Optimized Hybrid CNN-LSTM Model for Agriculture Supply-chain Management System

Abstract

Agricultural supply-chain management (ASM) has intricated and linked networks that make itpossible for agricultural goods to be transported from farms to customers. The integration ofdeep learning (DL) and blockchain technology has the power to completely transform theagriculture industry by improving sustainability, efficiency, and transparency. But there is stilla lot to learn about the scalability and long-term effectiveness of combining blockchain and DLtechnologies 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 andstorage of agriculture food products. To fine tune the hyperparameters of CNN-LSTM, theAdaptive White Shark Optimizer (AWSO) algorithm is applied. Before forecasting,Exploratory Data Analysis (EDA) on sales has been performed in which daily, monthly andyearly sales analysis are computed based on store and item features.