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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. [ABSTRACT FROM AUTHOR]

A differentiated creative search algorithm with multi-strategy improvement (MSDCS) is proposed for the path planning problem for agricultural UAVs under different complicated situations. First, the good point set and oppositional learning strategies are used to effectively improve the quality of population diversity; the adaptive fitness–distance balance reset strategy is proposed to motivate the low performers to move closer to the region near the optimal solution and find the potential optimal solution; and the vertical and horizontal crossover strategy with random dimensions is proposed to improve the computational accuracy of the algorithm and the ability to jump out of the local optimum. Second, the MSDCS is compared to different algorithms using the IEEE_CEC2017 test set, which consists of 29 test functions. The results demonstrate that the MSDCS achieves the optimal value in 23 test functions, surpassing the comparison algorithms in terms of convergence accuracy, speed, and stability by at least one order of magnitude difference, and it is ranked No. 1 in terms of comprehensive performance. Finally, the enhanced algorithm was employed to address the issue of path planning for agricultural UAVs. The experimental results demonstrate that the MSDCS outperforms comparison algorithms in path planning across various contexts. Consequently, the MSDCS can generate optimal pathways that are both rational and safe for agricultural UAV operations. [ABSTRACT FROM AUTHOR]

With the global population growth and increasing food demand, the development of precision agriculture has become particularly critical. In precision agriculture, accurately identifying areas of nitrogen stress in crops and planning precise fertilization paths are crucial. However, traditional coverage path-planning (CPP) typically considers only single-area tasks and overlooks the multi-area tasks CPP. To address this problem, this study proposed a Regional Framework for Coverage Path-Planning for Precision Fertilization (RFCPPF) for crop protection UAVs in multi-area tasks. This framework includes three modules: nitrogen stress spatial distribution extraction, multi-area tasks environmental map construction, and coverage path-planning. Firstly, Sentinel-2 remote-sensing images are processed using the Google Earth Engine (GEE) platform, and the Green Normalized Difference Vegetation Index (GNDVI) is calculated to extract the spatial distribution of nitrogen stress. A multi-area tasks environmental map is constructed to guide multiple UAV agents. Subsequently, improvements based on the Double Deep Q Network (DDQN) are introduced, incorporating Long Short-Term Memory (LSTM) and dueling network structures. Additionally, a multi-objective reward function and a state and action selection strategy suitable for stress area plant protection operations are designed. Simulation experiments verify the superiority of the proposed method in reducing redundant paths and improving coverage efficiency. The proposed improved DDQN achieved an overall step count that is 60.71% of MLP-DDQN and 90.55% of Breadth-First Search–Boustrophedon Algorithm (BFS-BA). Additionally, the total repeated coverage rate was reduced by 7.06% compared to MLP-DDQN and by 8.82% compared to BFS-BA. [ABSTRACT FROM AUTHOR]

[Objectives] In response to the impact of fragmented fields and obstacles on agricultural robot operations in hilly areas, this study aimed to propose a multi field block path planning method based on a combination of improved ant colony algorithm and A algorithm. [Methods] By using drones to construct remote sensing images of farmland, using grid method to model the environment of farmland, and then dividing and merging sub regions, a region heuristic function was introduced into the ant colony algorithm to distinguish the movement costs in different regions. The pheromone update method was improved by manually increasing the concentration of pheromones in the optimal path and establishing an adaptive pheromone volatilization coefficient, in order to solve the optimal traversal order of job sub intervals. The A algorithm with heuristic search function was used for sub region connection path planning, ultimately achieving multi field path planning. [Results] The MATLAB simulation test results showed that when the number of sub regions was 40, the average path length planned using the improved ant colony algorithm was reduced by 3.19% compared to the traditional ant colony algorithm, and the average iteration convergence number was reduced by 79.5%. In the path traversal simulation experiment, the coverage rate of the agricultural robot's traversal path could reach 100%, and the path repetition rate was 6.48%. The effectiveness of this method was further verified through simulation experiments of natural environment using remote sensing images of agricultural drones and field operation parameters. [Conclusions] The method proposed in this study could effectively solve the multi field path planning problem of agricultural robots in hilly areas, and provided path reference and technical support for large-scale operation of agricultural robots in hilly areas. [ABSTRACT FROM AUTHOR]

