Crop Disease Detection Using Machine Learning

Abstract

Agriculture remains one of the most essential sectors for sustaining human life and economic stability. However, crop diseases continue to pose a serious threat to agricultural productivity, often leading to significant financial losses for farmers. Traditional disease identification methods rely heavily on manual inspection, which is time-consuming, requires expert knowledge, and is not always accurate. In this paper, a smart crop disease detection system is proposed using machine learning techniques. The system focuses on analyzing leaf images to identify visible symptoms of diseases at an early stage. Image preprocessing techniques are applied to enhance the quality of the input data, followed by feature extraction and classification using an efficient learning model. The proposed approach aims to reduce human effort while improving detection accuracy. The model is trained and tested on a dataset of crop leaf images and demonstrates promising performance in identifying multiple types of plant diseases. The results indicate that the system can serve as a supportive tool for farmers by providing quick and reliable predictions. This approach not only improves productivity but also contributes to sustainable agricultural practices. Future enhancements can further improve real-time detection and expand the system for a wider range of crops

Plant phenotyping relevance

葉画像から植物病害の可視症状を抽出・分類する機械学習手法が研究の中心であり、植物状態の表現型推定に該当する。

Code and data availability

The paper describes a CNN crop disease detection system but provides no authors' public code, trained model, or paper-specific dataset deposit. The only dataset mention is PlantVillage/Kaggle as generic public sources (cited prior work), and TensorFlow/PlantVillage URLs appear only as generic references, not as authors

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