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Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham's algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients (${R^2}$ R 2 ) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture. [ABSTRACT FROM AUTHOR]

BACKGROUND: Effective utilization of plant protection UAVs in peanut cultivation management necessitates a comprehensive grasp of how application volume rates and pesticides influence peanut leaf spot and rust control. This study aimed to compare the effects of application volume rates and pesticides on droplet deposition, disease, leaf retention rate and peanut yield. A T20 plant protection unmanned aerial vehicle (UAV) sprayer was used to apply four various pesticide doses. In comparison, a knapsack sprayer was used to spray with an application volume rate of 450 L ha−1. RESULTS: The results showed a significant difference in droplet deposition between the plant protection UAVs and the electric knapsack sprayer. In the pesticide treatment with an application volume rate of 15.0 L ha−1, there was no significant difference in the deposition on the peanut canopy of each pesticide treatment, but there was a significant difference in the deposition on the ground in the treatment with adding vegetable oil adjuvant. The treatment with added vegetable oil additives showed the worst performance. The treatment with an application volume rate of 22.5 L ha−1 showed the best performance, with the leaf spot control effect being only 0.3% lower than that of the electric knapsack sprayer. CONCLUSION: Plant protection UAV spraying is feasible to control peanut diseases. Considering the operational effectiveness of the plant protection UAV and application volume rate, it is recommended to use an application volume rate of 22.5 L ha−1 without adding vegetable oil adjuvants for field operations. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: Astragalus is a widely used traditional Chinese medicine material that is easily confused due to its quality, price and other factors derived from different origins. This article describes a novel method for the rapid tracing and detection of Astragalus via the joint application of an electronic tongue (ET) and an electronic eye (EE) combined with a lightweight convoluted neural network (CNN)–transformer model. First, ET and EE systems were employed to measure the taste fingerprints and appearance images, respectively, of different Astragalus samples. Three spectral transform methods – the Markov transition field, short‐time Fourier transform and recurrence plot – were utilized to convert the ET signals into 2D spectrograms. Then, the obtained ET spectrograms were fused with the EE image to obtain multimodal information. A lightweight hybrid model, termed GETNet, was designed to achieve pattern recognition for the Astragalus fusion information. The proposed model employed an improved transformer module and an improved Ghost bottleneck as its backbone network, complementarily utilizing the benefits of CNN and transformer architectures for local and global feature representation. Furthermore, the Ghost bottleneck was further optimized using a channel attention technique, which boosted the model's feature extraction effectiveness. RESULTS: The experiments indicate that the proposed data fusion strategy based on ET and EE devices has better recognition accuracy than that attained with independent sensing devices. CONCLUSION: The proposed method achieved high precision (99.1%) and recall (99.1%) values, providing a novel approach for rapidly identifying the origin of Astragalus, and it holds great promise for applications involving other types of Chinese herbal medicines. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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

BACKGROUND: Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground‐based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards. This study evaluated the spraying performance of two UAVs in the Nanguo pear orchards and compared them with a manually used backpack electric sprayer (BES). The study also analyzed the effect of canopy size on droplet deposition and ground loss, and evaluated two sampling methods, leaf sampling and telescopic rod sampling. RESULTS: Compared to BESs, droplet deposition is lower for UAVs, but the actual pesticide active ingredient deposition is not necessarily lower given the solution concentration. The droplet deposition varies among different UAVs due to structural differences. Under the same UAV operating parameters, droplet deposition on trees with smaller canopy sizes is typically greater than that on trees with larger canopy sizes, and the ground loss was also more severe. Although telescopic rod sampling is a quick and convenient method, it can only reflect the trend of droplet deposition, and the data error is greater compared with leaf sampling. CONCLUSION: UAVs can achieve better droplet deposition in mountainous Nanguo pear orchards and does almost no harm to the operators compared with the BES. However, canopy size needs to be considered to adjust the application volume rate. Telescopic rods can be used for qualitative analyses, but are not recommended for quantitative analyses. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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]

BACKGROUND: The wettability of target crop surfaces affects pesticide wetting and deposition. The structure and properties of the leaf surface of litchi leaves undergo severe changes after infestation by Aceria litchii (Keifer). The objective of this study was to systematically investigate the surface texture and wettability of litchi leaves infested. RESULTS: Firstly, the investigation focused on the surface structure and physicochemical properties of litchi leaves infested with Aceria litchii. Subsequently, different levels of Contact Angle (CA) were measured individually on the infested litchi leaves. Lastly, Surface Free Energy (SFE) and its polar and dispersive components were calculated using the Owens‐Wendt‐Rabel‐Kaelble (OWRK) method. The outcomes revealed distinctive 3D surface structures of the erineum at various stages of mycorrhizal growth. At stage NO. 1, the height of the fungus displayed a peaked appearance, with the skewness value indicating a surface characterized by more crests. In contrast, at stages NO. 2 and NO. 3, the surface appeared relatively flat. Furthermore, post‐infestation of litchi leaves, the CA of droplets on the abaxial surface of diseased leaves exhibited an increase, while the SFE value on the abaxial surface of leaves decreased significantly, in contrast to the abaxial surface of healthy leaves. CONCLUSION: The infestation behavior of Aceria litchii changed the surface structure and chemistry of litchi leaves, which directly affected the CA value of foliar liquids and the SFE value of leaves, changing the surface wettability of litchi leaves from hydrophobic to superhydrophobic. This study provides useful information for improving the wetting and deposition behavior of liquid droplets on the surface of infested leaves. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The MeanShift algorithm is a nonparametric method based on gradient ascent and it can effectively handle complex variations in lychee orchard scenes as well as changes in lychee tree canopies due to its adaptability, multi-scale analysis capabilities, and robustness, making it widely used in the segmentation processing of drone-based remote sensing images of lychee orchards. However, due to the high computational complexity of the MeanShift algorithm, its performance in processing large-scale drone remote sensing images of lychee tree canopies is not highly efficient, leading to low segmentation efficiency, which hampers its broader application. To address these issues, this study proposes high-speed MeanShift parallel segmentation algorithms for drone remote sensing images of lychee tree canopies based on MapReduce and Spark distributed computing frameworks. In this study, a cluster consisting of four nodes with Hadoop and Spark was set up, and 4000 drone remote sensing images were used as test data to evaluate the algorithm. Experimental results show that, the MeanShift algorithm based on MapReduce reduced the task execution time by 86.1% compared to the traditional MeanShift algorithm, while the MeanShift algorithm based on Spark reduced the task execution time by 88.0%, without compromising segmentation accuracy. The MeanShift parallel segmentation algorithm based on Hadoop and Spark platform can overcome the bottleneck of task execution efficiency and significantly enhance computational speed on a single machine. [ABSTRACT FROM AUTHOR]