Introduction: As an advanced agricultural technology equipment, the functional integration design and appearance aerodynamic characteristics of hexacopter agricultural UAV are significant for improving the efficiency of agricultural production. Methods: Firstly, the study carried out the functional integration design of the hexacopter agricultural UAV, and completed the matching analysis and selection of the relevant components of the power system. Then, the numerical simulation method of computational fluid dynamics was used to analyze the aerodynamic characteristics of the UAV. Finally, the role of different propeller pitch on important parameters such as lift and range time of the UAV was verified. Results and discussion: The results showed that in the XY section of the UAV, namely, the surface parallel to the flight direction, the areas with high streamline velocityweremainly concentrated at the intersection of the airflowwith the landing gear and the motor. The streamlines in the front of the UAV were tightly and neatly distributed, while those in the rearwere sparser. Themotor force efficiencies of the UAVswith different propeller pitches ranged from7.05 to 7.25 g/w. At a flight speed of 5m/s, themotor force effect of theUAV with a pitch of 260mmreached 7.25 g/w, which was the highest value among all models. Meanwhile, it had the longest endurance time of 19.14 min, indicating the best performance. The results suggested that it not only promoted the modernization of agricultural UAVs but also effectively enhanced the efficiency of agricultural production. [ABSTRACT FROM AUTHOR]

In recent years, significant breakthroughs are observed in developing artificial intelligence (AI), which radically affects the most diverse areas of human life and activity. This review article examines the introduction of AI in agriculture using the example of China, which is a leader in the pace of introduction of AI into the national economy and seeks to head off the United States in the overall leadership in the development of AI technologies. Thanks to active work in this direction and significant financial investments in this area, China has managed to transform substantially its agricultural sector. The purpose of the article is to analyze the current trends and opportunities offered by the application of AI in the agricultural sector of the PRC economy. To this end, a series of difficulties that China faces in the development of agriculture is considered, as well as the main currently known areas of application of AI in agriculture and the types of technologies used. Information on Chinese companies using AI technologies in agriculture is summarized, including their specialization, technologies used and benefits gained. Early evidence shows that AI is being applied firstly to improve productivity and manufacturing performance, and secondly to address labor shortages and achieve manufacturing sustainability. Analysis of the situation allows us to conclude that AI can become the main driving force in the development of agriculture.

The agricultural sector of Ukraine requires fundamental changes to improve efficiency, rural development and sustainability, which is possible through the implementation of an innovative development model. The study aimed to assess the effectiveness of the introduction of one of the types of technical innovations – agricultural drones – in the agricultural production of Ukraine. The study uses the dialectical method of scientific cognition, analysis and synthesis, systematic generalisation, comparative analysis, rating method, and the mathematical method of calculation “solution optimisation” using Excel. The study analyses modern approaches to assessing the efficiency of individual processes, which helped to formulate its algorithm for calculating the efficiency of land cultivation using agricultural drones. At the first stage of the study, the technical advantages of agricultural drones were identified, which are manifested in increased labour productivity. The second stage of the study calculated the increase in the main crops (corn, wheat, sunflower, barley, soybeans, rapeseed, peas, buckwheat, and millet) that can be obtained as a result of the use of agricultural drones on a national scale. The third stage of the study yielded an indicator of the economic efficiency of introducing agricultural drones into agricultural production, as well as a forecast of the social and environmental effects. It is emphasised that state support for the Ukrainian production of agricultural drones in various forms (subsidies, preferential lending, leasing, etc.) will contribute to the development of innovative Ukrainian industry, which will positively affect the change in the sectoral structure of the national economy and significantly increase the country's gross domestic product. The following conclusions, suggestions and practical recommendations may be useful in developing relevant programmes and measures aimed at developing the agricultural sector of Ukraine

