# Quality Assessment and Prediction of Peanut Storage Life Based on Deep Learning

## TL;DR

This study uses deep learning to assess and predict peanut quality during storage, helping optimize storage conditions and shelf-life management.

## Contribution

A novel D-SCSformer model is proposed for predicting peanut quality indicators with improved accuracy over existing methods.

## Key findings

- The Deep Clustering Network outperformed DEC and K-Means++ in quality grading of stored peanuts.
- The D-SCSformer model achieved high predictive accuracy with significant improvements in MSE, MAE, and RMSE.
- The study provides a technical basis for managing peanut storage conditions to maintain quality.

## Abstract

As a globally significant oilseed and food crop, peanuts exhibit significant quality changes influenced by storage conditions. This study monitored six key quality indicators—including fatty acid content, carbonyl content, peroxide value, acid value, phenylacetaldehyde and moisture content—in peanut samples stored for 30 weeks under varying temperature and humidity conditions. A Deep Clustering Network (DCN) was employed for quality grading, yielding superior results compared to Deep Empirical Correlation (DEC) and K-Means++ clustering methods, thereby establishing effective quality grading standards. Building upon this, a D-SCSformer time series prediction model was constructed to forecast quality indicators. Through dimensionality-segmented embedding and statistical feature fusion, it achieved strong predictive performance (MSE = 0.2012, MAE = 0.2884, RMSE = 0.4387, and R2 = 0.9998), reducing MSE by 57.9%, MAE by 35.4%, and RMSE by 34.1%, while improving R2 from 0.9996 to 0.9998 compared to the mainstream Crossformer model. This study provides technical support and a decision-making basis for temperature and humidity regulation and shelf-life management during peanut storage.