21, July 2025

IOT-driven precision agriculture: A comprehensive review of machine learning techniques for rice plant disease diagnosis and nutrient deficiency detection using IOT sensor data

VOLUME-11; ISSUE-7; JULY-2025

Author(s): Subrat Chetia

Authors Affiliations:

Assistant Professor, Department of Computer Science, Pandit Deendayal Upadhyaya Adarsha  Mahavidyalaya,  Dalgaon 784116, India

DOIs:10.2015/IJIRMF/202507025     |     Paper ID: IJIRMF202507025


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In many nations, rice is the primary agricultural product. On the other hand, illnesses on rice plants may cause the crop to produce less rice overall and of lower quality. Consequently, early identification of plant diseases will aid in shielding rice from serious infection and minimizing crop loss. The recently utilizing in agricultural monitoring in conjunction with GPS and a camera. It is a substitute tool for gathering data in a big area fast and independently. This review describes a system based on Internet of Things (IoT) that uses real-time information, including acquisition utilizing image processing techniques to perform rice disease diagnosis and classification, in order to improve rice yield. The IoT sensors employed in this continuously monitor crucial environmental parameters, including soil moisture, temperature, and nutrient levels, providing real-time data. The system can map the location of diseased rice plants on rice fields and present the analytical technique by using a sensor to detect the position in real-time. The aim of this system is to function as a prototype for an early and real-time disease detection system based on the Internet of Things.

Rice Plant disease, Machine Learning(ML), Deep Learning(DL), Internet of Things(IoT), CNN, GPS, Sensor

Subrat Chetia(2025); IOT-driven precision agriculture: A comprehensive review of machine learning techniques for rice plant disease diagnosis and nutrient deficiency detection using IOT sensor data, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-7, Pp.170-180.          Available on –   https://www.ijirmf.com/

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