Mathematical Optimization of Deep Learning Models for Blight Disease Classification: A Multitask Approach

This study proposes a machine learning framework for analyzing blight diseases in rice, cassava, sugarcane, and corn, which are the primary economic crops of Thailand. To better reflect real‐world conditions, the framework performs two related tasks within a single model: crop classification and ordinal disease‐severity classification. Hamming loss and subset accuracy are employed as key evaluation metrics for multilabel prediction performance. The framework is designed to improve diagnostic accuracy and support practical applications in precision agriculture, contributing to more effective disease monitoring and management.