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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]