Artificial intelligence driven sentiment analysis and market intelligence for strategic business decision making
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Date
2025
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ES Food and Agroforestry
Abstract
There is an increasing threat to agricultural productivity and food security because of the problems posed by climate change, limited resources, and inefficient supply chain management. The purpose of this study is to introduce AI-based forecasting and supply optimization network (AIFSO-Net), which is an artificial intelligence-driven network for supply optimization and forecasting. Dynamic optimization techniques and hybrid deep learning Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN–BiLSTM) are the two components that constitute this system. By utilizing data from multiple sources, including IoT sensors, satellite imaging, and meteorological records, the system can make predictions regarding crop yields and improve resource allocation in real time. Through the implementation of Predictive Reinforcement Learning (PRL), AIFSO-Net can improve the efficiency of its supply chain by adjusting to changes in the environment and market. The experimental results reveal that AIFSO-Net outperforms existing models, achieving a 12.8% improvement in forecasting accuracy and a 15.6% increase in supply chain efficiency. This is compared to standard methods such as transformers and genetic algorithm-based models. AIFSO-Net can improve agricultural forecasting, reduce post-harvest waste, and increase food security systems worldwide. In conclusion, AIFSO-Net offers a solution for sustainable agriculture that is both scalable and adaptable in addition to providing vital information for decision-making in agricultural situations that are always changing. The novelty of this work lies in the unified integration of hybrid deep learning, multi-source data fusion, and adaptive optimization (Dynamic Multi-Objective Optimization Algorithm (DMOOA) + PRL) within a single end-to-end framework for both crop forecasting and supply chain management, an aspect not addressed in the existing literature. This study contributes to sustainable agriculture and responsible resource management through accurate forecasting and adaptive supply chain optimization in alignment with Sustainable Development Goals 2 and Sustainable Development Goals 12.