Paper Title
Plant Disease Detection Using Machine Learning

Abstract
Abstract - Detecting plant leaf disease is the key to preventing loss of yield and yield. Plant disease research refers to the study of visually distinct patterns in plants. Plant health monitoring and disease detection are critical to long-term agriculture. It is critical to detect sickness early in a country like India, where agriculture employs the majority of populations. Faster and precise prediction of disease could help reducing the losses. Significant advances in machine learning technology have made it possible to improve the performance and accuracy of object detection and recognition systems. This paper focuses on finding plant disease and reducing economic losses. For image recognition, we presented a machine learning approach. In our research, we suggested a system which is capable of accurately detecting a variety of ailments. In this paper, we use torch framework to analyze semantic segmentation of leaf images and after that we evaluate feature extraction and classification technique using CNN. Our work is feasible by the use of open source Plant Village Dataset. The results show that CNN is the best classifier for plant disease detection with high accuracy. Keywords - Plant Village Dataset, Torch Framework, CNN Model, CNN Classifier