  A LINE Bot System to diagnose rice diseases from actual paddy field images
was developed and presented in this paper. It was easy-to-use and automatic
system designed to help rice farmers improve the rice yield and quality. The
targeted images were taken from the actual paddy environment without special
sample preparation. We used a deep learning neural networks technique to detect
rice diseases from the images. We developed an object detection model training
and refinement process to improve the performance of our previous research on
rice leave diseases detection. 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 the deployment model was measured on 5 target classes and found that the
Average True Positive Point improved from 91.1% in the previous paper to 95.6%
in this study. Therefore, we used this deployment model for Rice Disease LINE
Bot system. Our system worked automatically real-time to suggest primary
diagnosis results to the users in the LINE group, which included rice farmers
and rice disease specialists. They could communicate freely via chat. In the
real LINE Bot deployment, the model's performance was measured by our own
defined measurement Average True Positive Point and was found to be an average
of 78.86%. The system was fast and took only 2-3 s for detection process in our
system server.
