Deep Learning based Tomato Disease Detection and Remedy Suggestions using Mobile Application

TL;DR

This paper presents a mobile AI system using YOLOv5 for detecting tomato diseases and providing remedy suggestions, aimed at assisting farmers with limited access to agricultural expertise in Nepal.

Contribution

It introduces a novel mobile application integrating YOLOv5 for tomato disease detection and remedy guidance, tailored for local farmers with minimal technical skills.

Findings

Achieved mean average precision of 0.76 in disease detection

Developed a user-friendly app for local language interaction

Curated a dataset of ten tomato disease classes

Abstract

We have developed a comprehensive computer system to assist farmers who practice traditional farming methods and have limited access to agricultural experts for addressing crop diseases. Our system utilizes artificial intelligence (AI) to identify and provide remedies for vegetable diseases. To ensure ease of use, we have created a mobile application that offers a user-friendly interface, allowing farmers to inquire about vegetable diseases and receive suitable solutions in their local language. The developed system can be utilized by any farmer with a basic understanding of a smartphone. Specifically, we have designed an AI-enabled mobile application for identifying and suggesting remedies for vegetable diseases, focusing on tomato diseases to benefit the local farming community in Nepal. Our system employs state-of-the-art object detection methodology, namely You Only Look Once (YOLO), to detect tomato diseases. The detected information is then relayed to the mobile application, which provides remedy suggestions guided by domain experts. In order to train our system effectively, we curated a dataset consisting of ten classes of tomato diseases. We utilized various data augmentation methods to address overfitting and trained a YOLOv5 object detector. The proposed method achieved a mean average precision of 0.76 and offers an efficient mobile interface for interacting with the AI system. While our system is currently in the development phase, we are actively working towards enhancing its robustness and real-time usability by accumulating more training samples.