数字农科院2.0

Precise extraction of targeted apple tree canopy with YOLO-Fi model for advanced UAV spraying plans

文献类型： 外文期刊

作者： Peng Wei;Xiaojing Yan;Wentao Yan;Lina Sun;Jun Xu;Huizhu Yuan

作者机构：

关键词： Canopy analysis;Deep learning;Path planning;Prescription map;UAV;Variable targeted spraying

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2024 年 226 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： The precise analysis of individual fruit tree canopy information for accurate navigation and spraying operations of plant protection machinery is important for intelligent orchard management. However, in the complex environment of an orchard, it is quite challenging to simultaneously accomplish the detection, localization, and segmentation of tree canopies to enable precise spraying. Fortunately, advancements in high-performance unmanned aerial vehicle (UAV), sensors, and deep learning algorithms have made it possible to quickly extract and analyze tree information from complex backgrounds. In this study, we proposed a comprehensive operational framework based on UAV data and deep learning algorithms to accurately obtain apple tree information, thereby enabling variable targeted spraying. First, the Max-Relevance and Min-Redundancy (mRMR) algorithm was used to select three features (RVI, NDVI, SAVI) to create fused images to enhance tree canopies from the background environment, and the enhanced images were then utilized to generate a labeled sample dataset. Secondly, leveraging the labeled dataset, the YOLO-Fi model was developed. Using this optimal model, precise detection, localization, and segmentation of fruit trees in the experimental area were conducted. Our results showed that the YOLO-Fi model achieved optimal results (FPS = 370, mAP50-95 (B) = 0.862, mAP50-95 (M) = 0.723, MIoU = 0.749). Subsequently, based on the segmented areas of the fruit tree canopies, a variable spraying prescription map was generated, contributing to a 47.92% reduction in spraying volume compared to direct spraying. Finally, the ant colony algorithm was employed to design the shortest path for the plant protection UAV to traverse over each fruit tree within the experimental area, leading to a 2.04% reduction in distance compared to the conventional UAV flight path. This research can provide a comprehensive scheme for UAV-based precision management in orchards, encompassing tree canopy monitoring, analysis, localization, navigation, and precise spraying.

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