REVOLUTIONIZING AGRICULTURE: AI-DRIVEN SMART FARMING ANALYTICS PLATFORM

Authors

  • Rohit Sharma International Institute of Information & Technology, Bangalore, India Author

Keywords:

Smart Agriculture, Artificial Intelligence, Internet Of Things (IoT), Precision Farming, Environmental Sustainability

Abstract

The Smart Farming Analytics Platform represents a revolutionary advancement in agricultural technology, integrating artificial intelligence, IoT sensors, and cloud computing to transform traditional farming practices. This article presents a comprehensive analysis of the platform's implementation across 50,000 acres of diverse agro-climatic zones. The system leverages 15,000 ground-based IoT sensors, high-resolution drone imagery, and satellite data to provide real-time monitoring and actionable insights. Field trials demonstrate significant improvements in key agricultural metrics: 23% increase in crop yields, 30% reduction in water consumption, and 25% decrease in pesticide usage. The platform's AI models, combining GradientBoost and LSTM networks, achieve 92% accuracy in yield prediction, while computer vision algorithms provide real-time disease detection with up to 95% accuracy. Environmental impact analysis shows a 20% reduction in carbon footprint, 35% decrease in chemical runoff, and 15% increase in beneficial insect populations. Economic analysis reveals a 215% ROI over 24 months, with annual savings of $45,000 per 500 acres. This article demonstrates the platform's potential to revolutionize agriculture through data-driven decision-making while promoting environmental sustainability.

References

Dalhatu Muhammed, Ehsan Ahvar, Shohreh Ahvar, Maria Trocan, Marie-José Montpetit, Reza Ehsani, "Artificial Intelligence of Things (AIoT) for smart agriculture: A review of architectures, technologies and solutions," Journal of Network and Computer Applications, Volume 228, August 2024, 103905. https://www.sciencedirect.com/science/article/pii/S1084804524000821 [2] K. Liakos, P. Busato, D. Moshou, S. Pearson, and D. Bochtis, "Data Integration for Precision Agriculture - Challenges and Opportunities for the Database community," DOI:10.5753/erbd.2022.223386, Conference: Escola Regional de Banco de Dados, At: Lages/SC - Brasil. https://www.researchgate.net/publication/362727720_Data_Integration_for_Precision_Agriculture_-_Challenges_and_Opportunities_for_the_Database_community [3] Abdul Salam, Syed Shah., "Internet of Things in Smart Agriculture: Enabling Technologies," in IEEE. https://ieeexplore.ieee.org/document/8767306 [4] S. Wolfert, L. Ge, C. Verdouw, and M. J. Bogaardt, "Big Data in Smart Farming – A review," Agricultural Systems, vol. 153, pp. 69-80, 2017, doi: 10.1016/j.agsy.2017.01.023. https://www.sciencedirect.com/science/article/pii/S0308521X16303754 [5] Muhammad Ayaz, Ammad Uddin, Zubair Sharif, Ali Mansour, "IoT based smart agriculture," August 2019 IEEE Access PP(99):1-1, DOI:10.1109/ACCESS.2019.2932609. https://www.researchgate.net/publication/334858202_Internet-of-Things_IoT-Based_Smart_Agriculture_Toward_Making_the_Fields_Talk [6] Kavita Jhajharia, Pratistha Mathur, Sanchit Jain, Sukriti Nijhawan, "Crop Yield Prediction using Machine Learning and Deep Learning Techniques," Procedia Computer Science, Volume 218, 2023, Pages 406-417. https://www.sciencedirect.com/science/article/pii/S1877050923000236 [7] Jiawei Li, Yongliang Qiao,, "An improved YOLOv5-based vegetable disease detection method," Published in Computers and Electronics in… 1 November 2022, Agricultural and Food Sciences, Computer Science. https://www.semanticscholar.org/paper/An-improved-YOLOv5-based-vegetable-disease-method-Li-Qiao/5add3c68a0e038751d33f76e1c8214263af3e82f [8] D. Wilson and S. Chen, "Advanced Visualisation of Big Data for Agriculture as Part of Databio Development," DOI:10.1109/IGARSS.2018.8517556, Conference: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. https://www.researchgate.net/publication/328985189_Advanced_Visualisation_of_Big_Data_for_Agriculture_as_Part_of_Databio_Development [9] Thomas Peprah Agyekum, Philip Antwi-Agyei, Andrew J. Dougill, Lindsay C. Stringer., "Benefits and barriers to the adoption of climate-smart agriculture practices in West Africa: A systematic review,". https://rmets.onlinelibrary.wiley.com/doi/10.1002/cli2.79

Published

2024-11-11

How to Cite

Rohit Sharma. (2024). REVOLUTIONIZING AGRICULTURE: AI-DRIVEN SMART FARMING ANALYTICS PLATFORM. INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND INFORMATION TECHNOLOGY (IJRCAIT), 7(2), 1056-1068. https://ijrcait.com/index.php/home/article/view/IJRCAIT_07_02_082