PLANT DISEASE DETECTION USING MACHINE LEARNING TECHNIQUES

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Nihal , Arvind Kumar

Vol 18, No 5 ( 2023 )   |  Licensing: CC 4.0   |   Pg no: 36-44   |   Published on: 05-05-2023

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Abstract
In this study, we developed a classification model for plant disease detection using SOTA CNN architecture, specifically the VGG-16 architecture. We also used data and image augmentation techniques to improve the performance of the model. Compared with previous studies using the different SOTA CNN architecture for plant disease detection, we optimized the hyperparameters of the model to obtain a more accurate prediction. The results show that our model performs better prediction than previous studies and demonstrates the efficiency of transfer learning, data and image augmentation holders and hyperparameter optimization in improving the performance of deep learning models for plant disease diagnosis.

Keywords:
deep learning,transfer Learning,image augmentation ,classification

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