AI Driven Crop Disease Prediction and Management System

Abstract

Agriculture is extremely important to human civilization, providing food and contributing to the economy. Plants are often susceptible to diseases and insects that have considerable challenges during production. Early detection of harvest diseases is important to minimize damage and reduce costs. While traditional methods do not provide real-time identification, foldable neuronal networks (CNNs) provide a solution by allowing for accurate detection and classification of leaf disease. This study focuses on identifying diseases in plants such as apples, grapes, corn, potatoes and tomatoes. The proposed deep CNN model is compared to a transfer learning approach, such as VGG16. AI-based systems analyze plant images to recognize diseases at the early stages and recommend management strategies, loss of harvests and improved yields. Such systems have applications in agriculture and biological research.

Plant phenotyping relevance

植物画像から葉の病害をCNNで検出・分類する手法の開発と比較が中心であり、病害状態という植物表現型を直接推定しているため。

Code and data availability

The paper describes a CNN/VGG16 leaf-disease system but contains no public dataset deposit, author code release, trained model checkpoint, or availability statement. The PlantVillage dataset is only mentioned as prior work, and no authors' public URL for assets is given.

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