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BACKGROUND: In order to address the issues of uneven pesticide deposition and low pesticide utilization in rubber gardens caused by the traditional diffuse plant protection spraying method, this study focuses on the air-assisted powder sprayer and proposes a variable pesticide application control system. A variable pesticide application decision-making model integrating the leaf area index (LAI) was designed based on powdery mildew control standards and individual rubber tree information. According to the target powder spraying accuracy requirements, a control model of the air velocity adjustment device was established and a fuzzy proportional-integral-differential (PID) air velocity control system was developed. RESULTS: The simulation results indicate that the wind speed control system exhibits a maximum overshoot of 2.18% and an average response time of 1.48 s. The field experiment conducted in a rubber plantation revealed that when the air-assisted powder sprayer operates in the variable powder spraying mode, the average response time of the control system is 2.5 s. The control accuracy of each executive mechanism exceeded 95.9%. The deposition coefficient of variation (CV) at different canopy heights was relatively consistent, with values of 35.38%, 36.26% and 36.90%. In comparison to the quantitative mode, the variable mode showed a significant 20.03% increase in the effective utilization rate of sulfur powder. CONCLUSION: These research findings provide valuable technical support for the advancement of mechanized variable powder spraying equipment in rubber tree cultivation. © 2024 Society of Chemical Industry.
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Multi-rotor unmanned aerial vehicle (UAV) is a new chemical application tool for tall stalk tropical crop Areca catechu, which could improve deposit performance, reduce operator healthy risk, and increase spraying efficiency. In this work, a spraying experiment was carried out in two A. catechu fields with two leaf area index (LAI) values, and different operational parameters were set. Spray deposit quality, spray drift, and ground loss were studied and evaluated. The results showed that the larger the LAI of A. catechu, the lesser the coverage of the chemical deposition. The maximum coverage could reach 4.28% and the minimum 0.33%. At a flight speed of 1.5 m/s, sprayed droplets had the best penetration and worst ground loss. The overall deposition effect was poor when the flight altitudes were greater than 11.09 m and the flight speed was over 2.5 m/s. Comparing flight speed of 2.5 to 1.5 m/s, the overall distance of 90% of the total drift increased to double under the same operating parameters. This study presents reference data for UAV chemical application in A. catechu protection.
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Natural rubber is an essential raw material for industrial products and plays an important role in social development. A variety of diseases can affect the growth of rubber trees, reducing the production and quality of natural rubber. Therefore, it is of great significance to automatically identify rubber leaf disease. However, in practice, different diseases have complex morphological characteristics of spots and symptoms at different stages and scales, and there are subtle interclass differences and large intraclass variation between the symptoms of diseases. To tackle these challenges, a group multi-scale attention network (GMA-Net) was proposed for rubber leaf disease image recognition. The key idea of our method is to develop a group multi-scale dilated convolution (GMDC) module for multi-scale feature extraction as well as a cross-scale attention feature fusion (CAFF) module for multi-scale attention feature fusion. Specifically, the model uses a group convolution structure to reduce model parameters and provide multiple branches and then embeds multiple dilated convolutions to improve the model's adaptability to the scale variability of disease spots. Furthermore, the CAFF module is further designed to drive the network to learn the attentional features of multi-scale diseases and strengthen the disease features fusion at different scales. In this article, a dataset of rubber leaf diseases was constructed, including 2,788 images of four rubber leaf diseases and healthy leaves. Experimental results show that the accuracy of the model is 98.06%, which was better than other state-of-the-art approaches. Moreover, the model parameters of GMA-Net are only 0.65 M, and the model size is only 5.62 MB. Compared with MobileNetV1, V2, and ShuffleNetV1, V2 lightweight models, the model parameters and size are reduced by more than half, but the recognition accuracy is also improved by 3.86-6.1%. In addition, to verify the robustness of this model, we have also verified it on the PlantVillage public dataset. The experimental results show that the recognition accuracy of our proposed model is 99.43% on the PlantVillage dataset, which is also better than other state-of-the-art approaches. The effectiveness of the proposed method is verified, and it can be used for plant disease recognition.