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17 pages, 11814 KiB

Open AccessArticle

Recognition of Maize Tassels Based on Improved YOLOv8 and Unmanned Aerial Vehicles RGB Images

by Jiahao Wei, Ruirui Wang, Shi Wei, Xiaoyan Wang and Shicheng Xu

Drones 2024, 8(11), 691; https://doi.org/10.3390/drones8110691 - 19 Nov 2024

Viewed by 415

Abstract

The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. However, the branches overlap each other during the growth of maize seedlings and cannot be used as [...] Read more.

The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. However, the branches overlap each other during the growth of maize seedlings and cannot be used as an identifying feature. However, during the tasseling stage, its apical ear blooms and has distinctive features that can be used as an identifying feature. However, the sizes of the maize tassels are small, the background is complex, and the existing network has obvious recognition errors. Therefore, in this paper, unmanned aerial vehicle (UAV) RGB images and an improved YOLOv8 target detection network are used to enhance the recognition accuracy of maize tassels. In the new network, a microscale target detection head is added to increase the ability to perceive small-sized maize tassels; In addition, Spatial Pyramid Pooling—Fast (SPPF) is replaced by the Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN) in the backbone network part to connect different levels of detailed features and semantic information. Moreover, a dual-attention module synthesized by GAM-CBAM is added to the neck part to reduce the loss of features of maize tassels, thus improving the network’s detection ability. We also labeled the new maize tassels dataset in VOC format as the training and validation of the network model. In the final model testing results, the new network model’s precision reached 93.6% and recall reached 92.5%, which was an improvement of 2.8–12.6 percentage points and 3.6–15.2 percentage points compared to the mAP₅₀ and F1-score values of other models. From the experimental results, it is shown that the improved YOLOv8 network, with high performance and robustness in small-sized maize tassel recognition, can accurately recognize maize tassels in UAV images, which provides technical support for automated counting, accurate cultivation, and large-scale intelligent cultivation of maize seedlings. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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26 pages, 1748 KiB

Open AccessArticle

Sparse Online Gaussian Process Adaptive Control of Unmanned Aerial Vehicle with Slung Payload

by Muhammed Rasit Kartal, Dmitry I. Ignatyev and Argyrios Zolotas

Drones 2024, 8(11), 687; https://doi.org/10.3390/drones8110687 - 19 Nov 2024

Viewed by 310

Abstract

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms [...] Read more.

In the past decade, Unmanned Aerial Vehicles (UAVs) have garnered significant attention across diverse applications, including surveillance, cargo shipping, and agricultural spraying. Despite their widespread deployment, concerns about maintaining stability and safety, particularly when carrying payloads, persist. The development of such UAV platforms necessitates the implementation of robust control mechanisms to ensure stable and precise maneuvering capabilities. Numerous UAV operations require the integration of payloads, which introduces substantial stability challenges. Notably, operations involving unstable payloads such as liquid or slung payloads pose a considerable challenge in this regard, falling into the category of mismatched uncertain systems. This study focuses on establishing stability for slung payload-carrying systems. Our approach involves a combination of various algorithms: the incremental backstepping control algorithm (IBKS), integrator backstepping (IBS), Proportional–Integral–Derivative (PID), and the Sparse Online Gaussian Process (SOGP), a machine learning technique that identifies and mitigates disturbances. With a comparison of linear and nonlinear methodologies through different scenarios, an investigation for an effective solution has been performed. Implementation of the machine learning component, employing SOGP, effectively detects and counteracts disturbances. Insights are discussed within the remit of rejecting liquid sloshing disturbance. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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13 pages, 20428 KiB

Open AccessArticle

Impact of Variable Device Structural Changes on Particle Deposition Distribution in Multi-Rotor UAV

by Jingang Han, Tongsheng Zhang, Lilian Liu, Guobin Wang, Cancan Song and Yubin Lan

Drones 2024, 8(10), 583; https://doi.org/10.3390/drones8100583 - 16 Oct 2024

Viewed by 570

Abstract

The aim of this study was to investigate the effect of structural changes in variable fertilizer application devices on the distribution of particle deposition in UAVs. With the rapid development of drone technology, particularly in particulate spreading, drones have demonstrated significant potential due [...] Read more.

