Spectral Segmentation Augmented with Normalized Cuts for Detection of Early Blight Disease in Potato

Abstract

Agriculture includes the production of crops, animals, aquaculture, fisheries, and forests for both food and non-food use. The primary factor in the rise of sedentary civilization among humans was agriculture, which allowed people to live in cities by producing surpluses of food from the farming of domesticated species. Uncertainties in the Agricultural Sector directly imply a steep decline in every other aspect of any society. The Agronomical Products, be they raw vegetables, or produced fruits, are all prone to Parasites and Microorganisms. In this research, we will focus on Solanum Tuberosum, and its disease Early Blight, of which, Alternaria Solani is a Causal Vector. For the detection of Early Blight in Potato Leaves, we will make use of the Image Segmentation Technique, based on Spectral Clustering, with Normalized Laplacian Cuts. The model works by segmenting, or rather segregating the affected part of the Potato leaf from the unaffected part, resulting in the formation of a few clusters, based on which the affected leaves can be classified from the healthy leaves. Though the model is a parametrized model, the results have been explored for different values of the parameters, σi, and σx that correspond to the varying configuration of the Gaussian Kernel. The efficiency of the Proposed Model have been compared with existing Image Clustering Models, like, Fuzzy C-means Clustering, Agglomerate Clustering, etc.