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A Unmanned Aerial Vehicle-Based Image Information Acquisition Technique for the Middle and Lower Sections of Rice Plants and a Predictive Algorithm Model for Pest and Disease Detection

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Publicado en:Agriculture vol. 15, no. 7 (2025), p. 790
Autor principal: Guo, Xiaoyan
Otros Autores: Ou, Yuanzhen, Deng, Konghong, Fan, Xiaolong, Gao, Ruitao, Zhou, Zhiyan
Publicado:
MDPI AG
Materias:
Data acquisition
Accuracy
Data processing
Deep learning
Algorithms
Disease
Disease control
Artificial neural networks
Optimization
Medical imaging
Corn
Agricultural land
Unmanned aerial vehicles
Data integration
Yeast
Monitoring
Ant colony optimization
Disease detection
Monitoring systems
Efficiency
Cameras
Pests
Target detection
Rice
Design
Multidimensional data
Image acquisition
Information processing
Stability
Path planning
High definition
Environmental
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Resumen: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.
ISSN:2077-0472
DOI:10.3390/agriculture15070790
Fuente:Agriculture Science Database