A mobile-based system for maize plant leaf disease detection and classification using deep learning

Abstract

Artificial Intelligence has been used for many applications such as medical, communication, object detection, and object tracking. Maize crop, which is the major crop in the world, is affected by several types of diseases which lower its yield and affect the quality. This paper focuses on this issue and provides an application for the detection and classification of diseases in maize crop using deep learning models. In addition to this, the developed application also returns the segmented images of affected leaves and thus enables us to track the disease spots on each leaf. For this purpose, a dataset of three maize crop diseases named Blight, Sugarcane Mosaic virus, and Leaf Spot is collected from the University Research Farm Koont, PMAS-AAUR at different growth stages on contrasting weather conditions. This data was used for training different prediction models including YOLOv3-tiny, YOLOv4, YOLOv5s, YOLOv7s, and YOLOv8n and the reported prediction accuracy was 69.40%, 97.50%, 88.23%, 93.30%, and 99.04% respectively. Results demonstrate that the prediction accuracy of the YOLOv8n model is higher than the other applied models. This model has shown excellent results while localizing the affected area of the leaf accurately with a higher confidence score. YOLOv8n is the latest model used for the detection of diseases as compared to the other approaches in the available literature. Also, worked on sugarcane mosaic virus using deep learning models has also been reported for the first time. Further, the models with high accuracy have been embedded in a mobile application to provide a real-time disease detection facility for end users within a few seconds.

Plant phenotyping relevance

トウモロコシ葉の病害領域を画像から検出・分類し、病斑をセグメンテーションして追跡する深層学習・モバイル手法が研究の中心であり、植物病害状態の表現型推定に該当する。

Code and data availability

The paper's maize disease image dataset (2,675 field images) and trained YOLO models are paper-specific assets, but no public repository, deposit, or authors' URL is provided; the data availability statement only promises availability upon request.

No evidence-backed public reproduction asset is currently recorded.