Abstract
A rice disease diagnosis LINE Bot System from paddy field images was presented in this paper. An easy-to-use automatic rice disease diagnosis system was necessary to help rice farmers improve yield and quality. We targeted on the images took from the paddy environment without special sample preparation. We used a deep learning neural networks technique to detect rice disease in the images. We purposed object detection model training and refinement process to improve the performance of our previous rice leaf diseases detection research. The process was based on analyzing the model's predictive results and could be repeatedly used to improve the quality of the database in the next training of the model. The deployment model for our LINE Bot system was created from the selected best performance technique in our previous paper, YOLOv3, trained by refined training data set. The performance of deployment model was measured on 5 target classes by average mAP improved from 82.74% in previous paper to 89.10%. We purposed Rice Disease LINE Bot system used this deployment model. Our system worked automatically real-time to suggest primary rice disease diagnosis results to the users in the LINE group. Our group included of rice farmers and rice disease experts, and they could communicate freely via chat. In the real LINE Bot deployment, the model's performance measured by our own defined measurement Average True Positive Point was 78.86%. It took approximately 2-3 seconds for detection process in our system servers.
Abstract (translated)
URL
https://arxiv.org/abs/2011.10823
