Agriculture

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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)

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