Agricultural Unmanned Aerial Vehicle Research Articles

Quadcopters in Smart Agriculture: Applications and Modelling

Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments.

Two-dimensional hyperchaos-based encryption and compression algorithm for agricultural UAV-captured planar images.

This study presents an approach that integrates compressed sensing technology with two-dimensional hyperchaotic coupled Fourier oscillator systems (2D-HCFOS) to address the challenge of slow encryption speeds in agricultural unmanned aerial vehicles (UAVs). The primary challenge in enhancing encryption speed lies in the limited capacity inherent in traditional chaotic-based systems and the computational complexity of their processes. The 2D-HCFOS utilizes a complex two-dimensional hybrid chaotic system, which significantly enhances the security of agricultural UAV image data. Notably, the image encryption process is performed on a personal computer connected to the drone, ensuring efficient processing. By integrating advanced Fourier series and nonlinear coupled oscillators, the model surpasses existing chaotic-based methods, improving both the pseudo-randomness and robustness of encryption. Additionally, incorporating Bonouille functions into the discrete cosine transform (DCT) domain results in a sparser measurement matrix, which is essential for efficient encryption on personal computers. The effectiveness of 2D-HCFOS in securely encrypting agricultural drone images has been rigorously validated through simulations and analytical evaluations using sophisticated row, rotation, and matrix encryption techniques. The improved security performance is further verified by comparative analysis. Compared with other models, the Lyapunov index of 2D-HCFOS is 15.1039, and the sample entropy is 2.4987, indicating that it possesses superior chaotic performance and encryption reliability.

Functional integration design and aerodynamic characterization of a hexacopter agricultural unmanned aerial vehicle based on its appearance

Functional integration design and aerodynamic characterization of a hexacopter agricultural unmanned aerial vehicle based on its appearance

Application of Drones Technology in Agriculture: A Modern Approach

Drone technology can be used for many applications for agricultural uses, including crop health monitoring and farm operations like weed management, Evapotranspiration estimation, spraying etc. For agricultural applications, regularized smart-farming solutions are being considered, including the use of unmanned aerial vehicles (UAVs). The agricultural UAVs are highly capable, and their use has expanded across all areas of agriculture, including pesticide and fertilizer spraying, seed sowing, and growth assessment and mapping. Accordingly, the market for agricultural UAVs is expected to continue growing with the related technologies. In this study, we consider the latest trends and applications of leading technologies related to agricultural UAVs, control technologies, equipment, and development. We discuss the use of UAVs in real agricultural environments. Furthermore, the future development of the agricultural UAVs and their challenges are presented. By flying safely and at relatively high speeds, drones can cover large and small properties quickly to manage assets, resources, and land. Many farms are now using drones to check and maintain water levels in dams and other resources previously inaccessible whilst removing any risk to the operator or surrounding area. In order to produce better crop quality and shield fields from harm, this study paper aims to emphasise the value of drones in agriculture and describe the best drones currently on the market for this purpose.

Taoism-Net: A Fruit Tree Segmentation Model Based on Minimalism Design for UAV Camera

The development of precision agriculture requires unmanned aerial vehicles (UAVs) to collect diverse data, such as RGB images, 3D point clouds, and hyperspectral images. Recently, convolutional networks have made remarkable progress in downstream visual tasks, while often disregarding the trade-off between accuracy and speed in UAV-based segmentation tasks. The study aims to provide further valuable insights using an efficient model named Taoism-Net. The findings include the following: (1) Prescription maps in agricultural UAVs requires pixel-level precise segmentation, with many focusing solely on accuracy at the expense of real-time processing capabilities, being incapable of satisfying the expectations of practical tasks. (2) Taoism-Net is a refreshingly segmented model, overcoming the challenges of complexity in deep learning, based on minimalist design, which is used to generate prescription maps through pixel level classification mapping of geodetic coordinates (the lychee tree aerial dataset in Guangdong is used for experiments). (3) Compared with mainstream lightweight models or mature segmentation algorithms, Taoism-Net achieves significant improvements, including an improvement of at least 4.8% in mIoU, and manifested a superior performance in the accuracy–latency curve. (4) “The greatest truths are concise” is a saying widely spread by ancient Taoism, indicating that the most fundamental approach is reflected through the utmost minimalism; moreover, Taoism-Net expects to a build bridge between academic research and industrial deployment, for example, UAVs in precision agriculture.

