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Application of UAVs in Precision Agriculture—2nd Edition

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A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 20 January 2025 | Viewed by 7955

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Special Issue Editors

Prof. Dr. Jiyu Li

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Guest Editor

College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: UAV; rotor airflow; drone; energy and payload; UAV architecture design; airflow sensor; droplet sensor; operation route planning; control system; artificial intelligence; operation effect; operation efficiency
Special Issues, Collections and Topics in MDPI journals

Dr. Jiating Li

E-Mail Website
Guest Editor

Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
Interests: precision and digital agriculture; UAV; image processing; spectroscopy; machine and deep learning; radiative transfer model; high-throughput plant phenotyping

Dr. Suiyuan Shen

E-Mail Website
Guest Editor

College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: flight control system design; flight simulation technology; flight dynamics; aerodynamics of rotorcraft; intelligent control algorithm; overall design of novel configuration aircraft

Special Issue Information

Dear Colleagues,

The use of agricultural UAVs has become an essential part of modern agriculture due to their high operational efficiency. The key elements of agricultural UAVs include rotor-motors, airframe architecture, airflow distribution, sensors, operational components, and control systems, all of which have complex and multidimensional interactions that affect the operational effectiveness of agricultural UAVs. Therefore, current research on agricultural drone operations is rapidly developing in the direction of simulation calculation, environmental perception, multidimensional control, and precision operations. This not only improves the performance of agricultural drones in remote sensing, spraying, sowing, and pollination but also demonstrates their capabilities in more traditional agricultural fields.

This Special Issue is a natural continuation of our previous Special Issue, titled “Application of UAVs in Precision Agriculture”. The focus of this Special Issue is on simulation calculation, environmental perception, multidimensional control, and precision operations. The theme of this Special Issue is “Improving the operational effectiveness of agricultural UAVs,” covering interdisciplinary research in agriculture, biology, electronics, engineering, and other fields. Operational drones in various applications, such as orchards, fields, cash crops, and ecosystems, all fall within the scope of this Special Issue. We welcome the submission of various types of articles, such as original research papers and reviews.

Prof. Dr. Jiyu Li
Dr. Jiating Li
Dr. Suiyuan Shen
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agriculture is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

agricultural UAVs
effects of operation
efficiency of operation
route planning
energy-consumption matching
variable spray
drift suppression
deposition of droplets

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Published Papers (7 papers)

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Research

18 pages, 11083 KiB

Open AccessArticle

Influence of Spatial Scale Effect on UAV Remote Sensing Accuracy in Identifying Chinese Cabbage (Brassica rapa subsp. Pekinensis) Plants

by Xiandan Du, Zhongfa Zhou and Denghong Huang

Agriculture 2024, 14(11), 1871; https://doi.org/10.3390/agriculture14111871 - 23 Oct 2024

Viewed by 564

Abstract

The exploration of the impact of different spatial scales on the low-altitude remote sensing identification of Chinese cabbage (Brassica rapa subsp. Pekinensis) plants offers important theoretical reference value in balancing the accuracy of plant identification with work efficiency. This study focuses on Chinese cabbage plants during the rosette stage; RGB images were obtained by drones at different flight heights (20 m, 30 m, 40 m, 50 m, 60 m, and 70 m). Spectral sampling analysis was conducted on different ground backgrounds to assess their separability. Based on the four commonly used vegetation indices for crop recognition, the Excess Green Index (ExG), Red Green Ratio Index (RGRI), Green Leaf Index (GLI), and Excess Green Minus Excess Red Index (ExG-ExR), the optimal index was selected for extraction. Image processing methods such as frequency domain filtering, threshold segmentation, and morphological filtering were used to reduce the impact of weed and mulch noise on recognition accuracy. The recognition results were vectorized and combined with field data for the statistical verification of accuracy. The research results show that (1) the ExG can effectively distinguish between soil, mulch, and Chinese cabbage plants; (2) images of different spatial resolutions differ in the optimal type of frequency domain filtering and convolution kernel size, and the threshold segmentation effect also varies; (3) as the spatial resolution of the imagery decreases, the optimal window size for morphological filtering also decreases, accordingly; and (4) at a flight height of 30 m to 50 m, the recognition effect is the best, achieving a balance between recognition accuracy and coverage efficiency. The method proposed in this paper is beneficial for agricultural growers and managers in carrying out precision planting management and planting structure optimization analysis and can aid in the timely adjustment of planting density or layout to improve land use efficiency and optimize resource utilization. Full article

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

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18 pages, 9929 KiB

Open AccessArticle

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

by Bingquan Tian, Hailin Yu, Shuailing Zhang, Xiaoli Wang, Lei Yang, Jingqian Li, Wenhao Cui, Zesheng Wang, Liqun Lu, Yubin Lan and Jing Zhao

