A Systematic Review: Detecting Plant Diseases Using Machine Learning Techniques

Agriculture industry represents an important component of Indian economy. Early detection of crop disease by using some latest technologies helps farmers to provide security to their crops. There are many types of disease found in different plants but it is not easy for farmers to detect at early stage of their life which is lead to yield loss and indirectly a financial loss to farmers. It is very tricky work, also required lots of time and knowledge to identify the exact disease on plant at a very initial stage. Latest technologies are very helpful to succeed in controlling this issue and hence here in this manuscript will provide a proper resolution along with the disease detection. The image processing with convolutional neural network (CNN), K-means clustering, Support Vector Machine (SVM), Naive Bayes is useful. Here this manuscript provides a literature review on Machine Learning (ML) techniques used in agricultural sector, mainly concentrating on the classification, detection of disease. Plant disease classification includes several steps like dataset creation, load creation, preparing, segmentation, feature extraction, training classifier and classification. The principal objective of this study is to get the information of various work done in this field and the future scope of research in this area.