Hyperparameter Optimization of Convolutional Neural Network for Classification of Corn Leaf Disease Types using CLAHE
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Abstract
Corn plays an important role as one of the main food sources in Indonesia and around the world. Diseases in corn plants are often visible through their leaves. However, problems arise when farmers have difficulty detecting diseases that attack corn plants, making it difficult to take appropriate action to control them. Diseases in corn plants can lead to reduced photosynthesis, disrupt agricultural productivity, and cause financial losses for farmers. Therefore, a digital approach that can detect various types of diseases in corn plants is highly needed. In recent years, the emergence of machine learning algorithms has provided support systems for classifying corn leaf diseases. This research aims to classify types of corn leaf diseases using the Optimization of Convolutional Neural Network (CNN) Method for Classifying Types of Corn Leaf Diseases Using Contrast Limited Adaptive Histogram Equalization (CLAHE). The research stages include data collection, image enhancement with CLAHE, data augmentation, data preprocessing, classification, and evaluation. The Optimization of the CNN Method for Classifying Types of Corn Leaf Diseases Using CLAHE resulted in an accuracy of 94%, indicating that this experiment is capable of classifying corn leaf diseases effectively.
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