The aim of this study was to investigate the effect of structural changes in variable fertilizer application devices on the distribution of particle deposition in UAVs. With the rapid development of drone technology, particularly in particulate spreading, drones have demonstrated significant potential due to their efficiency and precision. This paper evaluates the impact of different variable adjustment modes of the device on particulate deposition distribution through drone spreading experiments and particulate deposition data analysis. In this study, device structure change is the main variable factor, and flight altitude, flight speed and ambient wind speed are single quantitative factors. Experiments were conducted by varying the structure of the device to test the detailed deposition distribution of the device under group a, b, and c structures. Experimental results indicate that by choosing different variable combinations, the spreading device can achieve various fertilizer deposition states to meet regional needs. Among all 27 variable groups, the fertilizer particle deposition data for group b1b2b3 is relatively uniform, with three-quarters of particulate deposition values being 3 g/m² and the maximum value being 4 g/m². However, even with a relatively uniform distribution of fertilizer particles, the coefficient of variation for group b1b2b3 remains high (36.5%), with a range of 4.5% to 41%. Under different group adjustments, the particle distribution shows the smallest variability range in group b1b2b3, with a range of 15.71–26.44% and a variability difference of 10.73%. The particle distribution shows the largest variability range in group a1a2b3, with a range of 0.78–35.06% and a variability difference of 34.28%. These research conclusions provide important guidance for the study and practice of drone spreading systems. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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29 pages, 10032 KiB

Open AccessArticle

Using the MSFNet Model to Explore the Temporal and Spatial Evolution of Crop Planting Area and Increase Its Contribution to the Application of UAV Remote Sensing

by Gui Hu, Zhigang Ren, Jian Chen, Ni Ren and Xing Mao

Drones 2024, 8(9), 432; https://doi.org/10.3390/drones8090432 - 26 Aug 2024

Viewed by 547

Abstract

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in [...] Read more.

Remote sensing technology can be used to monitor changes in crop planting areas to guide agricultural production management and help achieve regional carbon neutrality. Agricultural UAV remote sensing technology is efficient, accurate, and flexible, which can quickly collect and transmit high-resolution data in real time to help precision agriculture management. It is widely used in crop monitoring, yield prediction, and irrigation management. However, the application of remote sensing technology faces challenges such as a high imbalance of land cover types, scarcity of labeled samples, and complex and changeable coverage types of long-term remote sensing images, which have brought great limitations to the monitoring of cultivated land cover changes. In order to solve the abovementioned problems, this paper proposed a multi-scale fusion network (MSFNet) model based on multi-scale input and feature fusion based on cultivated land time series images, and further combined MSFNet and Model Diagnostic Meta Learning (MAML) methods, using particle swarm optimization (PSO) to optimize the parameters of the neural network. The proposed method is applied to remote sensing of crops and tomatoes. The experimental results showed that the average accuracy, F1-score, and average IoU of the MSFNet model optimized by PSO + MAML (PSML) were 94.902%, 91.901%, and 90.557%, respectively. Compared with other schemes such as U-Net, PSPNet, and DeepLabv3+, this method has a better effect in solving the problem of complex ground objects and the scarcity of remote sensing image samples and provides technical support for the application of subsequent agricultural UAV remote sensing technology. The study found that the change in different crop planting areas was closely related to different climatic conditions and regional policies, which helps to guide the management of cultivated land use and provides technical support for the realization of regional carbon neutrality. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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21 pages, 4686 KiB

Open AccessArticle

Olive Tree Segmentation from UAV Imagery

by Konstantinos Prousalidis, Stavroula Bourou, Terpsichori-Helen Velivassaki, Artemis Voulkidis, Aikaterini Zachariadi and Vassilios Zachariadis

Drones 2024, 8(8), 408; https://doi.org/10.3390/drones8080408 - 21 Aug 2024

Viewed by 927

Abstract

This paper addresses the challenge of olive tree segmentation using drone imagery, which is crucial for precision agriculture applications. We tackle the data scarcity issue by augmenting existing detection datasets. Additionally, lightweight model variations of state-of-the-art models like YOLOv8n, RepViT-SAM, and EdgeSAM are [...] Read more.

