Federated Learning for Heterogeneous Multi-Site Crop Disease Diagnosis

Wesley Chorney, Abdur Rahman, Yibin Wang, Haifeng Wang () and Zhaohua Peng ()
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Wesley Chorney: Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Abdur Rahman: Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Yibin Wang: Department of Biological and Agricultural Engineering, Texas A&M AgriLife Research, Texas A&M University System, Dallas, TX 75252, USA
Haifeng Wang: Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS 39762, USA
Zhaohua Peng: Department of Biochemistry, Molecular Biology, Entomology and Plant Pathology, Mississippi State University, Mississippi State, MS 39762, USA

Mathematics, 2025, vol. 13, issue 9, 1-19

Abstract: Crop diseases can significantly impact crop growth and production, often leading to a severe economic burden for rice farmers. These diseases can spread rapidly over large areas, making it challenging for farmers to detect and manage them effectively and promptly. Automated methods for disease classification emerge as promising approaches for detecting and managing these diseases, provided there are sufficient data. Sharing data among farms could facilitate the development of a strong classifier, but it must be executed properly to prevent leaking sensitive information. In this study, we demonstrate how farms with vastly different datasets can collaborate through a federated learning model. The objective of this collaboration is to create a classifier that every farm can use to detect and manage rice crop diseases by leveraging data sharing while safeguarding data privacy. We underscore the significance of data sharing and model architecture in developing a robust centralized classifier, which can effectively classify multiple diseases (and a healthy state) with 83.24% accuracy, 84.24% precision, 83.24% recall, and an 82.28% F1 score. In addition, we demonstrate the importance of model design on classification outcomes. The proposed collaborative learning method not only preserves data privacy but also offers a cost-effective and communication-efficient lightweight solution for rice crop disease detection. Furthermore, this collaborative strategy can be extended to other crop disease classification tasks.

Keywords: rice; crop disease; federated learning; privacy; data sharing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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