Zhiyan Zhou

Aiming at the technical bottleneck of monitoring rice stalk, pest, and grass damage in the middle and lower parts of rice, this paper proposes a UAV-based image information acquisition method and disease prediction algorithm model, which provides an efficient and low-cost solution for the accurate early monitoring of rice diseases, and helps improve the scientific and intelligent level of agricultural disease prevention and control. Firstly, the UAV image acquisition system was designed and equipped with an automatic telescopic rod, 360° automatic turntable, and high-definition image sensing equipment to achieve multi-angle and high-precision data acquisition in the middle and lower regions of rice plants. At the same time, a path planning algorithm and ant colony algorithm were introduced to design the flight layout path of the UAV and improve the coverage and stability of image acquisition. In terms of image information processing, this paper proposes a multi-dimensional data fusion scheme, which combines RGB, infrared, and hyperspectral data to achieve the deep fusion of information in different bands. In disease prediction, the YOLOv8 target detection algorithm and lightweight Transformer network are adopted to determine the detection performance of small targets. The experimental results showed that the average accuracy of the YOLOv8 model (mAP@0.5) in the detection of rice curl disease was 90.13%, which was much higher than that of traditional methods such as Faster R-CNN and SSD. In addition, 1496 disease images and autonomous data sets were collected to verify that the system showed good stability and practicability in field environment.

The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variations—the UAV’s visual features may degrade, especially under high-speed movement, ultimately resulting in failure of the vision tracking task and reducing the stability and robustness of swarm flight. Therefore, this paper proposes an adaptive active light source system based on light intensity matching to address the issue of visual feature loss caused by environmental light intensity and scale variations in multi-UAV collaborative navigation. The system consists of three components: an environment sensing and control module, a variable active light source module, and a light source power module. This paper first designs the overall framework of the active light source system, detailing the functions of each module and their collaborative working principles. Furthermore, optimization experiments are conducted on the variable active light source module. By comparing the recognition effects of the variable active light source module under different parameters, the best configuration is selected. In addition, to improve the robustness of the active light source system under different lighting conditions, this paper also constructs a light source color matching model based on light intensity matching. By collecting and comparing visible light images of different color light sources under various intensities and constructing the light intensity matching model using the comprehensive peak signal-to-noise ratio parameter, the model is optimized to ensure the best vision tracking performance under different lighting conditions. Finally, to validate the effectiveness of the proposed active light source system, quantitative and qualitative recognition comparison experiments were conducted in eight different scenarios with UAVs equipped with active light sources. The experimental results show that the UAV equipped with an active light source has improved the recall of yoloV7 and RT-DETR recognition algorithms by 30% and 29.6%, the mAP50 by 21% and 19.5%, and the recognition accuracy by 13.1% and 13.6, respectively. Qualitative experiments also demonstrated that the active light source effectively improved the recognition success rate under low lighting conditions. Extensive qualitative and quantitative experiments confirm that the UAV active light source system based on light intensity matching proposed in this paper effectively enhances the effectiveness and robustness of vision-based tracking for multi-UAVs, particularly in complex and variable environments. This research provides an efficient and computationally effective solution for vision-based multi-UAV systems, further enhancing the visual tracking capabilities of multi-UAVs under complex conditions.

At present, research on aerial spraying operations with UAVs mainly focuses on the deposition outcomes of droplets, with insufficient depth in the exploration of the movement process of droplet deposition. The movement characteristics of droplet deposition as the most fundamental factors affecting the effectiveness of pesticide application by UAVs are of great significance for improving droplet deposition. This study takes flat spray nozzles as the research object, uses the Particle Image Velocimetry (PIV) technique to obtain movement data of water droplet deposition under the influence of rotor flow fields, and investigates the variation characteristics of droplet deposition speed under different influencing factors. The results show that the deposition speed and the distribution area of high-speed (>12 m/s) particles increase with the increase of rotor speed, spraying pressure, and nozzle size. When the rotor speed increases from 0 r/min to 1800 r/min, the average increase in maximum droplet deposition speed for nozzle models LU120-02, LU120-03 and LU120-04 is 33.26%, 19.02%, and 7.62%, respectively. The rotor flow field significantly increases the number of high-speed droplets, making the dispersed droplet velocity distribution more concentrated. When the rotor speed is 0, 1000, 1500, and 1800 r/min, the average decay rates of droplet deposition speed are 36.72%, 20.00%, 15.47%, and 13.21%, respectively, indicating that the rotor flow field helps to reduce the decrease in droplet deposition speed, enabling droplets to deposit on the target area at a higher speed, reducing drift risk and evaporation loss. This study’s results are beneficial for revealing the mechanism of droplet deposition movement in aerial spraying by plant protection UAVs, improving the understanding of droplet movement, and providing data support and guidance for precise spraying operations.

Introduction: Monitoring crop spike growth using low-altitude remote sensing images is essential for precision agriculture, as it enables accurate crop health assessment and yield estimation. Despite the advancements in deep learning-based visual recognition, existing crop spike detection methods struggle to balance computational efficiency with accuracy in complex multi-scale environments, particularly on resource-constrained low-altitude remote sensing platforms.Methods: To address this gap, we propose FDRMNet, a novel feature diffusion reconstruction mechanism network designed to accurately detect crop spikes in challenging scenarios. The core innovation of FDRMNet lies in its multi-scale feature focus reconstruction and lightweight parameter-sharing detection head, which can effectively improve the computational efficiency of the model while enhancing the model's ability to perceive spike shape and texture.FDRMNet introduces a Multi-Scale Feature Focus Reconstruction module that integrates feature information across different scales and employs various convolutional kernels to capture global context effectively. Additionally, an Attention-Enhanced Feature Fusion Module is developed to improve the interaction between different feature map positions, leveraging adaptive average pooling and convolution operations to enhance the model's focus on critical features. To ensure suitability for low-altitude platforms with limited computational resources, we incorporate a Lightweight Parameter Sharing Detection Head, which reduces the model's parameter count by sharing weights across convolutional layers.Results: According to the evaluation experiments on the global wheat head detection dataset and diverse rice panicle detection dataset, FDRMNet outperforms other state-of-the-art methods with mAP@.5 of 94.23%, 75.13% and R² value of 0.969, 0.963 between predicted values and ground truth values. In addition, the model's frames per second and parameters in the two datasets are 227.27,288 and 6.8M, respectively, which maintains the top three position among all the compared algorithms.Discussion: Extensive qualitative and quantitative experiments demonstrate that FDRMNet significantly outperforms existing methods in spike detection and counting tasks, achieving higher detection accuracy with lower computational complexity.The results underscore the model's superior practicality and generalization capability in real-world applications. This research contributes a highly efficient and computationally effective solution for crop spike detection, offering substantial benefits to precision agriculture practices.

Fungal spores that cause infectious fungal diseases in rice are mainly transmitted through air. The existing fixed, portable or vehicle-mounted fungal spore collection devices used for rice infectious diseases have several disadvantages, such as low efficiency, large volume, low precision and incomplete information. In this study, a mobile fungal spore collection device is designed, consisting of six filters called “Capture-A”, which can collect spores and other airborne particles onto a filter located on a rotating disc of six filters that can be rotated to a position allowing for the capture of six individual samples. They are captured one at a time and designed and validated by capturing spores above the rice field, and the parameters of the key components of the collector are optimized through fluid simulation and verification experiments. The parameter combination of the “Capturer-A” in the best working state is as follows: sampling vessel filter screen with aperture size of 0.150 mm, bent air duct with inner diameter of 20 mm, negative pressure fan with 1500 Pa and spore sampling of cylindrical shape. In the field test, the self-developed “Capturer-A” was compared with the existing “YFBZ3” (mobile spore collection device made by Yunfei Co., Ltd., Zhengzhou, China). The two devices were experimented on at 15 sampling points in three diseased rice fields, and the samples were examined and counted under a microscope in the laboratory. It was found that the spores of rice blast disease and rice flax spot disease of rice were contained in the samples; the number of samples collected by a single sampling vessel of “Capturer-A” was about twice that of the device “YFBZ3”in the test.

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.

Unmanned aerial vehicles (UAVs) are an important part of smart farms and have been widely used in granular fertilizer spreading. The multichannel pneumatic granular fertilizer spreader (MPGFS) has the advantages of light weight and precision spreading, and has been applied to UAV variable rate fertilization. Based on the problem that the airflow field disorder of the existing MPGFS reduces the uniformity of spreading, the aim of this study was to further improve the performance of the MPGFS through rectification. The computational fluid dynamics and discrete element method (CFD-DEM) and coupling simulation method were used to study the characteristics of the airflow field and fertilizer particle motion, and a honeycomb rectifier and grid rectifier were developed. The aperture of the honeycomb rectifier and the grid size of the grid rectifier were optimized. Then, the test bench was built to test the consistency of the discharge rate of each channel and the spreading uniformity of the MPGFS. The simulation results of the existing MPGFS showed that the airflow provided by the axial flow fan was rotational, and this caused the particles’ motion to be skewed in the shrinkage section, so the discharge rate of each channel was inconsistent. The airflow field analysis results of the shrinkage section showed that the airflow rotation was reduced after the rectification of the honeycomb rectifier and the grid rectifier. The bench test results showed that the coefficient of variation (CV) of each channel discharge rate of the existing MPGFS was 20.16%, the optimal honeycomb rectifier was 13.07%, and the optimal grid rectifier was 5.27%. The bench test results of spreading uniformity show that the CV of spreading uniformity of the existing MPGFS was 15.32%, the optimal honeycomb rectifier was 15.81%, and the optimal grid rectifier was 8.02%. The grid rectifier spread pattern was more reasonable and the CV of uniformity was better. This study demonstrated that the use of a grid rectifier to rectify the airflow field of MPGFS can effectively improve its spreading uniformity, which was of guiding significance for the design and research of MPGFS.