The Automate Process of Plant Disease Detection and Classification from an Image Based on Artificial Neural Network
Continuous advancement in image processing and machine learning techniques have made it possible for computers to see and learn. What is seen by the eyes of human being could be divide into pixels and gave to a computer so that the computer will be able to see and learn based on the provides values. Based on the input values fed in computers could learn to identify various things based on the things they have been learnt from them. There are many possible areas in which computers can be applied to see and learn in order to make the life of human being easier. In this research study an approach proposed which is capable of automatically detecting and classifying plant disease from an image based on artificial neural network. Now a days, plants have become much more important than they used to be some years ago where they have been only used to feed mankind as well as animals. This is due to the fact that plants are now used to prevent soil erosion, reduce wind, and also to improve the health conditions of the human beings by producing at least one fourth of the drugs that are prescribed. Generally the experts, agricultural extension worker and farmers follow the traditional way of manual visual inspection which is most commonly used. This approach is being adopted for detection and classification of plant diseases in practice. As there much disease, which are similar to each other, it is very difficult to identify them using the naked eye depending on its symptoms. The complexity of the disease symptoms and limitations of personnel experience may lead to errors in judgment. In addition, to detect the diseases researchers used the spectroscopic techniques. These techniques are very expensive and can only be utilized by trained persons only. By applying machine learning and image processing techniques, many researchers detect the plant diseases and classification with high accuracy rate different researchers have studied many techniques using machine learning and image processing. However, these proposed techniques still have limitation. Some of the limitation identified during this study are: speed of detection and classifying the disease is low, they have been focused on only few diseases, and still lacks in accuracy of results in some case.