Abstract
Traditional disease retrieval and localization for plant leaves typically demand substantial human resources and time. In this study, an intelligent approach utilizing deep hash convolutional neural networks (DHCNN) is presented to address these challenges and enhance retrieval performance. By integrating a collision-resistant hashing technique, this method demonstrates an improved ability to distinguish highly similar disease features, achieving over 98.4% in both precision and true positive rate (TPR) for single-plant disease retrieval on crops like apple, corn and tomato. For multi-plant disease retrieval, the approach further achieves impressive Precision of 99.5%, TPR of 99.6% and F-score of 99.58% on the augmented PlantVillage dataset, confirming its robustness in handling diverse plant diseases. This method ensures precise disease retrieval in demanding conditions, whether for single or multiple plant scenarios.