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Sanjay Kumar Singh,
- Professor, Senior Lecturer Department of English AKP JSR, Jharkhand, India
Abstract
Artificial Intelligence (AI) has emerged as a transformative technology with the potential to revolutionize agriculture, forestry, and rural development by enabling data-driven decision-making, resource optimization, and sustainable management practices. Rapid developments in computer vision (CV), machine learning (ML), deep learning (DL), natural language processing (NLP), and predictive analytics have increased the use of AI in a variety of rural industries. In agriculture, AI-driven technologies support precision farming, crop yield prediction, disease and pest detection, smart irrigation, and autonomous farming operations, leading to enhanced productivity and efficient resource utilization. In forestry, AI facilitates forest monitoring, deforestation detection, wildfire prediction, biodiversity conservation, and carbon stock estimation through the analysis of remote sensing and geospatial data. Beyond agriculture and forestry, AI contributes significantly to rural development by improving governance, financial inclusion, market intelligence, supply chain management, and digital advisory services. AI-powered systems assist policymakers and stakeholders in addressing complex socio-economic and environmental challenges while promoting sustainable rural livelihoods. Despite these benefits, the adoption of AI in rural ecosystems is constrained by challenges such as inadequate digital infrastructure, limited technical expertise, data privacy concerns, and issues related to accessibility and equity. This review examines the major AI technologies, their applications, benefits, challenges, and emerging trends in agriculture, forestry, and rural development. Furthermore, it highlights future research directions, including Explainable AI, Edge AI, Federated Learning, and the integration of AI with the Internet of Things (IoT) and blockchain technologies. The study concludes that responsible and inclusive implementation of AI can significantly enhance productivity, resilience, environmental sustainability, and socio-economic development in rural communities.
Keywords: Artificial Intelligence; Machine Learning; Deep Learning; Precision Agriculture; Smart 1 Farming; Computer Vision; Forest Monitoring; Rural Development; Predictive Analytics; Sustainable Agriculture.
References
1. Food and Agriculture Organization (FAO). (2023). Artificial Intelligence in Agriculture: Challenges and Opportunities. FAO Publications. 2. NITI Aayog. (2018). National Strategy for Artificial Intelligence. Government of India. 3. United Nations. (2015). Transforming our world: the 2030 Agenda for Sustainable Development. 4. Wolfert, S., et al. (2017). Big Data in Smart Farming – A Review, Agricultural Systems, Elsevier. 5. Kamilaris, A., & Prenafeta-Boldu, F. X. (2018). Deep Learning in Agriculture: A Survey, Computers and Electronics in Agriculture. 6. Shankar, G., et al. (2021). AI-Driven Precision Farming: A Review of Technologies and Implementation, Journal of Rural Studies.
Journal of Artificial Intelligence Research & Advances
| Volume | 13 | |
| 03 | ||
| Received | 18/04/2026 | |
| Accepted | 04/08/2026 | |
| Published | 30/09/2026 | |
| Publication Time | 165 Days |