Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.

Abstract

ABSTRACT Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG‐GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two‐step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG‐16) network to extract initial pixel‐based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition‐based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG‐GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG‐GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG‐GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.

Plant phenotyping relevance

トウモロコシ葉画像から健全・感染状態を判定する画像解析モデルを開発し、他モデルとの性能比較で検証しているため、植物病害表現型の取得手法が中心である。

Code and data availability

The paper's maize leaf image dataset is a public Kaggle dataset (Corn Leaf Infection by Acharya) explicitly cited as the source of the 4226 healthy/infected images used for phenotyping. No author analysis code or trained model is publicly deposited; the Data Availability Statement only offers data on request, so no作者代码