| Abstrakt: |
The major reason for minimizing crop productivity is various diseases in plants. To eliminate the disease-induced losses in plants during growth as well as to increase crop productivity, former disease detection and prevention on the crop are the most challenging factors. Thus, it is a suitable decision that can be taken by the farmers or villagers to avoid further losses. The automated detection of crop disease with images has been done using many classification techniques, such as k-Nearest Neighbor Classifier, Probabilistic Neural Network, Genetic Algorithm, Support Vector Machine, Main Component Analysis, Artificial Neural Network, and Fuzzy Logic. The works on the technique of deep learning which identifies the various diseases in plants. Here, we use an efficient convolution neural network algorithm (CNN) algorithm which can detect the type of diseases in leaves. Our proposed paper includes implementation steps such as datasets gathering, training, testing, classification, and using CNN to classify the leaves which are diseased or healthy based on data. This work identifies the potato leaf disease and mitigation using alerts with IoT and CNN. This work identifies the potato leaf disease using the KNN and CNN methods, and the results are compared in this paper. The developed scheme also provides help in mitigation using email for the solution of disease using IoT. An accuracy of ~ 90% has been achieved using CNN-based classification. |
Full text from SpringerLink