This paper addresses the challenge of olive tree segmentation using drone imagery, which is crucial for precision agriculture applications. We tackle the data scarcity issue by augmenting existing detection datasets. Additionally, lightweight model variations of state-of-the-art models like YOLOv8n, RepViT-SAM, and EdgeSAM are combined into two proposed pipelines to meet computational constraints while maintaining segmentation accuracy. Our multifaceted approach successfully achieves an equilibrium among model size, inference time, and accuracy, thereby facilitating efficient olive tree segmentation in precision agriculture scenarios with constrained datasets. Following comprehensive evaluations, YOLOv8n appears to surpass the other models in terms of inference time and accuracy, albeit necessitating a more intricate fine-tuning procedure. Conversely, SAM-based pipelines provide a significantly more streamlined fine-tuning process, compatible with existing detection datasets for olive trees. However, this convenience incurs the disadvantages of a more elaborate inference architecture that relies on dual models, consequently yielding lower performance metrics and prolonged inference durations. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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29 pages, 3153 KiB

Open AccessArticle

TriNet: Exploring More Affordable and Generalisable Remote Phenotyping with Explainable Deep Models

by Lorenzo Beltrame, Jules Salzinger, Lukas J. Koppensteiner and Phillipp Fanta-Jende

Drones 2024, 8(8), 407; https://doi.org/10.3390/drones8080407 - 21 Aug 2024

Viewed by 989

Abstract

In this study, we propose a scalable deep learning approach to automated phenotyping using UAV multispectral imagery, exemplified by yellow rust detection in winter wheat. We adopt a high-granularity scoring method (1 to 9 scale) to align with international standards and plant breeders’ [...] Read more.

In this study, we propose a scalable deep learning approach to automated phenotyping using UAV multispectral imagery, exemplified by yellow rust detection in winter wheat. We adopt a high-granularity scoring method (1 to 9 scale) to align with international standards and plant breeders’ needs. Using a lower spatial resolution (60 m flight height at 2.5 cm GSD), we reduce the data volume by a factor of 3.4, making large-scale phenotyping faster and more cost-effective while obtaining results comparable to those of the state-of-the-art. Our model incorporates explainability components to optimise spectral bands and flight schedules, achieving top-three accuracies of 0.87 for validation and 0.67 and 0.70 on two separate test sets. We demonstrate that a minimal set of bands (EVI, Red, and GNDVI) can achieve results comparable to more complex setups, highlighting the potential for cost-effective solutions. Additionally, we show that high performance can be maintained with fewer time steps, reducing operational complexity. Our interpretable model components improve performance through regularisation and provide actionable insights for agronomists and plant breeders. This scalable and explainable approach offers an efficient solution for yellow rust phenotyping and can be adapted for other phenotypes and species, with future work focusing on optimising the balance between spatial, spectral, and temporal resolutions. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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25 pages, 5283 KiB

Open AccessArticle

Predicting Apple Tree Macronutrients Using Unmanned Aerial Vehicle-Based Hyperspectral Imagery to Manage Apple Orchard Nutrients

by Ye Seong Kang, Chan Seok Ryu, Jung Gun Cho and Ki Su Park

Drones 2024, 8(8), 369; https://doi.org/10.3390/drones8080369 - 1 Aug 2024

Cited by 1 | Viewed by 1175

Abstract

Herein, the development of an estimation model to measure the chlorophyll (Ch) and macronutrients, such as the total nitrogen (T-N), phosphorus (P), potassium (K), carbon (C), calcium (Ca), and magnesium (Mg), in apples is detailed, using key band ratios selected from hyperspectral imagery [...] Read more.

Herein, the development of an estimation model to measure the chlorophyll (Ch) and macronutrients, such as the total nitrogen (T-N), phosphorus (P), potassium (K), carbon (C), calcium (Ca), and magnesium (Mg), in apples is detailed, using key band ratios selected from hyperspectral imagery acquired with an unmanned aerial vehicle, for the management of nutrients in an apple orchard. The k-nearest neighbors regression (KNR) model for Ch and all macronutrients was chosen as the best model through a comparison of calibration and validation R² values. As a result of model development, a total of 13 band ratios (425/429, 682/686, 710/714, 714/718, 718/722, 750/754, 754/758, 758/762, 762/766, 894/898, 898/902, 906/911, and 963/967) were selected for Ch and all macronutrients. The estimation potential for the T-N and Mg concentrations was low, with an R² ≤ 0.37. The estimation performance for the other macronutrients was as follows: R² ≥ 0.70 and RMSE ≤ 1.43 μg/cm² for Ch; R² ≥ 0.44 and RMSE ≤ 0.04% for P; R² ≥ 0.53 and RMSE ≤ 0.23% for K; R² ≥ 0.85 and RMSE ≤ 6.18% for C; and R² ≥ 0.42 and RMSE ≤ 0.25% for Ca. Through establishing a fertilization strategy using the macronutrients estimated through hyperspectral imagery and measured soil chemical properties, this study presents a nutrient management decision-making method for apple orchards. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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20 pages, 3697 KiB

