Monitoring of diseased banana plants based on collaborative RGB images from air-ground multi-view

【Objective】The precise monitoring method of diseased banana plants was explored by air-ground RGB image, to provide technique support for building intelligent system for efficient and accurate monitoring of diseased banana for large-scale planting areas. 【Method】The air-ground multi-view diseased banana plants RGB image dataset were established by unmanned aerial vehicle and ground mobile devices. A lightweight deep learning model for detecting the number of diseased banana plants in a large-scale planting area has been developed by combining attention mechanism and deep separable convolution strategy. The actual number of diseased banana plants and the incidence rate information were calculated by the multi-view target calibration method. 【Result】The performance metrics of the improved model were compared and found to perform better than the optimal base model in the commonly used YOLOv3,YOLOv4 and YOLOv5.In the experiments of target detection of diseased banana plants with multi-view, the mean average precision(mAP) of the model reached 94.95%. Based on the improved model, the monitoring of diseased banana plants in large planting areas was completed, and it was found that most of the diseased banana plants were located in the area or boundary of the bald spots formed by the disease, which indicated that the diseased banana plants were infectious. In addition, disease information statistics were completed for Zhongjiao No.4 in the slow and nutritive growth stages in the standardized smallscale experimental plots. The error of banana incidence rate from the model detection was less than 0.31%, the number of manual inspections and proofreading was greatly reduced by 82.98% to 100.00%, and it was found that this banana variety also showed typical perennial crop growth characteristics. 【Suggestion】In order to solve the problems of difficult accurate monitoring of diseased banana plants in large planting areas and insufficient generalization ability of detection models, it is recommended to extract banana crop disease information by lightweight diseased banana plant monitoring method based on collaborative RGB images from air-ground multi-view; multi-party collaboration was recommended to explore banana planting models suitable for the development of digital agriculture; extend the development of the industrial chain, improve the comprehensive benefits of banana planting, and promote the development of regional advantageous industries.