Introduction: The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current largescale rice production process. Methods: WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice. Results: The research results indicate that the training set R² and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R² and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme. Discussion: This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Given the limited awareness among farmers regarding agricultural spraying drones, the Analytic Hierarchy Process (AHP) method serves as a valuable tool to assist farmers in systematically selecting the most high-performing drone from the available options. This research employed the AHP method, utilizing Expert Choice software, to evaluate and prioritize several drone sprayers in the southern region of Kerman province, specifically Pelikan1, T16, T20, and MG-1P. Various parameters, including coverage percentage, spraying quality coefficient, spraying uniformity, device price, amount of pesticide consumption, and droplet diameter, were thoroughly examined to establish distinct priorities for each parameter. Within the AHP framework, the coverage percentage was accorded the highest weight of 0.340, while spraying uniformity received the lowest weight of 0.100. The spraying quality coefficient, cost, and amount of pesticide consumption were assigned weights of 0.222, 0.185, and 0.153, respectively. Consequently, the T16 drone sprayer emerged with the highest rank, carrying a weight of 0.277 in comparison to other drone sprayers. In contrast, Pelikan1 attained the lowest rank with a weight of 0.225. The prioritization of spraying drones based on their performance is as follows: T16, T20, MG-1P, and Pelikan1, respectively. This study provides valuable insights for farmers seeking to optimize the utilization of drone sprayers in the southern region of Kerman province. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

Timely diagnosis of trends in the development of agricultural crops is essential for precision agriculture. The use of unmanned aerial vehicles (UAV) allows us to quickly and accurately collect information about crops. The study tests the capabilities of a small aircraft in the fields of Dobruja, Bulgaria. We tested two small aircrafts with their built-in cameras and a near-infrared camera. An early diagnosis of a reduced vegetation index due to pathogens and a defect in the planter was confirmed. The obtained dependencies show the possibility of using aircraft in the agrometeorological situation of Dobruja. [ABSTRACT FROM AUTHOR]

The use of drones, also known as unmanned aerial vehicles (UAVs), has revolutionized the farming industry by providing valuable tools for various agricultural applications, including crop monitoring, spraying and treatment. A crucial aspect of agricultural drone operations is the efficient and accurate delivery of liquid payloads such as pesticides, fertilizers and herbicides. This research focuses on designing and manufacturing a lightweight tank specifically tailored for agro drones to enhance their payload delivery capabilities. The main objective of this study is to develop a tank that addresses the challenges associated with traditional tank designs, such as excessive weight and limited carrying capacity. The proposed tank utilizes lightweight materials, such as carbon fibre composites, which offer an excellent strength-to-weight ratio. Advanced manufacturing techniques are employed to maintain the tank's structural integrity while minimizing its overall weight. To ensure compatibility with agro drones, various design considerations are taken into account, including aerodynamic factors, size constraints and ease of integration with the drone's existing systems. The manufacturing process involves the use of computer-aided design (CAD) software to create a 3D model of the tank, followed by computer numerical control (CNC) machining or additive manufacturing techniques to produce the physical tank prototype. The developed tank undergoes rigorous testing to evaluate its structural integrity, payload carrying capacity and compatibility with different agro drone platforms. The lightweight tank developed in this study offers several advantages over conventional tanks, including improved flight performance, increased manoeuvrability and extended flight time for agro drones. Moreover, the use of lightweight materials reduces the energy consumption, contributing to enhanced overall operational efficiency in drone. [ABSTRACT FROM AUTHOR]

This study evaluated different treatments and application methods for pest control in corn cultivation. Variables such as the percentage of pest incidence, the level of crop infection according to the DAVIS scale, and the number of larvae at different days post-application were analyzed. The results showed that treatments with a dose of 200 cc/ha, whether applied by drone or conventionally (using the Aguilon sprayer), were the most effective in terms of pest incidence and crop infection level. At 5 days post-application, a significant decrease in the number of larvae was observed in treatments with a dose of 200 cc/ha applied by drone. However, at 7 and 10 days, a moderate increase in the number of larvae was recorded in all treatments. Additionally, it was found that drone application allowed for better product dispersion and greater coverage in the treated area. These findings emphasize the importance of considering both the dose and application method to achieve effective pest control in corn cultivation [ABSTRACT FROM AUTHOR]

The introduction of different technologies such as Geographic Information Systems, images obtained from satellites, airplanes, drones, various types of sensors as well as computer systems and tools have caused a revolution in Agriculture. The use of these technologies has always had the interest of using available resources effectively and efficiently, as well as humanizing agricultural work. This article reviews the benefits obtained with the use of drones internationally and also in Cuba. However, works that propose the use of Precision Agriculture in Cuba have also been cited. It is observed that there are still few published works that describe in detail the results obtained that allow their reproducibility and there are many that describe them qualitatively. It is considered that in the particular case of drones, the extension of their use is still very expensive since in a practical way only the GEOCUBA Enterprise has all the infrastructure and trained staff for their most complete use, so different enterprise, institutions and farmers that wish to use them must make large disbursements. [ABSTRACT FROM AUTHOR]