Open AccessArticle

Evaluation of Machine Learning Regression Techniques for Estimating Winter Wheat Biomass Using Biophysical, Biochemical, and UAV Multispectral Data

by Marco Spencer Chiu and Jinfei Wang

Drones 2024, 8(7), 287; https://doi.org/10.3390/drones8070287 - 26 Jun 2024

Cited by 2 | Viewed by 1627

Abstract

Crop above-ground biomass (AGB) estimation is a critical practice in precision agriculture (PA) and is vital for monitoring crop health and predicting yields. Accurate AGB estimation allows farmers to take timely actions to maximize yields within a given growth season. The objective of [...] Read more.

Crop above-ground biomass (AGB) estimation is a critical practice in precision agriculture (PA) and is vital for monitoring crop health and predicting yields. Accurate AGB estimation allows farmers to take timely actions to maximize yields within a given growth season. The objective of this study is to use unmanned aerial vehicle (UAV) multispectral imagery, along with derived vegetation indices (VI), plant height, leaf area index (LAI), and plant nutrient content ratios, to predict the dry AGB (g/m²) of a winter wheat field in southwestern Ontario, Canada. This study assessed the effectiveness of Random Forest (RF) and Support Vector Regression (SVR) models in predicting dry ABG from 42 variables. The RF models consistently outperformed the SVR models, with the top-performing RF model utilizing 20 selected variables based on their contribution to increasing node purity in the decision trees. This model achieved an R² of 0.81 and a root mean square error (RMSE) of 149.95 g/m². Notably, the variables in the top-performing model included a combination of MicaSense bands, VIs, nutrient content levels, nutrient content ratios, and plant height. This model significantly outperformed all other RF and SVR models in this study that relied solely on UAV multispectral data or plant leaf nutrient content. The insights gained from this model can enhance the estimation and management of wheat AGB, leading to more effective crop yield predictions and management. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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16 pages, 4076 KiB

Open AccessFeature PaperArticle

Ensemble Learning for Pea Yield Estimation Using Unmanned Aerial Vehicles, Red Green Blue, and Multispectral Imagery

by Zehao Liu, Yishan Ji, Xiuxiu Ya, Rong Liu, Zhenxing Liu, Xuxiao Zong and Tao Yang

Drones 2024, 8(6), 227; https://doi.org/10.3390/drones8060227 - 29 May 2024

Cited by 3 | Viewed by 946

Abstract

Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types [...] Read more.

Peas are one of the most important cultivated legumes worldwide, for which early yield estimations are helpful for agricultural planning. The unmanned aerial vehicles (UAVs) have become widely used for crop yield estimations, owing to their operational convenience. In this study, three types of sensor data (red green blue [RGB], multispectral [MS], and a fusion of RGB and MS) across five growth stages were applied to estimate pea yield using ensemble learning (EL) and four base learners (Cubist, elastic net [EN], K nearest neighbor [KNN], and random forest [RF]). The results showed the following: (1) the use of fusion data effectively improved the estimation accuracy in all five growth stages compared to the estimations obtained using a single sensor; (2) the mid filling growth stage provided the highest estimation accuracy, with coefficients of determination (R²) reaching up to 0.81, 0.8, 0.58, and 0.77 for the Cubist, EN, KNN, and RF algorithms, respectively; (3) the EL algorithm achieved the best performance in estimating pea yield than base learners; and (4) the different models were satisfactory and applicable for both investigated pea types. These results indicated that the combination of dual-sensor data (RGB + MS) from UAVs and appropriate algorithms can be used to obtain sufficiently accurate pea yield estimations, which could provide valuable insights for agricultural remote sensing research. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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11 pages, 4001 KiB

Open AccessArticle

Predicting Grape Yield with Vine Canopy Morphology Analysis from 3D Point Clouds Generated by UAV Imagery

by Adam Šupčík, Gabor Milics and Igor Matečný

Drones 2024, 8(6), 216; https://doi.org/10.3390/drones8060216 - 24 May 2024

Viewed by 1026

Abstract

With its ability to estimate yield, winemakers may better manage their vineyards and obtain important insights into the possible crop. The proper estimation of grape output is contingent upon an accurate evaluation of the morphology of the vine canopy, as this has a [...] Read more.

