Deep Learning-Based Comprehensive Classification of Strawberry Maturity Grades and Weight Specifications Using lmage Processing

This study addresses the challenges of low accuracy and slow speed in automatic strawberry classification. We propose an integrated approach that combines image processing, computer vision, and deep learning to classify ripe strawberries comprehensively. A lightweight convolutional neural network (CNN) model is developed to achieve 99.16% accuracy in ripeness level identification. Additionally, a multiple linear regression model, incorporating area, perimeter, length, and width, predicts strawberry weights with an R² of 0.924 and an average prediction error of 2.304%. By integrating ripeness recognition and weight prediction, our method provides a standardized classification system for ripe strawberries. The CNN model ensures high recognition accuracy and real - time grading, while the regression model enhances weight specification accuracy. This approach contributes a scientific and efficient non - destructive classification system, benefiting precision agriculture and strawberry quality control.