IN THIS study, the perceptions and attitudes of farmers towards drone use and the variables that affect their intention to use drones in two provinces in the Southeastern Anatolia region of Turkey were investigated. The main purpose of this study is to determine the hidden variables that may affect the intention to use unmanned aerial vehicles, which have been used in agricultural activities by some farmers in the region in the last few years and are expected to become widespread rapidly. For this purpose, the Technology Acceptance Model (TAM) was used as the primary reference to predict adoption intention. A proportional sample size was used to determine the number of surveys, and the survey was conducted with 249 producers. The study was conducted between September 2022 and January 2023 in Mardin and Şanlıurfa provinces located between 37°50›-41°54› east longitude and 36°40›-38°12› north latitude in the Southeastern Anatolia Region of Turkey.A structural equation model (SEM) was used for basic data analysis. As a result of the results obtained, it was determined that the perceived usefulness and perceived economic benefit had significant effects on the intention to use the drone, on the contrary, the perceived ease of use and trust attitude did not affect the intention to use. Furthermore, the possibility of reducing workload, enhancing productivity, and lowering expenses appeared to be the most significant factors influencing drone adoption intention. The results showed that spraying and fertilization activities using drones are more beneficial in terms of crop productivity and labor force reduction compared to traditional methods. As a result, it has been suggested that legal arrangements should be made to include drones in machinery and equipment support given by the state to encourage the use of drones and to expand the use of unmanned aerial vehicles in agricultural activities. [ABSTRACT FROM AUTHOR]

Tracking systems for wireless power transfer are becoming a necessity. The received target signals are sometimes weak, which is why it is critical to have a dedicated received–signal–strength indicator (RSSI) for signal detection. It can also be used for transmitter localization and automatic gain control (AGC) to ensure continuous coverage. In this paper, a logarithmic detector coupled with a high-speed full-wave rectifier is designed for an RSSI system. The aim is to place the RSSI on the receiver side of unmanned aerial vehicles used, for example, as agricultural drones. This system is intended to operate in a scenario where multiple drones in a smart farm are charged wirelessly whilst airborne. An RSSI is placed in the receiver to detect and track wireless signals and to ensure the drone is charged while in motion. The RSSI system operates at 5.8 GHz, reported for the first time, and is capable of detecting signal strengths from −60 dBm to 0 dBm with a sensitivity level of 17 mV/dBm. A logarithmic error of 0.4 dB with a dynamic range of 34 dB was achieved. The proposed RSSI system was designed using 65 nm TSMC CMOS technology, and it exhibits high sensitivity, better efficiency, and lower power consumption than those in other reported works. [ABSTRACT FROM AUTHOR]

Pest control is essential for increasing agricultural production. Agricultural drones with spraying systems for pest control have generated great interest among farmers. However, spraying systems installed on unmanned aerial vehicles, like any other sprayer, can cause damage to the environment due to drift of the agent. Air induction (AI) nozzles are known to produce less drift (e.g., larger spray drops) than other nozzles, but there is a lack of research analyzing their effectiveness in combination with drones. In this study, AI and flat fan nozzles were installed on drones to evaluate their spray and pest control performance. Aerial spraying was conducted on rice and soybeans to measure the coverage and penetration ratio and analyze the crop production as well as the crop damage due to pests and diseases. The drone flight was conducted at an altitude of 3 m and a velocity of 2 m/s. Spray droplets were collected using water-sensitive paper at two heights above the soil surface. The experiments showed that the crop coverage with the AI nozzle was 130% higher than that with the flat fan nozzle. The drift reduction of AI nozzles increased the coverage of spray droplets. But the difference in the penetration ratios, which is the ratio of agents to be delivered inside the crop, was not significant between the nozzles. Also, there was no significant difference in crop yield and pest control efficacy. Consequently, the performance of the AI nozzle did not show differences from that of the XR nozzle, except for coverage. However, the AI nozzle raised less drift, so it should be considered for use in aerial control. [ABSTRACT FROM AUTHOR]

