface condition, while identifying potential anomalies which may arise during the growth process. Specifically, the edge transformer segmentation network is employed to combine deep convolutional networks (CNNs) with the Transformer architecture, enhancing feature extraction and modeling long-range dependencies across different regions of an image. The edge perception mechanism improves segmentation accuracy by focusing on the boundary regions of the apple, particularly in the case of complex shapes or surface damage. Additionally, the natural language processing (NLP) module analyzes agricultural domain knowledge, such as planting records and meteorological data, providing insights into potential causes of growth anomalies and enabling more accurate predictions. The experimental results demonstrate that the proposed method significantly outperformed traditional models across multiple metrics. Specifically, in the apple phenotypic feature extraction task, the model achieved exceptional performance, with accuracy of 0.95, recall of 0.91, precision of 0.93, and mean intersection over union (mIoU) of 0.92. Furthermore, in the growth anomaly identification task, the model also performed excellently, with a precision of 0.93, recall of 0.90, accuracy of 0.91, and mIoU of 0.89, further validating its efficiency and robustness in handling complex growth anomaly scenarios. The method’s integration of image data with agricultural knowledge provides a comprehensive approach to both apple quality detection and growth anomaly prediction, offering reliable decision support for agricultural production. The proposed method, by integrating image data with agricultural domain knowledge, provides precise decision support for agricultural production, not only improving the efficiency and accuracy of apple quality detection but also offering reliable technical assurance for agricultural economic analysis. Full article

(This article belongs to the Special Issue Application of Vision Technology and Artificial Intelligence in Smart Farming—2nd Edition)

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