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A Real-Time Route Planning Method Based on DeepLabV3+ for Plant Protection UAVs

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2021 ASABE Annual International Virtual Meeting  2100018.(doi:10.13031/aim.202100018)
Authors:   Junjie Wan, Lijun Qi, Hao Zhang, Zhong’ao Lu, Jiarui Zhou
Keywords:   DeepLabV3+,MATLAB,Route planning,Semantic segmentation,Unmanned aerial vehicle

Abstract. At present, the flight path of plant protection drones is usually planned during operation, which is time-consuming, laborious, and poorly-timed, and cannot deal with sudden pests and diseases in orchards. To solve this problem, a real-time route planning method based on DeepLabV3+ for plant protection UAVs was proposed. Firstly, the DJI Phantom 3 aerial drone was used to obtain the image of the fruit tree canopy. Then, the DeepLabV3+ deep learning model is used to segment the image of the fruit tree canopy. Finally, the method of extracting navigation lines is automatically selected by judging the number of centroids of segmented binary images. The test results show that the accuracy of the algorithm for extracting navigation lines is 95%, and the average time to process a single image is about 0.55 s. The detection accuracy and real-time performance can meet the requirements of plant protection UAV operations. Besides, the method in this paper has good adaptability to the fracture of the fruit tree canopy.

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