Transfer Learning for Disease Classification in Paddy Crops Leveraging Nutrient Deficiency Classification Model
Keywords:
Agricultural, Disease Classification, Nutrient Deficiency, Paddy Crops, Transfer Learning.Abstract
Accurate classification of diseases in paddy crops is vital for ensuring agricultural productivity and food security. However, limited labeled data often hinders the development of robust classification models, particularly in agricultural settings. In this paper, we propose a novel approach to enhance disease classification in paddy crops by leveraging a pre-existing model initially designed for nutrient deficiency classification. Transfer learning is utilized to adapt the knowledge acquired from nutrient deficiency classification and enhance the performance of disease classification. Our method addresses the challenge of scarce labeled data by effectively transferring knowledge between related tasks. Experimental results demonstrate the efficacy of the transfer learning approach, revealing significant progress in accuracy and robustness compared to conventional methods. This research contributes to the advancement of automated disease detection systems in agriculture, fostering sustainable crop management practices and food production. By effectively leveraging models trained on related tasks, we can accelerate the development of AI tools for precision agriculture, ultimately contributing to increased crop yields, reduced resource waste, and more sustainable farming practices. The implications of this research extend beyond paddy crops, offering a blueprint for applying transfer learning to a wide range of agricultural challenges.
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