For sustainable rice farming, IoT-based smart agricultural technologies are attracting worldwide attention. In particular, agricultural UAVs, which are expected to be utilized in various seasonal processes of rice farming, are a facility that is expected to spread in the future. In Japan, however, the smooth spread of UAVs has not progressed because of uncertainty about the effectiveness of their introduction. In this study, we clarify the effect of the introduction and its limitations by simulating the synergy effect between customary management improvement methods by farmers' integration and the efficiency improvement of agricultural work using UAVs, using a mountainous area in Japan as an example. The visualization of the expected increase in labor productivity provides a strong basis for future policies to promote smart agricultural technologies. The simulation model was built on the theoretical basis of the optimal route search based on the traveling salesman problem, in which the farmer's behavior of visiting farmland is considered a traveling salesman problem, and the effect of introducing UAVs was predicted using annual working hours as an evaluation index. The results showed that the introduction of UAVs could achieve a greater reduction in labor hours than the case where farmer integration alone was used to improve labor productivity without the introduction of UAVs. In other words, the combination of farmer integration and UAVs is expected to synergize. However, even when assuming reasonable UAVs performance, labor productivity improved by about 20%, and even when using high-performance UAVs assuming drastic technological innovation, labor productivity improved by only about 30%. Furthermore, as the number of farmlands to be managed increased, the effect of UAVs was expected to reach a ceiling. In other words, the results suggest an appropriate size of farmland to manage by introducing UAVs. This result is expected to be convincing material to promote the introduction of UAVs to farmers in the future and to make a practical contribution to their management plans. • UAVs and farmer integration can help overcome challenges posed by aging farm households in paddy farming. • Farmer integration is crucial for reducing labor time in paddy farming under aging communities. • UAVs can benefit agriculture by reducing the burden of paddy management, but not enough. • Collaboration is key to implementing practical solutions for sustainable paddy farming proposed in the study. • For sustainable rice farming in rural Japan, UAVs and farmer integration have more potential. [ABSTRACT FROM AUTHOR]

An efficient method for weed detection and the precise generation of spraying maps is crucial to optimizing weed management strategies and minimizing the costs associated with herbicide use. This study presents a method that leverages low-resolution UAV images to accurately detect and map weeds in sugarcane fields. The proposed approach integrates RGB images, vegetation indices and the HSV colour space, enhancing segmentation through histogram equalization (HE) and object-based image analysis (OBIA). The models developed in this study demonstrate exceptional performance in weed detection, with the most suitable dataset, achieving a detection capability of 95.78% for Broad-leaved Weeds (BLW) and the highest accuracy of 94.45% for Narrow-leaved Weeds (NLW). When considering multiple targets such as BLW, NLW, soil and sugarcane, the models exhibit a detection accuracy of 89.69%. Furthermore, the precision spraying maps generated by the coverage model method (CM) demonstrate remarkable accuracy, reaching 97.50% for weed control using agricultural drones. This method offers an efficient, cost-effective, and timely solution for precise weed detection, leading to improved weed control outcomes by enabling the selection of appropriate chemical substances tailored to each weed species. It reduces repetitive spraying costs and minimises chemical usage through spot spraying. • Developed a method to use low-res UAV imaging for precise weed mapping in sugarcane fields. • Applied histogram equalization to enhance OBIA's accuracy in identifying similar-looking weeds. • Optimized weed type detection using RGB, vegetation indices, and HSV in a machine learning. • Achieved 97.50% accuracy in creating weed spraying maps. • The proposed method offers cost-effectiveness, precision, and rapid implementation. [ABSTRACT FROM AUTHOR]

Tracking systems for wireless power transfer are becoming a necessity. The received target signals are sometimes weak, which is why it is critical to have a dedicated received–signal–strength indicator (RSSI) for signal detection. It can also be used for transmitter localization and automatic gain control (AGC) to ensure continuous coverage. In this paper, a logarithmic detector coupled with a high-speed full-wave rectifier is designed for an RSSI system. The aim is to place the RSSI on the receiver side of unmanned aerial vehicles used, for example, as agricultural drones. This system is intended to operate in a scenario where multiple drones in a smart farm are charged wirelessly whilst airborne. An RSSI is placed in the receiver to detect and track wireless signals and to ensure the drone is charged while in motion. The RSSI system operates at 5.8 GHz, reported for the first time, and is capable of detecting signal strengths from −60 dBm to 0 dBm with a sensitivity level of 17 mV/dBm. A logarithmic error of 0.4 dB with a dynamic range of 34 dB was achieved. The proposed RSSI system was designed using 65 nm TSMC CMOS technology, and it exhibits high sensitivity, better efficiency, and lower power consumption than those in other reported works.