With its ability to estimate yield, winemakers may better manage their vineyards and obtain important insights into the possible crop. The proper estimation of grape output is contingent upon an accurate evaluation of the morphology of the vine canopy, as this has a substantial impact on the final product. This study’s main goals were to gather canopy morphology data using a sophisticated 3D model and assess how well different morphology characteristics predicted yield results. An unmanned aerial vehicle (UAV) with an RGB camera was used in the vineyards of Topoľčianky, Slovakia, to obtain precise orthophotos of individual vine rows. Following the creation of an extensive three-dimensional (3D) model of the assigned region, a thorough examination was carried out to determine many canopy characteristics, including thickness, side section dimensions, volume, and surface area. According to the study, the best combination for predicting grape production was the side section and thickness. Using more than one morphological parameter is advised for a more precise yield estimate as opposed to depending on only one. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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24 pages, 14167 KiB

Open AccessArticle

Multi-Altitude Corn Tassel Detection and Counting Based on UAV RGB Imagery and Deep Learning

by Shanwei Niu, Zhigang Nie, Guang Li and Wenyu Zhu

Drones 2024, 8(5), 198; https://doi.org/10.3390/drones8050198 - 14 May 2024

Viewed by 1322

Abstract

In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, [...] Read more.

In the context of rapidly advancing agricultural technology, precise and efficient methods for crop detection and counting play a crucial role in enhancing productivity and efficiency in crop management. Monitoring corn tassels is key to assessing plant characteristics, tracking plant health, predicting yield, and addressing issues such as pests, diseases, and nutrient deficiencies promptly. This ultimately ensures robust and high-yielding corn growth. This study introduces a method for the recognition and counting of corn tassels, using RGB imagery captured by unmanned aerial vehicles (UAVs) and the YOLOv8 model. The model incorporates the Pconv local convolution module, enabling a lightweight design and rapid detection speed. The ACmix module is added to the backbone section to improve feature extraction capabilities for corn tassels. Moreover, the CTAM module is integrated into the neck section to enhance semantic information exchange between channels, allowing for precise and efficient positioning of corn tassels. To optimize the learning rate strategy, the sparrow search algorithm (SSA) is utilized. Significant improvements in recognition accuracy, detection efficiency, and robustness are observed across various UAV flight altitudes. Experimental results show that, compared to the original YOLOv8 model, the proposed model exhibits an increase in accuracy of 3.27 percentage points to 97.59% and an increase in recall of 2.85 percentage points to 94.40% at a height of 5 m. Furthermore, the model optimizes frames per second (FPS), parameters (params), and GFLOPs (giga floating point operations per second) by 7.12%, 11.5%, and 8.94%, respectively, achieving values of 40.62 FPS, 14.62 MB, and 11.21 GFLOPs. At heights of 10, 15, and 20 m, the model maintains stable accuracies of 90.36%, 88.34%, and 84.32%, respectively. This study offers technical support for the automated detection of corn tassels, advancing the intelligence and precision of agricultural production and significantly contributing to the development of modern agricultural technology. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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19 pages, 10565 KiB

Open AccessFeature PaperArticle

Detection of Leak Areas in Vineyard Irrigation Systems Using UAV-Based Data

by Luís Pádua, Pedro Marques, Lia-Tânia Dinis, José Moutinho-Pereira, Joaquim J. Sousa, Raul Morais and Emanuel Peres

Drones 2024, 8(5), 187; https://doi.org/10.3390/drones8050187 - 8 May 2024

Viewed by 2034

Abstract

Water is essential for maintaining plant health and optimal growth in agriculture. While some crops depend on irrigation, others can rely on rainfed water, depending on regional climatic conditions. This is exemplified by grapevines, which have specific water level requirements, and irrigation systems [...] Read more.

