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Abstract: In modern vision and pattern recognition, complex tasks such as picture analysis, facial recognition, fingerprint identification, and DNA sequencing necessitate a nuanced approach, often requiring the integration of multiple feature descriptors. This research proposes a multi-model identification and classification strategy leveraging multi- feature fusion techniques to address these intricate challenges. Specifically, the focus is on fruit and vegetable recognition and classification, a burgeoning field in computer and machine vision. By employing an identification system tailored to fruits and vegetables and harnessing the capabilities of MobileNetV2 architecture, customers and buyers can more easily discern the type and quality of produce. MobileNetV2, a convolutional neural network architecture optimized for mobile devices, offers promising performance in real-world applications. This abstract highlight the significance of CNNs and MobileNetV2 in tackling multifaceted recognition tasks, underscoring the potential for enhanced efficiency and accuracy in fruit and vegetable classification.
Maurya et al. (Sun,) studied this question.