Implementation of Convolutional Neural Network (CNN) for Watermelon Plant Diseases Using Lenet-5 Architecture

Abstract

Watermelon (Citrullus Lanatus) is a horticultural commodity with high economic value that is widely cultivated by farmers in Indonesia, but the productivity of watermelon plants is often hampered by various types of diseases such as anthracnose, leaf spot and viral mosaic which can reduce the quality and quantity of crops. Manual identification of leaf diseases by farmers or agricultural experts is often subjective and takes a short time. Therefore, a plant disease identification method that is fast, accurate and easily accessible is needed. Artificial intelligence technology, especially Convolutional Neural Network (CNN), has proven effective in applying classification and object detection. Lenet-5 architecture is one of the early forms of CNN developed by Yann LeCun which is now widely reused for image classification purposes. The dataset used consists of 4 classes namely Antrachnose, Downy mildew, Healthy leaves and Mosaic virus with a total of 1155 images. The evaluation results of this study obtained the accuracy, recall, precision and F1-score results are 93%, 94% 95% and 94% respectively.