Water is essential for maintaining plant health and optimal growth in agriculture. While some crops depend on irrigation, others can rely on rainfed water, depending on regional climatic conditions. This is exemplified by grapevines, which have specific water level requirements, and irrigation systems are needed. However, these systems can be susceptible to damage or leaks, which are not always easy to detect, requiring meticulous and time-consuming inspection. This study presents a methodology for identifying potential damage or leaks in vineyard irrigation systems using RGB and thermal infrared (TIR) imagery acquired by unmanned aerial vehicles (UAVs). The RGB imagery was used to distinguish between grapevine and non-grapevine pixels, enabling the division of TIR data into three raster products: temperature from grapevines, from non-grapevine areas, and from the entire evaluated vineyard plot. By analyzing the mean temperature values from equally spaced row sections, different threshold values were calculated to estimate and map potential leaks. These thresholds included the lower quintile value, the mean temperature minus the standard deviation (

T_{m e a n} - σ

), and the mean temperature minus two times the standard deviation (

T_{m e a n} - 2 σ

). The lower quintile threshold showed the best performance in identifying known leak areas and highlighting the closest rows that need inspection in the field. This approach presents a promising solution for inspecting vineyard irrigation systems. By using UAVs, larger areas can be covered on-demand, improving the efficiency and scope of the inspection process. This not only reduces water wastage in viticulture and eases grapevine water stress but also optimizes viticulture practices. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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20 pages, 3010 KiB

Open AccessArticle

Yield Prediction Using NDVI Values from GreenSeeker and MicaSense Cameras at Different Stages of Winter Wheat Phenology

by Sándor Zsebő, László Bede, Gábor Kukorelli, István Mihály Kulmány, Gábor Milics, Dávid Stencinger, Gergely Teschner, Zoltán Varga, Viktória Vona and Attila József Kovács

Drones 2024, 8(3), 88; https://doi.org/10.3390/drones8030088 - 5 Mar 2024

Cited by 5 | Viewed by 3167

Abstract

This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving [...] Read more.

This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P₂O₅-77.5, K₂O-0; Balance: N-135.1, P₂O₅-91, K₂O-0; Genezis: N-135, P₂O₅-75, K₂O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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20 pages, 16424 KiB

Open AccessEditor’s ChoiceArticle

Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model

by Zhao Liu, Huapeng Li, Xiaohui Ding, Xinyuan Cao, Hui Chen and Shuqing Zhang

Drones 2023, 7(9), 586; https://doi.org/10.3390/drones7090586 - 19 Sep 2023

Cited by 2 | Viewed by 2002

Abstract

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, [...] Read more.

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R² value was 0.704, the RMSE was 34.58 μg/cm², and the MAE was 26.27 μg/cm². The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R² values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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18 pages, 11917 KiB

Open AccessArticle

Missing Plant Detection in Vineyards Using UAV Angled RGB Imagery Acquired in Dormant Period

by Salvatore Filippo Di Gennaro, Gian Luca Vannini, Andrea Berton, Riccardo Dainelli, Piero Toscano and Alessandro Matese

Drones 2023, 7(6), 349; https://doi.org/10.3390/drones7060349 - 26 May 2023

Cited by 6 | Viewed by 2720

Abstract

Since 2010, more and more farmers have been using remote sensing data from unmanned aerial vehicles, which have a high spatial–temporal resolution, to determine the status of their crops and how their fields change. Imaging sensors, such as multispectral and RGB cameras, are [...] Read more.

Since 2010, more and more farmers have been using remote sensing data from unmanned aerial vehicles, which have a high spatial–temporal resolution, to determine the status of their crops and how their fields change. Imaging sensors, such as multispectral and RGB cameras, are the most widely used tool in vineyards to characterize the vegetative development of the canopy and detect the presence of missing vines along the rows. In this study, the authors propose different approaches to identify and locate each vine within a commercial vineyard using angled RGB images acquired during winter in the dormant period (without canopy leaves), thus minimizing any disturbance to the agronomic practices commonly conducted in the vegetative period. Using a combination of photogrammetric techniques and spatial analysis tools, a workflow was developed to extract each post and vine trunk from a dense point cloud and then assess the number and position of missing vines with high precision. In order to correctly identify the vines and missing vines, the performance of four methods was evaluated, and the best performing one achieved 95.10% precision and 92.72% overall accuracy. The results confirm that the methodology developed represents an effective support in the decision-making processes for the correct management of missing vines, which is essential for preserving a vineyard’s productive capacity and, more importantly, to ensure the farmer’s economic return. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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23 pages, 13722 KiB