Material and Airfoil-Based Wind Dynamics Analysis for Fixed-Wing Agricultural Unmanned Aerial Vehicles (UAV)

Material and Airfoil-Based Wind Dynamics Analysis for Fixed-Wing Agricultural Unmanned Aerial Vehicles (UAV)

基于EMSDBO算法农业植保无人机三维航迹多目标优化研究

Both cruising ability and safety should be considered in the 3D inspection path planning of agricultural unmanned aerial vehicles (UAVs). Specific to a complex working environment, the 3D inspection environment of agricultural UAVs was simulated through terrain modeling and threat modeling. First, the dynamic constraints of flight approaching rate and response time were added to the threat cost, and the 3D mission space model and flight path cost function were constructed considering the influence of UAVs’ turning performance. Second, the offset estimation strategy, variable spiral search strategy, quasi-reverse learning strategy and dimension-by-dimension mutation strategy were introduced into the dung beetle optimizer (DBO) algorithm to improve the global optimization ability and convergence rate of the algorithm. By establishing a three-dimensional trajectory planning model for unmanned aerial vehicles, the trajectory planning is transformed into a multi-objective function optimization problem, and an improved algorithm is used to solve the three-dimensional trajectory planning of unmanned aerial vehicles. The fitness is evaluated by considering the objective function of trajectory cost, terrain cost, and danger level, and the trajectory planning is iteratively optimized. The results indicate that the proposed improved dung beetle algorithm for trajectory planning has lower overall cost and stability in adapting to different complex terrain environments.

Design and implementation of an AI-controlled spraying drone for agricultural applications using advanced image preprocessing techniques

PurposeNowadays, food problems are likely to arise because of the increasing global population and decreasing arable land. Therefore, it is necessary to increase the yield of agricultural products. Pesticides can be used to improve agricultural land products. This study aims to make the spraying of cherry trees more effective and efficient with the designed artificial intelligence (AI)-based agricultural unmanned aerial vehicle (UAV).Design/methodology/approachTwo approaches have been adopted for the AI-based detection of cherry trees: In approach 1, YOLOv5, YOLOv7 and YOLOv8 models are trained with 70, 100 and 150 epochs. In Approach 2, a new method is proposed to improve the performance metrics obtained in Approach 1. Gaussian, wavelet transform (WT) and Histogram Equalization (HE) preprocessing techniques were applied to the generated data set in Approach 2. The best-performing models in Approach 1 and Approach 2 were used in the real-time test application with the developed agricultural UAV.FindingsIn Approach 1, the best F1 score was 98% in 100 epochs with the YOLOv5s model. In Approach 2, the best F1 score and mAP values were obtained as 98.6% and 98.9% in 150 epochs, with the YOLOv5m model with an improvement of 0.6% in the F1 score. In real-time tests, the AI-based spraying drone system detected and sprayed cherry trees with an accuracy of 66% in Approach 1 and 77% in Approach 2. It was revealed that the use of pesticides could be reduced by 53% and the energy consumption of the spraying system by 47%.Originality/valueAn original data set was created by designing an agricultural drone to detect and spray cherry trees using AI. YOLOv5, YOLOv7 and YOLOv8 models were used to detect and classify cherry trees. The results of the performance metrics of the models are compared. In Approach 2, a method including HE, Gaussian and WT is proposed, and the performance metrics are improved. The effect of the proposed method in a real-time experimental application is thoroughly analyzed.

Quantity Monitor Based on Differential Weighing Sensors for Storage Tank of Agricultural UAV

Nowadays, unmanned aerial vehicles (UAVs) play a pivotal role in agricultural production. In scenarios involving the release of particulate materials, the precision of quantity monitors for the storage tank of UAVs directly impacts its operational accuracy. Therefore, this paper introduces a novel noise-mitigation design for agricultural UAVs’ quantity monitors, utilizing differential weighing sensors. The design effectively addresses three primary noise sources: sensor-intrinsic noise, vibration noise, and weight-loading uncertainty. Additionally, two comprehensive data processing methods are proposed for noise reduction: the first combines the Butterworth low-pass filter, the Kalman filter, and the moving average filter (BKM), while the second integrates the Least Mean Squares (LMS) adaptive filter, the Kalman filter, and the moving average filter (LKM). Rigorous data processing has been conducted, and the monitor’s performance has been assessed in three UAV typical states: static, hovering, and flighting. Specifically, compared to the BKM, the LKM’s maximum relative error ranges between 1.24% and 2.74%, with an average relative error of 0.31%~0.58% when the UAV was in a hovering state. In flight mode, the LKM’s maximum relative error varies from 1.68% to 10.06%, while the average relative error ranges between 0.74% and 2.54%. Furthermore, LKM can effectively suppress noise interference near 75 Hz and 150 Hz. The results reveal that the LKM technology demonstrated superior adaptability to noise and effectively mitigates its impact in the quantity monitoring for storage tank of agricultural UAVs.

