Deep Learning based Tomato Disease Detection and Remedy Suggestions
  using Mobile Application
Yagya Raj Pandeya Samin Karki Ishan Dangol Nitesh Rajbanshi
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.