Pest control is essential for increasing agricultural production. Agricultural drones with spraying systems for pest control have generated great interest among farmers. However, spraying systems installed on unmanned aerial vehicles, like any other sprayer, can cause damage to the environment due to drift of the agent. Air induction (AI) nozzles are known to produce less drift (e.g., larger spray drops) than other nozzles, but there is a lack of research analyzing their effectiveness in combination with drones. In this study, AI and flat fan nozzles were installed on drones to evaluate their spray and pest control performance. Aerial spraying was conducted on rice and soybeans to measure the coverage and penetration ratio and analyze the crop production as well as the crop damage due to pests and diseases. The drone flight was conducted at an altitude of 3 m and a velocity of 2 m/s. Spray droplets were collected using water-sensitive paper at two heights above the soil surface. The experiments showed that the crop coverage with the AI nozzle was 130% higher than that with the flat fan nozzle. The drift reduction of AI nozzles increased the coverage of spray droplets. But the difference in the penetration ratios, which is the ratio of agents to be delivered inside the crop, was not significant between the nozzles. Also, there was no significant difference in crop yield and pest control efficacy. Consequently, the performance of the AI nozzle did not show differences from that of the XR nozzle, except for coverage. However, the AI nozzle raised less drift, so it should be considered for use in aerial control.

This paper proposes an approach to implementing modern technology farming, namely agricultural drones, to help farmers enhance crop spraying efficiency, with a case study on conventional paddy fields in West Kalimantan province, Indonesia. Traditional spraying operations in paddy fields are dangerous as operators must deal with prolonged and frequent exposure to toxic chemicals. In the field experiment, an agricultural drone sprayed fertilizer and pesticides in the 1.5 ha paddy field in Parit Keladi Village, Kubu Raya Regency, West Kalimantan, Indonesia. For fertilizer and pesticide spraying, the impact on the paddy was measured periodically, involving leaves length and tiller number, randomly in the same field. Implementation in the area shows that the ground area coverage by the spraying drone was 6-7.5 m when it had an altitude is 4 m, with four nozzles and a 1.6 l/m spraying flow rate. This study implemented drone technology in the conventional paddy field, one of the famous agricultural commodities in Indonesia and other Asian countries. With this agricultural drone technology approach, we believe that semi-modernization or even modernization of conventional paddy fields can be achieved and accepted by the farmer community to increase the efficiency of farming activities and pave the way for smart farming.

