Pendeteksi Penyakit Daun Kentang Menggunakan Algoritma Convolutional Neural Network (CNN)

Abstract

Potato leaf disease is one of the main problems in potato cultivation because it can reduce plant quality, decrease crop yield, and cause economic losses for farmers. Manual disease detection still has limitations because it depends on farmers’ experience and is prone to errors, especially when disease symptoms have similar visual characteristics. This study aims to apply the Convolutional Neural Network (CNN) algorithm to predict potato leaf diseases based on digital images. The dataset used in this study was obtained from Kaggle and consisted of 1,500 potato leaf images divided into three classes: healthy leaves, early blight, and late blight. The research stages included dataset collection, data splitting into training, testing, and validation data, CNN modeling using Jupyter Notebook, model training with 50 epochs, model evaluation using a Confusion Matrix, and model implementation into a web-based system using Flask. The test results show that the CNN model was able to classify potato leaf diseases with an accuracy of 97%. These results indicate that CNN is effective in recognizing visual patterns in potato leaf images, such as color changes, spots, and leaf damage. This study is expected to serve as a basis for developing an early detection system for potato leaf diseases that is faster, more accurate, and easier for farmers to use.

Plant phenotyping relevance

ジャガイモ葉の画像から病害状態を推定するCNN手法が研究の中心であり、モデル評価と実装も行っているため、植物フェノタイピング手法として含める。

Code and data availability

The paper uses a third-party public Kaggle dataset (Potato Leaf Disease Dataset by Muhammad Ardi Putra) and describes CNN modeling in Jupyter Notebook with a Flask web deployment, but provides no authors' public code, model checkpoint, or dataset deposit URL. The Kaggle dataset is a generic external resource, not an作者-

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