An Integrated Digital Framework for Sustainable Crop Residue Management and AIBased Maize Leaf Disease Detection

Abstract

Two of the greatest agricultural sustainability, crop productivity, and environmental health problems are crop residue burning and maize leaf diseases. Poor management of residue causes wastage of resources and air pollution, whereas late diagnosis of diseases causes losses of huge yields. To solve these problems, this paper will suggest an integrated intelligent agricultural system, combining a web-based Crop Residue Management System (CRMS) with a maize leaf disease detection module, based on deep learning.

Plant phenotyping relevance

トウモロコシ葉の病害状態を深層学習で検出するモジュールが統合システムの中心的構成要素であり、植物病害フェノタイピング手法の開発に該当する。

Code and data availability

The paper describes a hybrid CNN maize leaf disease detection module using the public PlantVillage dataset, but provides no author code, trained model checkpoints, data deposits, or any availability URLs. PlantVillage is a generic third-party dataset, not a paper-specific asset, and no qualifying public asset with an作者

No evidence-backed public reproduction asset is currently recorded.