Plant Disease Identification and its Solution using Machine Learning

In the agricultural sector, crop cultivation is crucial. At the time, ill crops are the main source of food loss, which affects output rates as a result. It has not yet been investigated how to diagnose plant illnesses at an inopportune moment. The key problem is to minimize the use of pesticides in the agricultural area while enhancing both the quantity and the quality of output. Our article investigates the prediction of leaf at an unfavorable time, sickness manifests itself. To estimate the contaminated region of the leaves, we present an improved k-mean clustering approach A segmentation model based on color is used to segment the polluted zone and allocate it to the appropriate groups. When it comes to temporal complexity and the size of the diseased area, experimental evaluations were performed on sample photos. Image processing It's possible to implement it to diagnose plant illnesses.Image capture, picture the disease detection method includes steps such as preprocessing, the procedure includes processes such as image segmentation, feature extraction, and classification. Our initiative aims to identify plant illnesses and give remedies to help people recover from them. It represents the proportion of the leaf that is impacted.