An Intelligent Spraying System with Deep Learning-based Semantic Segmentation of Fruit Trees in Orchards
Jeongeun Kim, Jeahwi Seol, Sukwoo Lee, Se-Woon Hong, Hyoung Il Son
Abstract
This study proposes an intelligent spraying system with semantic segmentation of fruit trees in a pear orchard. A fruit tree detection system was developed using the SegNet model, a semantic segmentation structure. The system is trained with images categorized into five distinct classes. The learned deep learning model performed with an accuracy of 83.79%. Further, we fusion depth data from an RGB-D camera to prevent the tree in the background from being detected. To operate the nozzles, each image captured from the camera is separated lengthwise into quarters and mapped to the nozzles. Then, the nozzle was opened when the area of fruit trees in each zone exceeded 20%. Two types of field experiments were performed in a pear orchard to verify the effectiveness of our system. From the results obtained, we can confirm the satisfactory performance of our deep learning-based intelligent spraying system. It is expected that the introduction of this system to actual farms will signicantly reduce the amount of pesticide used and will make the work environment safer for farmers.