Fuzzy Deep Learning Recurrent Neural Network Algorithm to Detect Corn Leaf Disease

Abstract

Corn is a major commodity after rice in supporting food self-sufficiency in Indonesia. However, due to leaf disease, the quality and quantity of corn plants are greatly reduced. The problem with detecting corn leaf diseases is that the detection method is still manual, making it inefficient and ineffective. Therefore, in this study, disease detection on corn leaves was performed using the Fuzzy C-Means (FCM) and Long Short-Term Memory (LSTM) methods. First, oversampling was carried out to ensure an equal amount of data in all classes, then the corn leaf images were pre-processed before being input into the LSTM algorithm. After completing clustering process in the [Formula: see text] algorithm, the next step involved extracting texture features using the Gray Level Co-occurrence Matrix (GLCM) technique, followed by classification using LSTM. To assess their performance, both algorithms underwent evaluation using the k-fold cross-validation method, and their accuracy and speed were compared. The results of the k-fold cross-validation demonstrated that the [Formula: see text] algorithm achieved an accuracy of 63.53%, whereas the LSTM algorithm achieved an accuracy of 80.24%. In terms of the time required for training and prediction, the LSTM algorithm took 13[Formula: see text]min and 18[Formula: see text]s for training on corn leaf disease images, while the prediction process only took 1.59[Formula: see text]s. The training and prediction time required for the [Formula: see text] algorithm were 65[Formula: see text]min and 24[Formula: see text]s and 5[Formula: see text]min and 44[Formula: see text]s, respectively. The conclusion of this study is that the LSTM algorithm has better accuracy and time compared to [Formula: see text] on the dataset used in this study in terms of corn leaf disease detection.