Sugarcane Leaf Diseases and Detection

Abstract

As Sugarcane is very important in agriculture field the efficient management is required. It is mainly dependent on proper identification and detection. Sugarcane plant leaf suffers from bacterial, fungal, viral, phytoplasma etc. Plant leaf diseases can result in the reduction of both quality and quantity of agricultural yield. We cannot observe minute variation in the infected part of leaf. In this paper, the different software solution to automate detection and classification of plant leaf disease is given. The various computer vision and machine learning techniques are used to classify diseases & then appropriate measures can be carried out. This approach will enhance productivity of sugarcane crops and increase the efficiency and accuracy of disease detection in plant leaves. It includes several steps viz. Image acquisition, image pre-processing, segmentation, features extraction and neural network based classification.