SMART DETECTION AND DIAGNOSIS OF PLANT DISEASE USING DEEP AND MACHINE LEARNING METHODS

Abstract

Many tasks are difficult for us but easier for machine to do as to detect any disease in plants it is difficult for human beings to find the diseases in plants without heaving years of experience in farming which can cause immense effect to the plants. In agriculture it is very important to recognize and find the disease of the plants in the early stages. As, disease in the plants can affect the yield of the crops. It is unhealthy for the plants and in return it can affect a lot to the farmer and in last danger to the food-security. Using computer vision techniques, we can classify the plants with the help of state-of the art ML algorithms and deep learning models to differentiate between healthy and the effected plants by classifying their leaves. It is one of the techniques that different researchers worked on different plants using different techniques and different pre-trained deep learning networks (DenseNet121, EfficientNetB0, InceptionV3, VGG19, and Xception) and classic machine learning algorithms to detect the diseases on the plants using the plant leaves. This research worked on two different approaches. First, the experimental component trains models using different plants images. Then, the training of the models that have already been trained. According to the results that we get in our experiments in our work, the Xception networks performed admirably from deep learning models. It gives us an accuracy of 89.93%. On other hand, from machine learning algorithms Random Forest performed much better than the SVM and Decision Tree. In addition to our results, it indicates that the performance of the pre trained network system gives the best findings for Plant disease detection.