Open AccessArticle

Numerical Analysis and Wind Tunnel Validation of Droplet Distribution in the Wake of an Unmanned Aerial Spraying System in Forward Flight

by Manuel Carreño Ruiz, Nicoletta Bloise, Giorgio Guglieri and Domenic D’Ambrosio

Drones 2022, 6(11), 329; https://doi.org/10.3390/drones6110329 - 29 Oct 2022

Cited by 14 | Viewed by 2602

Abstract

Recent developments in agriculture mechanization have generated significant challenges towards sustainable approaches to reduce the environmental footprint and improve food quality. This paper highlights the benefits of using unmanned aerial systems (UASs) for precision spraying applications of pesticides, reducing the environmental risk and [...] Read more.

Recent developments in agriculture mechanization have generated significant challenges towards sustainable approaches to reduce the environmental footprint and improve food quality. This paper highlights the benefits of using unmanned aerial systems (UASs) for precision spraying applications of pesticides, reducing the environmental risk and waste caused by spray drift. Several unmanned aerial spraying system (UASS) operation parameters and spray system designs are examined to define adequate configurations for specific treatments. A hexarotor DJI Matrice 600 equipped with T-Motor “15 × 5” carbon fiber blades is tested numerically using computational fluid dynamics (CFD) and experimentally in a wind tunnel. These tests assess the aerodynamic interaction between the wake of an advancing multicopter and the fine droplets generated by atomizers traditionally used in agricultural applications. The aim of this research is twofold. First, we analyze the effects of parameters such as flight speed (0, 2, and 3 m·s

^{- 1}

), nozzle type (hollowcone and fan), and injection pressure (2–3 bar) on spray distribution. In the second phase, we use data from the experimental campaign to validate numerical tools for the simulation of rotor–droplet interactions necessary to predict spray’s ground footprint and to plan a precise guidance algorithm to achieve on-target deposition and reduce the well-known droplet drift problem. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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Review

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30 pages, 929 KiB

Open AccessReview

Drones in Precision Agriculture: A Comprehensive Review of Applications, Technologies, and Challenges

by Ridha Guebsi, Sonia Mami and Karem Chokmani

Drones 2024, 8(11), 686; https://doi.org/10.3390/drones8110686 - 19 Nov 2024

Viewed by 712

Abstract

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides [...] Read more.

In the face of growing challenges in modern agriculture, such as climate change, sustainable resource management, and food security, drones are emerging as essential tools for transforming precision agriculture. This systematic review, based on an in-depth analysis of recent scientific literature (2020–2024), provides a comprehensive synthesis of current drone applications in the agricultural sector, primarily focusing on studies from this period while including a few notable exceptions of particular interest. Our study examines in detail the technological advancements in drone systems, including innovative aerial platforms, cutting-edge multispectral and hyperspectral sensors, and advanced navigation and communication systems. We analyze diagnostic applications, such as crop monitoring and multispectral mapping, as well as interventional applications like precision spraying and drone-assisted seeding. The integration of artificial intelligence and IoTs in analyzing drone-collected data is highlighted, demonstrating significant improvements in early disease detection, yield estimation, and irrigation management. Specific case studies illustrate the effectiveness of drones in various crops, from viticulture to cereal cultivation. Despite these advancements, we identify several obstacles to widespread drone adoption, including regulatory, technological, and socio-economic challenges. This study particularly emphasizes the need to harmonize regulations on beyond visual line of sight (BVLOS) flights and improve economic accessibility for small-scale farmers. This review also identifies key opportunities for future research, including the use of drone swarms, improved energy autonomy, and the development of more sophisticated decision-support systems integrating drone data. In conclusion, we underscore the transformative potential of drones as a key technology for more sustainable, productive, and resilient agriculture in the face of global challenges in the 21st century, while highlighting the need for an integrated approach combining technological innovation, adapted policies, and farmer training. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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23 pages, 1780 KiB

Open AccessFeature PaperReview

Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms

by Takashi Sonam Tashi Tanaka, Sheng Wang, Johannes Ravn Jørgensen, Marco Gentili, Armelle Zaragüeta Vidal, Anders Krogh Mortensen, Bharat Sharma Acharya, Brittany Deanna Beck and René Gislum

Drones 2024, 8(6), 212; https://doi.org/10.3390/drones8060212 - 21 May 2024

Cited by 7 | Viewed by 1943

Abstract

The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate [...] Read more.

