Priyanka Roy

Modern agriculture is increasingly leveraging digital technologies, particularly digital twins, to enhance productivity and sustainability. Digital twins create real-time virtual models of agricultural systems, allowing for improved decision-making in crop growth, soil health, and resource use by integrating IoT sensors, AI, machine learning, satellite imagery, and cloud analytics. Studies show that these technologies can boost crop yields by 10-25%, reduce water usage by 20-30%, and cut fertilizer application by 15-20% while preserving soil quality. The proposed architecture for agricultural digital twins includes data acquisition, modeling, predictive analytics, and decision support, with applications in crop simulation, soil monitoring, and precision irrigation. Nevertheless, challenges like high infrastructure costs and data interoperability hinder broader implementation. Ultimately, digital twins represent a transformative shift toward intelligent, sustainable agricultural practices.

The global food system faces challenges in food security, environmental sustainability, and supply chain inefficiencies, with traditional methods struggling to meet demands while minimizing resource depletion and ecological impact. New approaches that integrate AI and new technologies must be developed to address the growing concerns of global food security and sustainability. This article reviews the potential progressive roles of automation, artificial intelligence, and renewable energy for maintaining sustainable practices, optimizing food production, and enhancing decision-making. Blockchain technology improves transparency and traceability in food supply chains. IoT-powered smart systems enable real-time monitoring of crops, livestock, and food storage conditions, optimizing resource usage. AI-driven developing algorithms enhance decision-making, automate agricultural processes, and improve food quality and safety. While renewable energy sources like solar-powered aquaponics and hybrid energy systems promote ecologically sustainable food production, robotics and 3D printing are developing agricultural processes. However, widespread adoption faces challenges such as high costs, infrastructure limitations, and regulatory barriers alongside these benefits. Future research should focus on enhancing AI-driven solutions, addressing scalability issues, and ensuring equitable access to these technologies across AI ethics, infrastructure, and regulatory framework.

Renewable energy, particularly solar energy, is an important component of sustainable agriculture because it provides energy-efficient and ecologically friendly alternatives to traditional techniques. AI reduced waste and produced predicted insights, improving agricultural operations. This review focuses on how solar energy and artificial intelligence are being used in aquaculture and post-harvest technologies. Aquaculture uses AI-driven systems to monitor real-time water quality and fish health, enhancing productivity by reducing mortality rates and minimizing environmental impact. AI algorithms optimize the utilization of sun dryers and cold storage units, reducing post-harvest losses by over 30% and ensuring high-quality produce by minimizing waste. The paper discusses the economic and environmental effects of various technologies, ranging from the high initial cost and existing constraints to the potential for decentralized energy networks and data-driven optimization. This analysis brings out the key trends, gaps, and future opportunities in integrating solar and artificial intelligence technologies for resilient, sustainable, and energy-efficient agriculture. The study establishes the groundwork for future developments in sustainable agriculture by highlighting the important implications for energy efficiency, climate resilience, and global food security.