Plant IVRNet- A deep transfer Learning Model with stacked pre-trained models for plant leaf disease detection

DOI: https://doie.org/10.0109/Jbse.2025609901

Suma S H , Jayashri M Rudagi, Jagadish S Jakati

Keywords:

Leaf Classification, Neural Network, K-means clustering, Image Processing

Abstract:

To increase food production and spare farmers from expensive spraying processes, Plant leaf diseases must be identified early on. Because the afflicted and healthy portions of plant leaves are so similar, accurately and promptly identifying a number of plant leaf diseases is a challenging undertaking. Furthermore, the presence of noise and blurring on the photos, as well as variations in light, color, and brightness, add to the intricacy of the detecting process. Automatic disease detection through neural network based digital image processing tech nique is proposed in this paper. The Otsu segmentation is used for proper segmentation and the modified co-occurrence of gray levels Accurate texture feature extraction from matrices is used to create feature sets that are used to precisely diagnose illness. The proposed algorithm can detect disease correctly with 95.51% accuracy which is quite high compared with existing algorithm.

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