Optimized Hybrid CNN-LSTM Model for Agriculture Supply-chain Management System
DOI:
https://doi.org/10.48047/4sdny979Keywords:
Agricultural supply-chain management (ASM), Deep Learning (DL), Adaptive White Shark Optimizer (AWSO), CNN-LSTM model, Statistical error measuresAbstract
Agricultural supply-chain management (ASM) has intricated and linked networks that make it
possible for agricultural goods to be transported from farms to customers. The integration of
deep learning (DL) and blockchain technology has the power to completely transform the
agriculture industry by improving sustainability, efficiency, and transparency. But there is still
a lot to learn about the scalability and long-term effectiveness of combining blockchain and DL
technologies in ASM. In order to tackle these issues, we suggest an Optimized Hybrid CNNLSTM model for ASM system, to make effective decisions regarding the production and
storage of agriculture food products. To fine tune the hyperparameters of CNN-LSTM, the
Adaptive White Shark Optimizer (AWSO) algorithm is applied. Before forecasting,
Exploratory Data Analysis (EDA) on sales has been performed in which daily, monthly and
yearly sales analysis are computed based on store and item features.
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