Exploring OMFA-CNN for Potato Leaf Disease Identification: An Assessment against Existing Models
Keywords:
DL (Deep Learning), GLCM (grey level co-occurrence matrix), OMFA-CNN (optimized matrix feature analysis-convolutional neural network), Potato leaf disease detection, PlantVillage dataset, PCA (principle component analysis)Abstract
Nowadays, cultivation acts as a most important regions for the survival of humans. Imaging and adapting technology are very important for the area of agriculture and consequently advantageous to the farmer and the user. Due to imaging, adapting technology and systematic monitoring it is much possible to verify the disease at the very initial phase and that can be avoided to achieve increased harvest production. This research work was implemented for the classification of Potato Leave (PL) diseases. For this case, the publicly accessible dataset, famously known as “Plant Village” dataset, was considered. For the process of IS (image segmentation), k-means were measured, for the combination of FE (feature extraction) purposes, the GLCM and PCA concepts were used, and for the detection and classification the research methodology,OMFA-CNN, was used. The PL disease dataset comprises images acquired in real-time and from the famous Kaggle (PlantVillage) dataset. The research methodology can obtain a precision of 99.3%, recall of 99%, and MSE of 4.0 was used. The developed technique is also compared with existing techniques like Mask R-CNN, SVM, Vgg16, Vgg19, ResNet50, etc., and it shown a high precision and recall than others.
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