Smart Crop Cultivation System Using Automated Agriculture Monitoring Environment in the Context of Bangladesh Agriculture

Md Bayazid Rahman; Joy Dhon Chakma; Abdul Momin; Shahidul Islam; Md Ashraf Uddin; Md Aminul Islam; Sunil Aryal

doi:10.3390/s23208472

Smart Crop Cultivation System Using Automated Agriculture Monitoring Environment in the Context of Bangladesh Agriculture

Sensors (Basel). 2023 Oct 15;23(20):8472. doi: 10.3390/s23208472.

Authors

Md Bayazid Rahman¹, Joy Dhon Chakma¹, Abdul Momin², Shahidul Islam³, Md Ashraf Uddin⁴, Md Aminul Islam⁵, Sunil Aryal⁴

Affiliations

¹ Department of Computer Science and Engineering, Faculty of Science and Technology, Notre Dame University Bangladesh, Dhaka 1000, Bangladesh.
² Agricultural Engineering Technology, School of Agriculture, Tennessee Tech University, Cookeville, TN 38505, USA.
³ Department of Agriculture, University of Arkansas at Pine Bluff, Pine Bluff, AR 71601, USA.
⁴ School of Information Technology, Deakin University, Geelong, VIC 3220, Australia.
⁵ Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh.

Abstract

The Internet of Things (IoT) is a transformative technology that is reshaping industries and daily life, leading us towards a connected future that is full of possibilities and innovations. In this paper, we present a robust framework for the application of Internet of Things (IoT) technology in the agricultural sector in Bangladesh. The framework encompasses the integration of IoT, data mining techniques, and cloud monitoring systems to enhance productivity, improve water management, and provide real-time crop forecasting. We conducted rigorous experimentation on the framework. We achieve an accuracy of 87.38% for the proposed model in predicting data harvest. Our findings highlight the effectiveness and transparency of the framework, underscoring the significant potential of the IoT in transforming agriculture and empowering farmers with data-driven decision-making capabilities. The proposed framework might be very impactful in real-life agriculture, especially for monsoon agriculture-based countries like Bangladesh.

Keywords: crop monitoring; crop yield prediction; decision tree classifier; irrigation control; smart agriculture.

MeSH terms

Agriculture* / methods
Bangladesh
Technology*

Grants and funding

This research was funded by the United States Department of Agriculture–National Institute of Food and Agriculture (USDA-NIFA) grant number GR019237 and the APC was funded by The Principal Investigator (PI): Prof. Shahidul Islam.