Plant Disease Prediction System Using Machine Learning

Abstract

Agriculture is an important sector for food production and economic growth. Plant diseases reduce crop quality and productivity, causing financial loss to farmers. This project proposes a Real-Time Plant Disease Detection System using Deep Learning techniques. Users can upload plant leaf images through a website, and the system analyzes the image using CNN and MobileNetV2 models to detect whether the leaf is healthy or diseased. The system provides fast and accurate disease prediction along with remedy suggestions for farmers. Keywords: Deep Learning, CNN, MobileNetV2, Plant Disease Detection, Machine Learning, Smart Agriculture.

Plant phenotyping relevance

葉画像から健康・罹病状態をCNN/MobileNetV2で推定する手法が研究の中心であり、植物病害状態の画像ベース表現型推定に該当する。

Code and data availability

The paper describes a plant disease detection system using CNN/MobileNetV2 but provides no public dataset, image, code, model, or supplement links. Datasets mentioned (PlantVillage, Kaggle) are generic third-party sources without paper-specific deposit language or URLs.

No evidence-backed public reproduction asset is currently recorded.