Context-Aware AutoML for Accurate Wheat Disease Detection

Timely detection and management of crop diseases are crucial for food security and agricultural productivity. Traditional methods, which rely on manual inspection, are often slow and prone to human error. With the rise of diseases like stripe rust in wheat, there is a growing need for efficient automated detection methods. This paper proposes a novel classification strategy that leverages Automated Machine Learning (AutoML) in combination with advanced feature engineering techniques. We develop a scalable framework that detects stripe rust by extracting comprehensive statistical features from images, distinguishing disease symptoms from healthy crops. To enhance feature quality, we employ Context-Aware Automated Feature Engineering, which iteratively generates meaningful features to capture subtle patterns in the data. Our method achieves 95.35% accuracy on the RustNet dataset, significantly outperforming the state-of-the-art ResNet-18 model, which achieved 85.2% accuracy. These findings highlight the potential of AutoML and automated feature engineering to revolutionize disease detection in agriculture, offering a cost-effective alternative to traditional deep learning methods that require extensive computational resources and expertise.