A hybrid feature fusion framework integrating HSM-CNN and GLCM-SVM for real-time mango grading

Mango grading in post-harvest supply chains has traditionally relied on manual inspection, a process prone to subjectivity, inconsistency, and economic inefficiency. Automated quality assessment offers a compelling alternative, yet existing systems often lack the multi-dimensional feature integration necessary to replicate the nuanced judgements of experienced graders. This study presents a hybrid computational framework that fuses deep learning with handcrafted image analysis to achieve accurate, interpretable, and real-time mango grading aligned with established AGMARK quality standards. The framework simultaneously evaluates shape regularity, surface texture, and chromatic properties extracted from multiple colour spaces, combining a Hierarchical Shape Morphology Convolutional Neural Network model with a Grey-Level Co-occurrence Matrix Support Vector Machine classifier through a calibrated weighted fusion mechanism. Trained and evaluated on 1,800 mango images spanning four quality grades, the system achieved an overall classification accuracy of 96.8%, a macro F1-score of 0.967, and an area under the receiver operating characteristic curve of 0.991, surpassing standalone deep learning and traditional machine learning baselines by meaningful margins. Each image was processed in 42 ms on an embedded GPU platform, demonstrating suitability for deployment on industrial conveyor systems without sacrificing throughput. These results indicate that integrating morphological and textural handcrafted features with learned deep representations produces a more robust and generalisable grading system than either approach alone. Beyond mango assessment, the proposed fusion methodology offers a transferable blueprint for automated quality inspection across a broader range of horticultural produce, with the potential to reduce post-harvest losses, standardise grading practices, and support more efficient agricultural supply chains globally.