Image-Based Deep Learning Approach for Crop Disease Detection: A Schematic Review
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Abstract
Crop diseases significantly affect agricultural productivity, food quality, and economic stability worldwide. Early and accurate detection of plant diseases is essential for reducing crop losses and improving agricultural sustainability. Traditional disease identification methods mainly rely on manual inspection by agricultural experts, which is time-consuming, labor-intensive, and often inaccurate under large-scale farming conditions. In recent years, image-based deep learning techniques have emerged as powerful tools for automated crop disease detection due to their high accuracy, feature extraction capability, and real-time analysis performance. This schematic review presents a comprehensive overview of image-based deep learning approaches used for crop disease detection and classification. The study discusses various deep learning architectures, including Convolutional Neural Networks (CNN), Transfer Learning models, Recurrent Neural Networks (RNN), and hybrid deep learning frameworks applied to plant disease identification using leaf images. The review also highlights different stages involved in disease detection such as image acquisition, preprocessing, segmentation, feature extraction, classification, and performance evaluation. Publicly available datasets, data augmentation techniques, and evaluation metrics such as accuracy, precision, recall, and F1-score are also analyzed. Furthermore, the paper discusses the advantages, limitations, and challenges associated with deep learning-based crop disease detection systems, including dataset imbalance, environmental variations, computational complexity, and real-time deployment issues. The study concludes that image-based deep learning approaches have significant potential for developing intelligent and automated agricultural monitoring systems capable of improving crop health management and sustainable farming practices.
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