Search

With the continuous increase of the spraying area, the problem of droplet drift risk in the spraying process of UAV is becoming increasingly prominent, especially the herbicide drift. In order to clarify the effect of the herbicide solution on the droplet size and the deposition and drift distribution characteristics sprayed by UAVs, the droplet sizes of 15 herbicide solutions sprayed by the centrifugal rotary atomizer nozzle installed in the plant protection UAV were measured in the laboratory, and the distribution of droplet deposition and drift in the spraying area and drift area were measured by adding a fluorescent tracer (60 g/hm2) to the tank in the field. The results showed that the herbicide solution had a significant effect on the droplet size distribution. The DV50 of all the other solutions was reduced after sprayed by the centrifugal atomizer except the Carfentrazone-ethyl water dispersible granule, and the maximum decrease ratio was 22.0%. The proportion of small droplets (V

Obtaining consistent multi-view images of litchis is crucial for various litchi-related studies, such as data augmentation and 3D reconstruction. This paper proposes a two-stage model that integrates the Mask2Former semantic segmentation network with the Wonder3D multi-view generation network. This integration aims to accurately segment and extract litchis from complex backgrounds and generate consistent multi-view images of previously unseen litchis. In the first stage, the Mask2Former model is utilized to predict litchi masks, enabling the extraction of litchis from complex backgrounds. To further enhance the accuracy of litchi branch extraction, we propose a novel method that combines the predicted masks with morphological operations and the HSV color space. This approach ensures accurate extraction of litchi branches even when the semantic segmentation model's prediction accuracy is not high. In the second stage, the segmented and extracted litchi images are passed as input into the Wonder3D network to generate multi-view of the litchis. After comparing different semantic segmentation and multi-view synthesis networks, the Mask2Former and Wonder3D networks demonstrated the best performance. The Mask2Former network achieved a mean Intersection over Union (mIoU) of 79.79% and a mean pixel accuracy (mPA) of 85.82%. The Wonder3D network achieved a peak signal-to-noise ratio (PSNR) of 18.89 dB, a structural similarity index (SSIM) of 0.8199, and a learned perceptual image patch similarity (LPIPS) of 0.114. Combining the Mask2Former model with the Wonder3D network resulted in an increase in PSNR and SSIM scores by 0.21 dB and 0.0121, respectively, and a decrease in LPIPS by 0.064 compared to using the Wonder3D model alone. Therefore, the proposed two-stage model effectively achieves automatic extraction and multi-view generation of litchis with high accuracy. [ABSTRACT FROM AUTHOR]

Path planning is the core problem of orchard fertilization robots during their operation. The traditional full-coverage job path planning algorithm has problems, such as being not smooth enough and having a large curvature fluctuation, that lead to unsteady running and low working efficiency of robot trajectory tracking. To solve the above problems, an improved A* path planning algorithm based on a multi-constraint Bessel curve is proposed. First, by improving the traditional A* algorithm, the orchard operation path can be fully covered by adding guide points. Second, according to the differential vehicle kinematics model of the orchard fertilization robot, the robot kinematics constraint is combined with a Bessel curve to smooth the turning path of the A* algorithm, and the global path meeting the driving requirements of the orchard fertilization robot is generated by comprehensively considering multiple constraints such as the minimum turning radius and continuous curvature. Finally, the pure tracking algorithm is used to carry out tracking experiments to verify the robot's driving accuracy. The simulation and experimental results show that the maximum curvature of the planned trajectory is 0.67, which meets the autonomous operation requirements of the orchard fertilization robot. When tracking the linear path in the fertilization area, the average transverse deviation is 0.0157 m, and the maximum transverse deviation is 0.0457 m. When tracking the U-turn path, the average absolute transverse deviation is 0.1081 m, and the maximum transverse deviation is 0.1768 m, which meets the autonomous operation requirements of orchard fertilization robots. [ABSTRACT FROM AUTHOR]

This study introduces a novel methodology for the dynamic extraction of information on cotton growth in terms of height utilizing the DJI Zenmuse L1 LiDAR sensor mounted onto a DJI Matrice 300 RTK Unmanned Aerial Vehicle (UAV), aimed at enhancing the precision and efficiency of growth monitoring within the realm of precision agriculture. Employing the Progressive TIN Densification (PTD) and Cloth Simulation Filter (CSF) algorithms, combined with Kriging interpolation, we generated Canopy Height Models (CHMs) to extract the cotton heights at two key agricultural sites: Zengcheng and Tumxuk. Our analysis reveals that the PTD algorithm significantly outperforms the CSF method in terms of accuracy, with its R2 values indicating a superior model fit for height extraction across different growth stages (Zengcheng: 0.71, Tumxuk: 0.82). Through meticulous data processing and cluster analysis, this study not only identifies the most effective algorithm for accurate height extraction but also provides detailed insights into the dynamic growth patterns of cotton varieties across different geographical regions. The findings highlight the critical role of UAV remote sensing in enabling large-scale, high-precision monitoring of crop growth, which is essential for the optimization of agricultural practices such as precision fertilization and irrigation. Furthermore, the study demonstrates the potential of UAV technology to select superior cotton varieties by analyzing their growth dynamics, offering valuable guidance for cotton breeding and cultivation. [ABSTRACT FROM AUTHOR]

