Optimized CNN-LSTM Tuple Aggregation Framework with Reinforcement Learning for Real-Time Dairy Production Enhancement

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 6

Year of Publication : 2025

Authors : V. Manochitra, A.Shaik Abdul Khadir

10.14445/23488549/IJECE-V12I6P119

How to Cite?

V. Manochitra, A.Shaik Abdul Khadir, "Optimized CNN-LSTM Tuple Aggregation Framework with Reinforcement Learning for Real-Time Dairy Production Enhancement," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 6, pp. 238-246, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I6P119

Abstract:

The present-day dairy industry is dealing with mounting pressure for product quality, cost-effectiveness, and the volatility of supply chain performance. Solving these problems requires intelligent systems to process spatial and temporal information related to real-time working dairy farms. To deal with both spatial and temporal diffusions, in this paper, we introduce a more efficient aggregated framework of splice point signal and propose an Optimized Deep Learning-powered Tuple Aggregation framework combining CNN and LSTM for complete spatiotemporal feature extraction, as well as an RL-based anomaly detection algorithm to detect anomalies in manufacturing processes. The frame can model data tuples of dairy machines, environmental sensors, and quality control checkpoints to predict milk quality, maximise machine allocation, and perform predictive maintenance. By implementing such an integrated system, productivity is improved, wastage is reduced, and the operation of the supply chain is optimized. The experimental results based on monitoring dairy production data in real-time have proven the effectiveness of this model in accuracy, response time, and efficiency as opposed to classical machine learning methods. This study paves the way for completely automated intelligent management of dairy plants for eco-friendly, high-level products.

Keywords:

Dairy production optimization, Tuple aggregation, CNN-LSTM, Reinforcement learning, Anomaly detection, Spatiotemporal analysis, Predictive maintenance, Supply chain management, Milk quality prediction, Industry 4.0.

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