Deep Learning Framework for Identification of Leaf Diseases in Native Plants of Tamil Nadu Geographical Region

Abstract

Plant pathogens are a prominent cause of reduced yields, resulting in decreased crop yields. Scientists are striving to develop a mechanism for identifying plant ailments in order to boost farm output. Deep learning algorithms have been developed for pathogen recognition and prediction in tomato plant leaves. Two different types of diseases impact both healthy and sick leaves. A Convolution Neural Network, which is effective for detection and prediction barrier, was used to forecast Septoria spot and bacterial spot. A dataset of 4930 images of healthy and damaged leaves from a plant community is used for the experiments. The model’s performance is precisely evaluated, and the conclusion is accurate. The project makes use of Plant Village images of tomato, potato, and onion leaves. Four different classes can each be recognized by the suggested CNNs. In each instance, the trained model achieves accuracy of 100%, 98.3%, and 97.89%. The classification of leaf disease detection using simulation data shows the potential effectiveness of the proposed approach. The algorithm proposed can be applied to categories any additional species of native plant to Tamil Nadu. Self Help Groups (SHGs), which are found in each and every village in India, will be utilized to gather information on how farmers see themselves. The observations and ameliorate both will be communicated to the same SHGs. Because of its high success rate, the model is a good tool for counselling or early warning.