Precision agriculture in the regional system of strategic development of grain production

Subject. The article investigates strategic development of grain production, which, in the future, will increasingly rely on elements of precision agriculture. Their use enables to significantly increase the yield of grain crops due to systematic implementation of specialized digital technologies. The latter, under scarce resources, is a determinant in the long-term development of the grain industry. Objectives. The study aims to describe precision agriculture in the regional system of strategic development of grain production. Methods. I performed a comprehensive assessment of effectiveness of precision farming elements introduction by such indicators as the specific weight of the area of grain fields, the specific weight of synchronized agricultural machinery and other precision farming elements in a single digital space, and the share of covering the need for agricultural unmanned aerial vehicles and crop sensing systems. The Ministry of Agriculture and Food Resources of the Nizhny Novgorod Oblast data on the number of used precision farming elements served as the analytical base of the study. Results. In the Buturlinsky and Dalnekonstantinovsky municipal districts, the introduction of precision farming elements is at an average systemic level, in Arzamas urban district and Bolshemurashkinsky municipal district – at a low systemic level, and in the rest, it is unsystematical. I offered and tested indicators of comprehensive assessment of effectiveness of precision farming elements introduction. Their analysis helped form a rating of the introduction of precision farming elements in the Nizhny Novgorod Oblast. Conclusions. Each farm should determine its own level of use of precision farming elements to generate a strategy for introduction of other elements of technology to maximize economic efficiency, including synergistic effect.

Remote Sensing Revolution: Mapping Land Productivity and Vegetation Trends with Unmanned Aerial Vehicles (UAVs)

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

Значимые факторы развития рынка сельскохозяйственных беспилотных летательных аппаратов в новых реалиях

Abstract. The article studies the development of the unmanned aerial vehicle (UAV) market in agriculture in the regions of the Russian Federation in the new realities. The purpose is to analyse the development of the agricultural unmanned aerial vehicle market in the Russian Federation and to identify the immediate prospects for its development in the new reality. Objectives: to study the external and internal environmental factors influencing the development of the agricultural UAV market; to identify the stages of development of the agricultural UAV market in the Russian Federation; to identify the key characteristics of the agricultural UAV market in order to determine the prospects for further development of the agricultural UAV market in the Russian Federation. Methods: systematisation, integration and analytical processing of data, method of expert evaluation, factor analysis. Scientific novelty. On the basis of the generalization of theoretical provisions and the development of methodological tools, the author proposed an approach to assessing the state of the agricultural UAV market in the Russian Federation and identifying the prospects for its development. Results. Based on the analysis of external and internal environment factors, a diagnosis of the stage of development of the Russian market (industry) in the context of the key criteria of periodization was made, the stages of development of the agricultural UAV market in the Russian Federation were determined. The identified asymmetry in the stage of market development determines most of the market characteristics that distinguish it from the global market and represent a vivid example of oligopoly. Against the background of changes in the external environment, it is concluded that the agricultural unmanned aerial vehicle market in the near future in its development will strive to overcome the stage imbalance and acquire more balanced proportions.

Yield prediction models of organic oil rose farming with agricultural unmanned aerial vehicles (UAVs) images and machine learnaing algorithms

