Detection of corn plant diseases using convolutional neural network: A review

The objective of this study is to detect corn plant diseases using the Convolutional Neural Network (CNN) algorithm in a Systematic Literature Review (SLR). The study related to the deepening of disease classification based on images, especially corn plants, and the use of the CNN algorithm. The most common diseases that attack corn plants are Common Rust (leaf rust), Gray Leaf Spot (gray leaf spot), and Blight (leaf blight). Recognizing these various diseases requires digital image technology or processing with algorithms that can perform classification well, that is the CNN algorithm found from SLR. The use of the CNN is very dependent on the parameters arranged in each convolution layer so the CNN needs to be improved, that is to develop hyperparameter tuning. The results of this study indicate that the direction of future research is to develop hyperparameter tuning in CNN to achieve higher accuracy. From SLR, the implementation of the hyperparameter tuning in CNN has not been found.