Automated Fruit Disease Detection and Grading Using Image Processing and Hybrid Deep Learning Models
DOI:
https://doi.org/10.26438/ijcse/v13i11.99107Keywords:
Convolutional Neural Networks (CNN), Fruit Disease Detection, Deep Learning, Image Processing, Agricultural Automation, Crop Health MonitoringAbstract
Automated fruit disease detection and grading are critical for advancing precision agriculture and reducing crop losses. This paper proposes a novel diagnostic framework that integrates advanced image processing techniques with a hybrid deep learning architecture. Two feature extraction pipelines were employed: statistical transforms, including Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT), and computer vision descriptors such as Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), and Gray-Level Co-occurrence Matrix (GLCM). The classification phase benchmarks five models—Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and a proposed Hybrid CNN+ANN architecture. Experimental results on a diverse fruit dataset demonstrate that the hybrid model achieves 99.54% accuracy with near-perfect precision and recall, outperforming traditional and standalone deep learning approaches. The system exhibits rapid convergence, scalability, and robustness against variations in lighting and background conditions. Integrated into a Python-based interface, this solution enables real-time disease diagnosis and grading, offering significant potential for smart farming and sustainable agricultural practices.
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