An Interpretable High-Accuracy Method for Rice Disease Detection Based on Multisource Data and Transfer Learning.

Abstract

With the evolution of modern agriculture and precision farming, the efficient and accurate detection of crop diseases has emerged as a pivotal research focus. In this study, an interpretative high-precision rice disease detection method, integrating multisource data and transfer learning, is introduced. This approach harnesses diverse data types, including imagery, climatic conditions, and soil attributes, facilitating enriched information extraction and enhanced detection accuracy. The incorporation of transfer learning bestows the model with robust generalization capabilities, enabling rapid adaptation to varying agricultural environments. Moreover, the interpretability of the model ensures transparency in its decision-making processes, garnering trust for real-world applications. Experimental outcomes demonstrate superior performance of the proposed method on multiple datasets when juxtaposed against advanced deep learning models and traditional machine learning techniques. Collectively, this research offers a novel perspective and toolkit for agricultural disease detection, laying a solid foundation for the future advancement of agriculture.

Code and data availability

The paper's rice disease image and sensor datasets were collected in situ and via web scraping, but no public deposit, availability statement, or author code URL is provided. The only public URL mentioned (Kaggle Global Wheat Detection) is a cited external dataset, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.