Work

Dairy Product Production Prediction Based on BiLSTM-Attention model

Year: 2025

Type: article

Abstract: With the rapid development of the dairy industry, accurate prediction of production is crucial to optimizing production plans. To this end, this paper proposes a prediction model that combines a bidirectional long short-term memory network (BiLSTM) with an attention mechanism (Attention). BiLSTM can effectively capture long-term and short-term dependencies in time series, while the attention mechanism can dynamically focus on the features of key time points, thereby improving prediction accuracy. Experiments show that the BiLSTM-Attention model improves the accuracy of dairy production prediction compared to traditional regression analysis and a single LSTM model, especially when processing long time series data. This study provides an effective solution for the accurate prediction of dairy production.

Source: Journal of Computer Science and Digital Technology

DOI: 10.71204/by8g5g40

Authors: Xiangyuan Qi, Guanglei Qiang, Fujiang Yuan

Institution: Taiyuan Normal University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: hybrid

Funders: Taiyuan Normal University, Division of Graduate Education