Green Symphony: Deep Learning for Crop Health Assessment

Abstract

Abstract: The agricultural sector holds paramount importance in our economy, impacting our daily lives significantly. Effective management of agricultural resources is crucial for ensuring profitability in crop production. However, farmers often lack expertise in identifying and managing plant leaf diseases, leading to reduced yields. Detecting and classifying leaf diseases is pivotal for maximizing agricultural productivity. Utilizing Convolutional Neural Networks (CNNs) offers a promising solution for automated leaf disease detection and classification. This research focuses on detecting diseases in key crops such as apple, grape, corn, potato, and tomato plants. By leveraging deep CNN models, this study aims to enhance disease monitoring in large crop fields, enabling prompt identification of disease symptoms and facilitating timely intervention. Such advancements in plant leaf disease detection have broad applications in biological research and agricultural institutes, offering immense potential to optimize crop health management and maximize yields. Comparing the proposed deep CNN model with established transfer learning approaches like VGG16 underscores the significance of this research endeavor in addressing the critical need for efficient disease detection and management in agriculture..

Plant phenotyping relevance

植物葉の病徴を画像から検出・分類するCNN手法が研究の中心であり、植物の病害状態を直接推定するため、表現型計測手法として採用。

Code and data availability

植物フェノタイピング解析を再現する公開資産であることを、入力本文と直接リンクから確認できなかったため保留しました。

No evidence-backed public reproduction asset is currently recorded.