Robust coffee plant disease classification using deep learning and advanced feature engineering techniques.

ABSTRACT: Coffee, the world's most traded tropical crop, is vital to the economies of many producing countries. However, coffee leaf diseases pose a serious threat to coffee quality and sustainable production. Deep learning has shown strong performance in plant disease identification through automatic image classification. Nevertheless, reliance on a single convolutional neural networks (CNNs) architecture restricts feature variability and real-world generalization. Moreover, limited work has systematically combined feature selection/reduction with CNNs, which constrains the advancement of hybrid models capable of capturing complementary features while ensuring computational efficiency without accuracy loss. This article presents an enhanced deep learning-based framework for coffee disease classific

SUBMITTER: Ardah H 

PROVIDER: S-EPMC12695173 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature