Plant species identification and disease detection using deep learning

Abstract

Plant diseases affect the growth of the species and their respective crops, early identification of the plant disease prevents the losses in the yield and improves the quality and quantity of the agriculture products. Many machine learning models are used to detect the disease in the plant but, after the advancements of deep learning models, this area of research appears to have a great potential in terms of increasing accuracy.The proposed system identifies the plant species and disease of the leaf. The dataset we got from internet Kaggle is segregated and the different plant species are identified and are renamed to form a proper database then obtain test-database which consists of various plant diseases that are used for checking the accuracy of the project. Then using training data, we will train our classifier and then output will be predicted with better accuracy, we used google Net model to train the data, which consists of different layers which are used for predicting the disease.The existing system the farmers are using for the detection of diseases in the plants is that- they could be identified through the naked eye and their knowledge about plant disease. For doing so, on large number of plants is time consuming, difficult and accuracy is not good, Consulting experts is of great cost. In such kind of conditions to improve the accuracy rate and make it more beneficial suggested techniques are implemented for the detection of the diseases that makes the process cheaper and easier This review provides a comprehensive explanation of deep learning models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.