Identifying Rotten Region on the Plant Leaf in Advance to Increase the Crop Yield using Multinominal Probit Regression

Detection of plant disease at an early stage increases the crop yield otherwise these diseases may negatively impact the agro market economy. The conventional methods were time consuming and practically infeasible to cover thousands acres of farming areas to detect leaf diseases. A methodology is proposed in this paper, to spot and to analyze the plant leaf diseases using digital image processing techniques through a supervised machine learning technique called multi-support vector machine (m-SVM) algorithm. SVM handles both semi structured and unstructured data. The proposed model recognizes and classifies the images of the leaves that were captured by digital camera or a mobile phone or drones or web camera. A novel way of training and methodology was used to accelerate the speedy, easy and simple implementation of the system in real-time. The experimental outcomes make evident that the proposed system detects and classifies the major 6 plant leaves diseases successfully: Cercospora leaf spot, Alternaria Alternata, Rust, Anthracnose, Powdery Mildew and Bacterial Blight. Also some of the unanswered challenges are discussed that require to be answered by developing a sensible automatic plant disease recognition system to apply in field conditions.