KLASIFIKASI PENYAKIT TANAMAN JAGUNG BERDASARKAN CITRA DAUN MENGGUNAKAN CNN

Abstrak

Corn (Zea mays L.) is a strategic agricultural commodity in Indonesia, playing a vital role in food security and the economic livelihood of farmers. However, corn productivity is often threatened by the presence of pests and diseases. With advancements in artificial intelligence (AI), particularly in image processing, Convolutional Neural Networks (CNN) have emerged as a promising method for automated disease identification. This study aims to design and develop an AI-based image identification system using CNNs to detect diseases on corn leaves, including Northern Corn Leaf Blight (Helminthosporium turcicum), Southern Corn Leaf Blight (Bipolaris maydis), and Common Rust (Puccinia polysora). The methodology involves training the AI system on a dataset consisting of infected corn leaf images. The outcome of this research is expected to be an accurate, fast, and user-friendly image identification application, thereby assisting farmers in monitoring the health of their corn crops. Ultimately, the system is intended to make a positive contribution to improving productivity and ensuring the sustainability of corn farming in Indonesia.

Keywords— Corn Leaf Disease, Image Classification, Convolutional Neural Network, Artificial intelligence