Intensification in Crop Health Status Identification and Classification System Using Mathematical Morphology and Learning Techniques

Abstract

Several research studies on groundnut leaves have been conducted, newlineparticularly for disease detection and classification using image processing and newlineautomation classification techniques such as machine learning, deep learning and a newlinecombination of both techniques. Relatively, few researchers focused on identifying newlinenutrient deficiency in some crops using automation techniques that too after newlineperceptible symptoms and not for groundnut crops. In the existing work of nutrient newlinedeficiency identification, researchers concluded their results with a binary newlineclassification like quotdeficientquot or quothealthyquot. There were no studies that attempted to newlinecategorise the various levels of nutrient concentration or predict the excess nutrient newlinecontent of groundnut leaves. Currently, no research has been conducted to identify newlinediseases and nutrient disorders of groundnut leaves together. newlineThe practice of real-time monitoring frequently incorporates the utilization newlineof modern technologies such as data analytics, adaptive learning techniques and newlinesensors. However, farmers are still facing certain challenges in identifying and newlinemanaging crop health status in real-time using modern technologies newline