Klasifikasi Penyakit Daun Jagung Menggunakan Metode CNN Dengan Image Processing HE Dan CLAHE - Mochammad Faisal Nur Sayyid (Pusat Riset dan Inovasi Nasional, 2024)
This research focuses on classifying diseases on corn leaves using Convolutional Neural Network (CNN) with HE and CLAHE image processing methods. Corn plants, as the main food crop, are susceptible to various diseases that can reduce the quantity and quality of the harvest. With advances in AI technology, CNN is implemented to automatically identify disease symptoms on corn leaves. Previously, research using KNN and GLCM feature extraction resulted in low accuracy. Therefore, this research utilizes HE and CLAHE image processing techniques to improve image quality before classification is carried out. The research results show that CNN with CLAHE achieves the highest accuracy of 95%, while the use of HE produces an accuracy of 91%. Testing successfully identified 149 disease images with CLAHE, while HE classified 145 disease images. The conclusion of the research is that using CNN with CLAHE is more effective in classifying corn leaf diseases compared to HE with an accuracy of 95%. It is hoped that the application of this method can help farmers efficiently identify and overcome diseases in corn plants, supporting increased yields.