Wheat Rust Disease Classification using Edge-AI

Abstract

Wheat leaf rust is considered one of the most detrimental fungal diseases that spread rapidly after its first appearance and can significantly damage the entire crop field. This can lead to a severe decline in wheat yield, posing a serious threat to food security considering an unceasing growth in the country's population. The conventional method of wheat rust detection is visual inspection, which is an ineffective and unsuitable approach for large agricultural lands. Additionally, such monitoring is solely dependent on the farmer's knowledge base and experience. Towards such an end, an Edge AI-based system for detecting and classifying wheat leaves into healthy and rusted leaves in real-time is proposed. The dataset collected is analyzed with several machine learning-based classifiers where Random Forest outperformed with a classification accuracy of 97.3% and 82.8% using Gray Level Co-occurrence Matrix (GLCM) and binary feature extraction techniques respectively. In addition, a Deep Convolution Neural Network (DCNN) model is explored to classify rusted and healthy leaves, which showed an accuracy of 88.33 %. This trained DCNN model is also deployed on the edge device for real-time classification of wheat rust disease. The developed system would contribute to promoting technology-based solutions over old farming practices and assist in minimizing the spread of wheat rust disease.