Transfer learning Model for Plant Disease Detection

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Swati Arya, Ashwani


The significant task in image processing is to detect the diseases in plants as the data utilized for input is complex in nature. The infected plants are diagnosed in diverse phases. For this purpose, various algorithms are available. The prior work presented SVM (Support Vector Machine) algorithm in order to detect the disorder. This research work introduces a transfer learning system for enhancing the diverse metrics such as accuracy, precision and recall obtained from the earlier work. The Python is applied to deploy the introduced and traditional technique. Some parameters are considered for analyzing the outcomes. The results of analysis depicted that the introduced approach outperforms the traditional approach concerning accuracy, precision and recall.

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