Wheat Leaf Disease Detection and Classification Model Using Transfer Learning Approach

Abstract

Wheat is a significant cereal crop for developing countries like Ethiopia. However, low productivity persists due to factors such as weather conditions, diseases, and pests. Traditional techniques of detecting wheat disease are laborious, consuming time, and require solid knowledge of the fields. Therefore, many studies have attempted to develop detection and classification models. Most of these studies are limited to a few diseases, used a handcrafted feature extraction method, and took no remedy action for the identified diseases. Therefore, the study’s aimed to construct an effective model for wheat leaf diseases detection and classification by employing a transfer learning approach. To achieve this, a total of 2,316 images of wheat leaf diseases, including five different types of diseases like leaf rust, Septoria tritic blotchi, strip rust, powdery mildow, and tanspot, are collected. Image preprocessing techniques, including resizing, normalization, noise removal, and augmentation, are applied. A transfer learning based pretrained models’ is utilized for extracting features and classifying the image. Four pre-trained models’ extreme inception (Xception), Visual geometry group (Vgg16, Vgg19), and Residual Network (ResNet50) are compared to determine best efficient model that detects and classifies the diseases. Confusion matrices and classification reports are used for evaluating effectiveness of the model. The suggested model attained an impressive accuracy of 99.70% through fine-tuning and outperformed other models. Additionally, a user-friendly web application is developed to assist experts and farmers in detecting wheat leaf diseases and suggesting potential remedies to control the diseases.