Search

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]

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]

The study was conducted to examine modern irrigation systems aimed at improving water efficiency, reducing water consumption and increasing crop yields. The study analysed the environmental and socio-economic aspects of modern irrigation systems, including their impact on the environment and social sustainability of agriculture. The study determined that the use of modern irrigation systems, such as drip irrigation and IoT-based automatic control systems, can significantly improve the efficiency of water use in agriculture. Data analysis demonstrated that such systems can reduce water consumption by up to 50% while increasing yields by up to 30%. The use of agricultural drones to monitor field conditions and precisely regulate irrigation helps to improve plant health and reduce labour costs. The study also determined that water recycling and reuse systems can significantly reduce the use of freshwater, which is especially important in regions with limited water resources. In general, modern irrigation technologies demonstrate high efficiency and economic feasibility, contributing to the sustainable development of agriculture. The study proved that the use of computer modelling and forecasting of plant water requirements provides optimal conditions for their growth, which further contributes to an increase in yields. The results of the study can be used in practice to develop and implement more efficient and environmentally friendly technologies in irrigation and drainage systems, which helps to reduce the negative impact on the environment through more efficient use of water resources and reduction of water runoff. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Nanjing Agricultural University / Nanjuing Nongye Daxue Xuebao is the property of Journal of Nanjing Agricultural University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Copyright of Journal of Shenyang Agricultural University is the property of Journal of Shenyang Agricultural University Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

Copyright of Ciencia en Desarrollo is the property of Universidad Pedagogica y Tecnologica de Colombia, Centro de Investigaciones y Extension and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]