Agriculture 2024, 14(9), 1452; https://doi.org/10.3390/agriculture14091452 - 25 Aug 2024

Viewed by 909

Abstract

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R² of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R² of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels. Full article

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

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17 pages, 6424 KiB

Open AccessArticle

Wind Vortex Target Control of a Plant Protection UAV Based on a Rice Wind Vortex–Flight Parameter Model

by Hang Xing, Zhijie Liu, Taoran Huang, Minyue Dong, Jia Lv and Feng Tang

Agriculture 2024, 14(8), 1413; https://doi.org/10.3390/agriculture14081413 - 21 Aug 2024

Viewed by 717

Abstract

The strong airflow beneath a rotary drone generates a wind vortex within the rice canopy; precise control of the wind vortex distance and wind vortex area can improve pesticide utilization efficiency. This paper calculates the flight parameter curve based on the wind vortex target from the wind vortex target parameter control model of the four-rotor plant protection drone, designs a flight control system using a Cube Orange flight controller and a Jetson AGX Xavier onboard computer, and implements flight parameter control using both PID control and fuzzy control algorithms. Experimental results indicate that when using PID control and fuzzy control, the average deviation values of UAV flight altitude and speed are 0.08 m, 0.08 m/s, 0.06 m, and 0.08 m/s, respectively. When using PID control, the average distance and area errors of the target downwind and upwind are 0.17 m and 0.37 m² and 0.20 m and 0.46 m², respectively. The corresponding values for fuzzy control are 0.12 m, 0.38 m², 0.09 m, and 0.31 m². In the twelve voyage experiments, the target parameter variance using fuzzy control was relatively smaller for eight voyages compared to PID control, which had a smaller variance for four voyages. On the whole, the effect of fuzzy control is superior. The wind vortex control method proposed in this paper can effectively enable precise pesticide spraying by drones. This has significant implications for reducing agricultural production costs and safeguarding the natural environment. Full article

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

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29 pages, 11497 KiB

Open AccessArticle

Study on the Characteristics of Downwash Field Range and Consistency of Spray Deposition of Agricultural UAVs

by Zongru Liu, Rong Gao, Yinwei Zhao, Han Wu, Yunting Liang, Ke Liang, Dong Liu, Taoran Huang, Shaoqiang Xie, Jia Lv and Jiyu Li

Agriculture 2024, 14(6), 931; https://doi.org/10.3390/agriculture14060931 - 13 Jun 2024

Cited by 1 | Viewed by 985

Abstract

Agricultural unmanned aerial vehicles (UAVs), increasingly integral to crop protection through spraying operations, are significantly influenced by their downwash fields, which in turn affect the distribution of spray droplets. The key parameters impacting spray deposition patterns are the velocity of the downwash airflow and its spatial extent. Understanding the interplay of these parameters can enhance the efficacy of UAV applications in agriculture. Previous research has predominantly focused on downwash airflow velocity, often neglecting the spatial scope of the downwash. This paper presents an applied foundational study grounded in the compressible Reynolds-averaged Navier–Stokes (RANS) equations. Leveraging a dependable

k

ε

turbulence model and dynamic mesh technology, it develops an effective three-dimensional computational fluid dynamics (CFD) approach to analyze the downwash field’s distribution characteristics during UAV hover. To validate the CFD method, a visualization test was conducted using EPS (expanded polystyrene foam) balls dispersed in the airspace beneath the UAV, illustrating the airflow’s spatial distribution. Additionally, a parameter η was introduced to quantify changes in the wind field’s range, enabling the mapping of the cross-sectional area of the downwash airflow at various velocities within the UAV’s airspace. The study reveals that the downwash field’s overall shape evolves from a “four-point type” to a “square-like” and then to an “ellipse-like” configuration. Lower downwash airflow velocities exhibit a more rapid expansion of the wind field area. High-velocity downwash areas are concentrated beneath each rotor, while lower-velocity zones coalesce under each rotor and extend downward, forming a continuous expanse. Within the UAV’s downwash area, the deposition of droplets is more pronounced. At a given nozzle position, an increase in downwash airflow velocity correlates with greater droplet deposition within the downwash field. This research bridges a gap in downwash field studies, offering a solid theoretical foundation for the development of future UAV downwash field models. Full article

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

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18 pages, 17503 KiB

Open AccessArticle

Recognition of Plastic Film in Terrain-Fragmented Areas Based on Drone Visible Light Images

by Xiaoyi Du, Denghong Huang, Li Dai and Xiandan Du

Agriculture 2024, 14(5), 736; https://doi.org/10.3390/agriculture14050736 - 9 May 2024