The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate the advantages and challenges of UAV-based phenotyping techniques. This is a comprehensive overview summarizing the UAV platforms, sensors, and data processing while also introducing recent technological developments. Recently developed software and sensors greatly enhance the accessibility of UAV-based phenotyping, and a summary of recent research (publications 2019–2024) provides implications for future research. Researchers have focused on integrating multiple sensing data or utilizing machine learning algorithms, such as ensemble learning and deep learning, to enhance the prediction accuracies of crop physiological traits. However, this approach will require big data alongside laborious destructive measurements in the fields. Future research directions will involve standardizing the process of merging data from multiple field experiments and data repositories. Previous studies have focused mainly on UAV technology in major crops, but there is a high potential in minor crops or cropping systems for future sustainable crop production. This review can guide new practitioners who aim to implement and utilize UAV-based phenotyping. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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29 pages, 5721 KiB

Open AccessReview

An Intelligent Grazing Development Strategy for Unmanned Animal Husbandry in China

by Yuanyang Cao, Tao Chen, Zichao Zhang and Jian Chen

Drones 2023, 7(9), 542; https://doi.org/10.3390/drones7090542 - 22 Aug 2023

Cited by 2 | Viewed by 2564

Abstract

Grazing is the most important and lowest cost means of livestock breeding. Because of the sharp contradiction between the grassland ecosystem and livestock, the grassland ecosystem has tended to degrade in past decades in China; therefore, the ecological balance of the grassland has [...] Read more.

Grazing is the most important and lowest cost means of livestock breeding. Because of the sharp contradiction between the grassland ecosystem and livestock, the grassland ecosystem has tended to degrade in past decades in China; therefore, the ecological balance of the grassland has been seriously damaged. The implementation of grazing prohibition, rotational grazing and the development of a large-scale breeding industry have not only ensured the supply of animal husbandry products, but also promoted the restoration of the grassland ecosystem. For the large-scale breeding industry, the animal welfare of livestock cannot be guaranteed due to the narrow and crowded space, thus, the production of the breeding industry usually has lower competitiveness than grazing. Disorderly grazing leads to grassland ecological crises; however, intelligent grazing can not only ensure animal welfare, but also fully improve the competitiveness of livestock husbandry products. Under the development of urbanization, the workforce engaged in grazing and breeding in pastoral areas is gradually lost. Intelligent grazing breeding methods need to be developed and popularized. This paper focuses on intelligent grazing, reviews grass remote sensing and aerial seeding, wearable monitoring equipment of livestock, UAV monitoring and intelligent grazing robots, and summarizes the development of intelligent grazing elements, exploring the new development direction of automatic grazing management with the grazing robot at this stage. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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23 pages, 6249 KiB

Open AccessEditor’s ChoiceReview

Independent Control Spraying System for UAV-Based Precise Variable Sprayer: A Review

by Adhitya Saiful Hanif, Xiongzhe Han and Seung-Hwa Yu

Drones 2022, 6(12), 383; https://doi.org/10.3390/drones6120383 - 28 Nov 2022

Cited by 34 | Viewed by 14265

Abstract

Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial [...] Read more.

Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial vehicle (UAV) variable-rate spraying offers a precise and adaptable alternative strategy for overcoming these challenges. This study explores research trends in the application of semi-automatic approaches and land-specific platforms for precision spraying. The employment of an autonomous control system, together with a selection of hardware such as microcontrollers, sensors, pumps, and nozzles, yields the performance necessary to accomplish spraying precision, UAV performance efficacy, and flexibility in meeting plant pesticide requirements. This paper discusses the implications of ongoing and developing research. The comparison of hardware, control system approaches, and data acquisition from the parameters of each study is presented to facilitate future research. Future research is incentivized to continue the precision performance of the variable rate development by combining it with cropland mapping to determine the need for pesticides, although strict limits on the amount of spraying make it difficult to achieve the same, even though the quality is very beneficial. Full article

(This article belongs to the Special Issue Advances of UAV in Precision Agriculture)

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