Agriculture is the main source of wealth, and its contribution is essential to humans. However, several obstacles faced by the farmers are due to different kinds of plant diseases. The determination and anticipation of plant diseases are the major concerns and should be considered for maximizing productivity. This project proposes an effective image processing method for plant disease classification. In this research, the input image is subjected to the pre-processing phase for removing the noise and artifacts present in the image. After pre-processed image goes to feature extraction phase in which the texture features are extracted. The obtained texture features are subjected to the classification phase, which uses Auto- Metric Graph Neural Network. Here, the proposed training using Auto- Metric Graph Neural Network and designed integrating the Nomadic People Optimization Algorithm(NPO).The experimental results proved and outperformed other existing methods with maximal accuracy of 0.877, sensitivity of 0.862, and the specificity of 0.877 respectively.
Plant Disease Dataset
Classified Image
# Agriculture, # wealth, # contribution, # farmers, # plant diseases, # determination, # anticipation, # concerns, # productivity, # project, # image, # pre-processing, # noise, # feature, # texture, # extraction, #Clusters, # Metric, # Graph, # Neural, # Network, # proposed , # Nomadic, # Optimization, # Algorithm, # accuracy, # sensitivity, # obstacles, # classification, # specificity
[1]. Li, L., Zhang, S. and Wang, B., 2021. Plant disease detection and classification by deep learning—a review. IEEE Access, 9, pp.56683-56698. [2]. Shrivastava, V.K. and Pradhan, M.K., 2021. Rice plant disease classification using color features: a machine learning paradigm. Journal of Plant Pathology, 103(1), pp.17-26.