Cited by 2 | Viewed by 1043

Abstract

In order to meet the growing demand for food and achieve food security development goals, contemporary agriculture increasingly depends on plastic coverings such as agricultural plastic films. The remote sensing-based identification of these plastic films has gradually become a necessary tool for agricultural production management and soil pollution prevention. Addressing the challenges posed by the complex terrain and fragmented land parcels in karst mountainous regions, as well as the frequent presence of cloudy and foggy weather conditions, the extraction efficacy of mulching films is compromised. This study utilized a DJI Mavic 2 Pro UAV to capture visible light images in an area with complex terrain features such as peaks and valleys. A plastic film sample dataset was constructed, and the U-Net deep learning model parameters integrated into ArcGIS Pro were continuously modified and optimized to achieve precise plastic film identification. The results are as follows: (1) Sample quantity significantly affects recognition performance. When the sample size is 800, the accuracy of plastic film extraction notably improves, with area accuracy reaching 91%, a patch quantity accuracy of 96.38%, and an IOU and F1-score of 85.89% and 94.20%, respectively, compared to the precision achieved with a sample size of 300; (2) Different learning rates, batch sizes, and iteration numbers have a certain impact on the training effectiveness of the U-Net model. The most suitable model parameters improved the training effectiveness, with the highest training accuracy achieved at a learning rate of 0.001, a batch size of 10, and 25 iterations; (3) Comparative experiments with the Support Vector Machine (SVM) model validate the suitability of U-Net model parameters and sample datasets for precise identification in rugged terrains with fragmented spatial distribution, particularly in karst mountainous regions. This underscores the applicability of the U-Net model in recognizing plastic film coverings in karst mountainous regions, offering valuable insights for agricultural environmental health assessment and green planting management in farmlands. Full article

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

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25 pages, 15202 KiB

Open AccessArticle

Research on the Effect Characteristics of Free-Tail Layout Parameters on Tail-Sitter VTOL UAVs

by Hao Qi, Shi-Jie Cao, Jia-Yue Wu, Yi-Ming Peng, Hong Nie and Xiao-Hui Wei

Agriculture 2024, 14(3), 472; https://doi.org/10.3390/agriculture14030472 - 15 Mar 2024

Cited by 1 | Viewed by 1357

Abstract

The tail-sitter VTOL UAV boasts not only high-speed cruising and air hovering capabilities, but also its unique tail-sitting vertical takeoff and landing and hovering attitude enable aerial operations with an exceptionally small cross-sectional area. This feature effectively broadens the scope of application for the UAV in intelligent agriculture, encompassing tasks such as agricultural inspection, production monitoring, and topographic mapping. Given the necessity for frequent modal transitions, this paper is grounded in a thorough examination of the typical structural characteristics of the tail-sitter VTOL UAV. A comprehensive technical solution for tail-sitter VTOL UAVs, based on the free-tail configuration, is proposed in this paper. The free-tail structure is utilized to address the limitations of traditional tailless layout and fixed landing gear in terms of flight stability and takeoff/landing performance of tail-sitter VTOL UAVs. However, the implementation of this solution necessitates the addition of a new maneuvering unit. Consequently, this paper delves into the aerodynamic coupling characteristics and laws between the layout parameters such as tail number, tail length, and tail area and the tail-sitter VTOL UAV fuselage. To optimize the free-tail configuration, a multi-objective optimization is performed by integrating the overall UAV dynamics, landing dynamics, and modal transition trajectory constraints. The results of stability modeling simulations and flight tests demonstrate that the tail-sitter VTOL UAV equipped with this technical solution exhibits enhanced maneuverability and flight efficiency compared to the conventional tailless layout. Full article

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

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19 pages, 7359 KiB

Open AccessArticle

Complex Habitat Deconstruction and Low-Altitude Remote Sensing Recognition of Tobacco Cultivation on Karst Mountainous

by Youyan Huang, Lihui Yan, Zhongfa Zhou, Denghong Huang, Qianxia Li, Fuxianmei Zhang and Lu Cai

Agriculture 2024, 14(3), 411; https://doi.org/10.3390/agriculture14030411 - 3 Mar 2024

Viewed by 1234

Abstract

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting management, precise fertilization, and yield prediction. In the karst mountainous of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats, and uneven plant growth. This study took a tobacco planting area in Guizhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds, and shadow masking, this area was classified into eight habitats. The U-Net model was trained using different habitat datasets. The results show that (1) the overall precision, recall, F1-score, and Intersection over Union (IOU) of tobacco plant information extraction were 0.68, 0.85, 0.75, and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can blur images, reducing the plant-identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas. Full article

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

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