Rapid acquisition and analysis of crop information is the precondition and basis for carrying out precision agricultural practice. Variable spraying and agricultural operation management based on the actual degree of crop diseases, pests and weeds can reduce the cost of agricultural production, optimize crop cultivation, improve crop yield and quality, and thus achieve precise agricultural management. In recent years, with the rapid development of UAV industry, UAV agricultural remote sensing technologies have played an important role in monitoring crop diseases, insects and weeds because of high spatial resolution, strong timeliness and low cost. Firstly, this research introduces the basic idea and system composition of precision agricultural aviation, and the status of UAV remote sensing in precision agricultural aviation. Then, the common UAV remote sensing imaging and interpreting methods were discussed, and the progress of UAV agricultural remote sensing technologies in detecting crop diseases, pests and weeds were respectively expounded. Finally, the challenges in the development of UAV agricultural remote sensing technologies nowadays were summarized, and the future development directions of UAV agricultural remote sensing were prospected. This research can provide theoretical references and technical supports for the development of UAV remote sensing technology in the field of precision agricultural aviation.

The agriculture sector plays a very important role in the increasing population, year by year, to fulfill requirements and contributes significantly to the economy of the country. One of the main challenges in agriculture is the prevention and early detection of pest attack on crops. Farmers spend a significant amount of time and money in detecting pests and diseases, often by looking at plant leaves and analyzing the presence of diseases and pests. Late detection of pest attacks and improper use of pesticide application can cause damage to plants and compromise food quality. This problem can be solved through artificial intelligence, machine learning, and accurate image classification systems. In recent years, machine learning has made improvements in image recognition and classification. Hence, in this research article, we used convolutional neural network (CNN)-based models, such as the Cov2D library and VGG-16, to identify pest attacks. Our experiments involved a personal dataset consisting of 7000 images of pest-attacked leaf samples of different positions on maize plants, categorized into two classes. The Google Colab environment was used for experimentation and implementation, specially designed for cloud computing and machine learning. [ABSTRACT FROM AUTHOR]

The LAI (leaf area index) is an important parameter describing the canopy structure of citrus trees and characterizing plant photosynthesis, as well as providing an important basis for selecting parameters for orchard plant protection operations. By fusing LiDAR data with multispectral data, it can make up for the lack of rich spatial features of multispectral data, thus obtaining higher LAI inversion accuracy. This study proposed a multiscale LAI inversion method for citrus orchard based on the fusion of point cloud data and multispectral data. By comparing various machine learning algorithms, the mapping relationship between the characteristic parameters in multispectral data and point cloud data and citrus LAI was established, and we established the inversion model based on this, by removing redundant features through redundancy analysis. The experiment results showed that the BP neural network performs the best at both the community scale and the individual scale. After removing redundant features, the R2, RMSE, and MAE of the BP neural network at the community scale and individual scale were 0.896, 0.112, 0.086, and 0.794, 0.408, 0.328, respectively. By adding the three-dimensional gap fraction feature to the two-dimensional vegetation index features, the R2 at community scale and individual scale increased by 4.43% and 7.29%, respectively. The conclusion of this study suggests that the fusion of point cloud and multispectral data exhibits superior accuracy in multiscale citrus LAI inversion compared to relying solely on a single data source. This study proposes a fast and efficient multiscale LAI inversion method for citrus, which provides a new idea for the orchard precise management and the precision of plant protection operation. [ABSTRACT FROM AUTHOR]

Data processing of low-altitude remote sensing visible images from UAVs is one of the hot research topics in precision agriculture aviation. In order to solve the problems of large model size with slow detection speed that lead to the inability to process images in real time, this paper proposes a lightweight target detector CURI-YOLOv7 based on YOLOv7tiny which is suitable for individual citrus tree detection from UAV remote sensing imagery. This paper augmented the dataset with morphological changes and Mosica with Mixup. A backbone based on depthwise separable convolution and the MobileOne-block module was designed to replace the backbone of YOLOv7tiny. SPPF (spatial pyramid pooling fast) was used to replace the original spatial pyramid pooling structure. Additionally, we redesigned the neck by adding GSConv and depth-separable convolution and deleted its input layer from the backbone with a size of (80, 80) and its output layer from the head with a size of (80, 80). A new ELAN structure was designed, and the redundant convolutional layers were deleted. The experimental results show that the GFLOPs = 1.976, the parameters = 1.018 M, the weights = 3.98 MB, and the mAP = 90.34% for CURI-YOLOv7 in the UAV remote sensing imagery of the citrus trees dataset. The detection speed of a single image is 128.83 on computer and 27.01 on embedded devices. Therefore, the CURI-YOLOv7 model can basically achieve the function of individual tree detection in UAV remote sensing imagery on embedded devices. This forms a foundation for the subsequent UAV real-time identification of the citrus tree with its geographic coordinates positioning, which is conducive to the study of precise agricultural management of citrus orchards. [ABSTRACT FROM AUTHOR]

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters. [ABSTRACT FROM AUTHOR]