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

Tanveer, Muhammad Usama and Munir, Kashif and Raza, Ali and Abualigah, Laith and Garay, Helena and Prado González, Luis Eduardo and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, helena.garay@uneatlantico.es, uis.prado@uneatlantico.es, UNSPECIFIED (2025) Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images. Food Science & Nutrition, 13 (1). ISSN 2048-7177

Text Food Science Nutrition - 2025 - Tanveer - Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop.pdf Available under License Creative Commons Attribution. Download (866kB)

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.

Item Type:	Article
Uncontrolled Keywords:	feature extraction ; plant disease detection ; plant leaf detection ; precision agriculture ; transfer learning
Subjects:	Subjects > Engineering Subjects > Nutrition
Divisions:	Europe University of Atlantic > Research > Scientific Production Fundación Universitaria Internacional de Colombia > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production
Depositing User:	Sr Bibliotecario
Date Deposited:	07 Jan 2025 12:10
Last Modified:	07 Jan 2025 12:10
URI:	http://repositorio.funiber.org/id/eprint/15983

Actions (login required)

View Item