• UAV Downwash flow impacts the spray structure and droplet deposition uniformity. • Drone flight altitude defines the downwash flow impact on canopy and drift potential. • Drone flight speed increases the propagation of droplet deposition. • Nozzle position under the propeller should be selected by care. • Spray issued by air induction nozzle has much higher resistance against the downwash flow impact of the propeller compare to XR type. Delivering pesticides in the agricultural field has been a venturing advancement in the past decades with the aim of target spraying. Agricultural drones have been introduced as useful assets for target spraying, however, the turbulent downwash flow generated by the drone blades accelerates the atomization process that attributes to the drift potential. In this experimental study, the effect of downwash flow on an air induction nozzle is compared to that of a conventional flat fan XR nozzle under various drone operating conditions to evaluate the best working condition. The test was carried out in an indoor facility in which the effect of environmental parameters can be minimized. The 2D patternation of the droplet deposition known as coverage ratio was evaluated using water-sensitive papers with the change of operating conditions, including nozzle type, drone speed, flying height, and blade rotational speed. As the drone flight speed, height, rotational speed, and nozzle position changed, alterations in droplet size and coverage uniformity were observed along with propagation in the drift effect. In addition, the coverage ratio of the AI nozzle was higher than that of the XR nozzle. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) sprayers are widely utilized in commercial aerial application of plant protection products (PPPs) in East Asian countries due to their high flexibility, high efficiency and low cost, but spray drift can lead to low utilization of UAV sprayers application, environmental pollution and bystander exposure risk. Droplet size and spray volume are critical factors affecting spray drift. Currently, the high temperature and humidity environment under the influence of the tropical monsoon climate brings new challenges for UAV sprayers. Therefore, in this study, pesticides were simulated with seduction red solution, and spraying trials were conducted using the DJI commercial T40 UAV sprayers for a typical tropical crop, coconut. In this study, the spray drift distribution of droplets on the ground and in the air, as well as the bystander exposure risk, were comparatively analyzed using droplet size (VF, M, and C) and spray volume (75 L/hm2 and 60 L/hm2) as trial variables. The results indicated that the spray drift characteristics of UAV sprayers were significantly affected by droplet size and spray volume. The spray drift percentage was negatively correlated with the downwind distance and the sampling height. The smaller the droplet size, the farther the buffer zone distance, up to more than 30 m, and the cumulative drift percentage is also greater, resulting in a significant risk of spray drift. The reduction in spray volume helped to reduce the spray drift, and the cumulative drift percentage was reduced by 73.87 % with a droplet size of M. The region of the body where spray drift is deposited the most on bystanders is near chest height. This study provides a reference for the rational and safe use of multirotor UAV sprayers application operations in the tropics and enriches the spray drift database in the tropics. • Field trials were designed to investigate the risk of environmental and bystander exposure from UAV sprayers. • The spray drift risk of UAV sprayers was assessed for different operational parameters (droplet size and spray volume). • Drift deposition and bystander exposure negatively correlated with downwind distance/height. • The risk of spray drift can be effectively reduced by varying the operating parameters of the UAV sprayer. [ABSTRACT FROM AUTHOR]

The objective of this work was to develop a research to find interesting solutions to the problems we face in relation to the protection of the environment, the health and welfare of animal's important solutions built using some new technologies. With the help of science, namely the use of automated sites such as UAVS and how to understand their role, we can find out what might be the advantages of using such an approach, in comparison with the classical method, in terms of efficiency. The collection of images with the help of drones, intended to evaluate the environment with improved performance, a new area of activity that provide very interesting results in the research work that can be implemented in potential applications in various technologies in the real world (surveying, construction, etc.). [ABSTRACT FROM AUTHOR]

Recently, agricultural drones have been widely used for precise agricultural control. In the case of drone control, improved coverage and reduced drift potential of chemical sprays are of major concern, and this is greatly influenced by the downwash flow characteristics formed by the rotor blades. In this study, the mean and turbulent flow characteristics of the downwash flow generated by a single blade of a four-sided agricultural control drone were quantitatively evaluated. The blade radius used to generate the downwash flow was 370 mm, and the measurement position was normalized using this length. In addition, the rotational speed of the blade was 3000 rpm, and the velocity components were dimensionlessly expressed using the blade tip velocity (V Btip) corresponding to this rotational speed. The velocity of downwash flow was measured using CTA-type hot-wire anemometry with an X-type probe, and data were collected at a sampling rate of 30 kHz for turbulence characteristics analysis. The mean velocity, vorticity distribution, turbulence intensity, and Reynolds stress of the fluctuation velocity were quantitatively measured. From extensive experimental works, it was revealed that a high axial velocity is formed at a specific position (r/L b = 0.4–0.8) along the blade length. The radial velocity shows an asymmetric distribution in the plane close to the blade, and the tangential velocity is higher in the central part of the downwash flow than in the region of a high axial and negative radial velocity. An asymmetric spiral rotational flow with low axial velocity and low-pressure distribution exists inside the downwash flow due to the shear flow, and the influence of the driving motor almost disappears at the axial position (Z/L b ∼ 1). The averaged mean value of the axial, radial, and tangential velocity components measured at six axial planes and normalized by blade tip velocity (V Btip) is composed of the ratio of the axial velocity component of 12.8%, the radial velocity component of 0.19%, and the tangential velocity component of 1.3%. The maximum values of the turbulence intensity of the axial, radial, and tangential velocity components were estimated as 6.8%, 3.0%, and 4.3%, respectively. [ABSTRACT FROM AUTHOR]