A System for Automatic Rice Disease Detection from Rice Paddy Images Serviced via a Chatbot

TL;DR

This paper presents an automated LINE chatbot system utilizing deep learning to diagnose rice diseases from field images, improving accuracy and providing real-time support to farmers and specialists.

Contribution

It introduces a refined object detection model based on YOLOv3 for rice disease diagnosis, integrated into a user-friendly chatbot system for practical field use.

Findings

Detection accuracy improved from 91.1% to 95.6%.

System response time was 2-3 seconds per detection.

The system achieved an average true positive rate of 78.86%.

Abstract

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.