An Improved Lightweight ConvNeXt for Peach Ripeness Classification

Accurate grading of peach ripeness is essential for determining optimal harvest timing, postharvest storage potential, and market value. However, traditional methods are often inefficient and highly subjective. Meanwhile, existing deep learning models face challenges in balancing complexity with accuracy. To address this, this paper proposes a lightweight improved model named LightConvNeXt-FCS, which centers on a novel lightweight module named LightBlock. This module drastically reduces the parameter count and computational overhead. To preserve representational capacity, auxiliary structures—including attention enhancement, cross-stage connections, and multi-scale fusion—are incorporated. Experimental results show that the model requires only 2.75 M parameters and 624.23 M FLOPs, representing a 90.1% reduction in parameters and an 86.0% decrease in computational cost compared to ConvNeXt-Tiny, with the model size compressed to 9.9% of the original, while achieving an accuracy of 94.62%, slightly outperforming the original model. This approach effectively resolves the common trade-off between model complexity and accuracy. By achieving high accuracy with a lightweight architecture, it provides a more feasible solution for deploying rapid and intelligent fruit ripeness grading systems.