Abstract: This paper presents intuitive directions for field research by introducing a ground-breaking IoT-ML-driven intelligent farm management platform. This study’s main goal is to address agricultural difficulties by providing a thorough, integrated solution. This work makes a variety of important contributions. By utilizing cutting-edge technology like IoT and Machine Learning (ML), it first improves conventional farm management procedures. Farmers now have the capacity to remotely monitor and regulate irrigation management thanks to sensor-based real-time data. Second, based on data gathered from agricultural fields, our machine learning model offers improved water control management and fertilizer use recommendations, maximizing production while minimizing resource usage. The suggested solution also uses blockchain technology to create a safe, decentralized network that guarantees data integrity and defends against threats. We also introduce energy harvesting technology to address the issue of continuous energy supply for IoT devices, which lessens the load on farmers by removing the requirement for additional batteries. We achieved 89.5% accuracy in our proposed machine learning model. The suggested model would provide a variety of services to farmers, including pesticide recommendations and water motor control via mobile applications and a cloud database.

Md. Mamun Hossain, Md. Ashiqur Rahman, Sudipto Chaki, Humayra Ahmed, Ahsanul Haque, Iffat Tamanna, Sweety Lima, Most. Jannatul Ferdous and Md. Saifur Rahman, “Smart-Agri: A Smart Agricultural Management with IoT-ML-Blockchain Integrated Framework” International Journal of Advanced Computer Science and Applications(IJACSA), 14(7), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01407107

@article{Hossain2023,
title = {Smart-Agri: A Smart Agricultural Management with IoT-ML-Blockchain Integrated Framework},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01407107},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01407107},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {7},
author = {Md. Mamun Hossain and Md. Ashiqur Rahman and Sudipto Chaki and Humayra Ahmed and Ahsanul Haque and Iffat Tamanna and Sweety Lima and Most. Jannatul Ferdous and Md. Saifur Rahman}
}