The alternative solution for predicting yields involves leveraging the latest technological advancements in unmanned aerial vehicles (UAVs) imagery. This approach aims to empower agricultural systems operating in nature-dependent conditions, enabling them to optimize the utilization of limited resources effectively. This study aimed to provide the agricultural data management needed for the adoption of sustainable agriculture systems and the transition of precision to smart agriculture systems by combining technology. The study was carried out in the 2019–2020 vegetation period to determine the effect of the parameters on the yield in the areas where organic farming is grown in Isparta oil rose (Rosa Damascena Mill.) and to develop yield prediction models by combining them with spectral measurements. In the study, the phenological, morphological, and biochemical changes of the Isparta oil rose were examined periodically, and the effects of biotic and abiotic stress factors during the vegetation period were determined in the study and testing area. For the early yield prediction of organic Isparta oil rose was chosen thirteen parameters as soil moisture, pH, electrical conductivity (EC), soil temperature, and nine vegetation indices were derived from UAV-acquired multispectral images. The parameters were functioned in machine learning algorithms, which were multiple linear regression (MLR), multivariate adaptive regression splines (MARS), decision trees (chi-squared automatic interaction detector (CHAID), exhaustive chi-squared automatic interaction detector (ExCHAID), classification and regression tree (CART)), random forest (RF)) and artificial neural network (ANN), to develop the prediction models. Early period (day 69) for organic rose yield prediction models were determined to be useable as MARS 1 (0.907 R2), ExCHAID 1 (0.888 R2), CART 1 (0.931 R2), and RF 1 (0.909 R2).As a result of the study, the models can be used integrated with UAV and soil data for the early prediction of Isparta oil rose yield under organic farming systems, especially during the Agriculture 4.0 transition phase in our country. It is such that models be developed and used in different ecologies for sustainable agricultural systems.

Comparative evaluation of current regulations regarding agricultural unmanned aerial vehicles used for spraying purposes in Türkiye

Comparative evaluation of current regulations regarding agricultural unmanned aerial vehicles used for spraying purposes in Türkiye

Comparative evaluation of current regulations regarding agricultural unmanned aerial vehicles used for spraying purposes in Türkiye

Comparative evaluation of current regulations regarding agricultural unmanned aerial vehicles used for spraying purposes in Türkiye

Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture

Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture

LFA-YOLO: Detection of Lychee Fruit Anthracnose Based on UAV Images and Deep Learning

HighlightsMulti-path coordinated attention mechanism incorporating salient information is proposed.LeanNet, a backbone network based on depth-separable convolution, is proposed.Channel grouping multiscale fusion convolution is proposed and applied in C2f and detection head.Deployed LFA-YOLO in edge devices for real-time applications.Abstract. Anthracnose is a communicable agricultural disease with a high incidence and significant damage, posing as a major threat to litchi production. In this article, we propose a lightweight target detection method called LFA-YOLO to address the problem of leakage and misdetection in the detection of lychee fruit anthracnose due to the complexity of the agricultural environment. Our method incorporates a Multipath Coordinate Attention (MCA) mechanism into the YOLOv8 algorithm to mitigate detection errors caused by mutual occlusion between fruits. Additionally, in order to reduce the number of model parameters, we utilize the more efficient LeanNet to replace the backbone network of the baseline model. Moreover, we propose a more efficient channel grouping multi-scale fusion convolution (CGMF convolution) and apply it to the C2f module and decoupled detection head. Experimental results on the lychee fruit anthracnose (LFA) dataset demonstrate that the proposed LFA-YOLO model achieves a mAP50 of 88.61, with precision and recall rates of 90.18 and 82.77, respectively. Furthermore, the number of model parameters and floating-point operations (FLOPs) of the new method decreases to 61% and 48% of that of the baseline network, while the frames per second (FPS) improves to 90.3. Finally, we deploy the proposed LFA-YOLO on the NVIDIA Jetson Nano edge computing device to provide vision support for agricultural unmanned aerial vehicles (UAVs). Keywords: Attention mechanism, Lychee fruit anthracnose, Object detection, YOLOv8.

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

Computer vision for unmanned aerial vehicles in agriculture: applications, challenges, and opportunities

Unmanned Aerial Vehicles (UAVs), commonly called drones, have gained significant attention in agriculture due to their potential to revolutionize traditional farming practices. This paper explores the integration of computer vision techniques with UAV technology to enhance agricultural processes. The fusion of these two domains has the potential to provide farmers and agronomists with valuable insights for optimized decision-making. The primary focus of this research is to showcase the various applications of computer vision for UAVs in agriculture. The paper begins by presenting an overview of the challenges faced by modern agriculture, such as resource optimization, crop monitoring, disease detection, and yield prediction. It then delves into the technological advancements in UAVs and their suitability for addressing these challenges.

World experience in the use of unmanned aerial vehicles in agriculture

The study of unmanned aerial vehicles in agriculture is limited by regulations. Each country has its own restrictions in the form of flight altitude, maximum takeoff weight, and the required package of documents. The article discusses the main regulatory restrictions for the use of drones in various countries of the world.