Banana Shelf Life Prediction Method based on Novel Causal Convolution Lightweight Transformer Network

Accurately predicting the remaining shelf life can effectively reduce the risk of spoilage during the storage process of agricultural products. The quality of agricultural products can be indirectly indicated by changes in environmental parameters. To better explore the intrinsic relationship between key environmental parameters during banana storage and their remaining shelf life, this paper proposes a novel causal convolution lightweight Transformer network. This model utilizes causal convolution operations to mine the temporal features of sensor data and applies positional encoding to the input signals. It employs a Transformer encoder to extract and fuse features while also utilizing a probabilistic sparse self-attention mechanism instead of the conventional self-attention mechanism. Moreover, a distillation operation is introduced, which effectively reduces the number of trainable parameters in the Transformer-based model and shortens the training time. Compared to traditional machine learning algorithms (BP, SVM) and conventional time series data mining algorithms (LSTM, RNN), the proposed prediction method achieves a mean squared error of 0.0221, a root mean squared error of 0.1486, a mean absolute error of 0.1101, and a maximum prediction error of 0.2221 days, allowing for more accurate and efficient predictions of bananas’ remaining shelf life.