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A Robust Deep Learning Framework for Multi-Class Fruit and Vegetable Classification Using Optimized AlexNet
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AI
DOI：10.18280/ts.420623.png)
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Abstract
En 中文
Automated sorting of fruits and vegetables is critical in modern agriculture and industry. However, this task faces a number of challenges, including the need for high classification accuracy, real-time processing speed, interclass similarities like the resemblance of chili peppers and bell peppers, and intraclass variability like the size and color differences among class apples. To tackle these challenges, this study implemented changes to the pre-trained AlexNet deep learning model, in which the first seven layers were frozen for feature extraction, replacing ReLU activations with LeakyReLU to improve discrimination of visually similar species, and class-weighted loss concerning imbalance among underrepresented classes like ginger (68 samples) and orange (69 samples). The model achieved 98.04% accuracy on the 36-class dataset (3,818 images), demonstrating a 2.47% improvement over the baseline AlexNet (95% confidence interval [1.12%, 4.19%]) and a 56.2% reduction in classification errors. As a side effect, computational efficiency improved, achieving 127.13 images per second for training and 55.45 images per second for testing on GPU hardware, demonstrating an optimal balance of performance and efficiency for practical deployment. This study revealed a solution for automated sorting of produce, where accuracy, morphological ambiguities, and operational speed posed critical constraints.
Keywords:
ANN
deep learning
alexnet
fruit classification
LeakyReLU
class imbalance
Journal
T
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1
Papers:
102
Citations:
1.2K