Review on Leaf Plant Disease Classification Using Machine Learning Techniques

Agriculture plays a vital role in the world economy. It basically provides job opportunities for the teaming population, eradicates poverty and contributes to the growth of the economy. Hence the need for improved effort for classifying diseases in plant from its leaf is important as it leads to increase in crop yield. Machine learning methods had being used in leaves plant diseases classification. This paper reviews various techniques used for plant leaf disease classification, and found that Most of the researchers used Support Vector Machine (SVM) algorithms for plant disease classification which they concluded that (SVM) is not suitable for large dataset and it does not perform very well when the dataset has more noise, also the target class will be overlapping. To overcome this difficulties a proposed methodology with different approaches to Machine learning was suggested; Deep learning is a sort of machine learning in which a model figures out how to accomplish classification tasks in a direct way from pictures, Neural network will be train using Fine-tuning techniques on different neural networks architectures and at the end comparisons will be done to find out the best neural networks that will be the best for providing an improved solution for leaf plant disease classification by checking their performance best on their accuracy and confusion matrix.