IRO Journals
Journal of ISMAC

F.A.R.M. (Field Analysis Revenue Management): An IoT and ML Integrated Solution

Open Access
Published: 30 September, 2026
Pages: 360-375
  • Mareeshwaran G. , Mareeshwaran G.
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
  • Gowthamkishore V. , Gowthamkishore V.
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
  • Muthukumaran V. , Muthukumaran V.
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
  • Nithish S. , Nithish S.
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
    Student, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
  • Arockia Rubi S. Arockia Rubi S.
    Assistant Professor, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
    Assistant Professor, Department of Information Technology, NPR College of Engineering and Technology, Tamil Nadu, India
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How to Cite

G., Mareeshwaran, Gowthamkishore V., Muthukumaran V., Nithish S., and Arockia Rubi S. 2026. “F.A.R.M. (Field Analysis Revenue Management): An IoT and ML Integrated Solution”. Journal of ISMAC 8 (4): 360-75. https://doi.org/10.36548/jismac.2026.4.003.
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Keywords

Smart Agriculture
Internet of Things (IoT)
Random Forest
Long Short-Term Memory (LSTM)
Precision Farming
ThingSpeak
Smart Farming

Abstract

Decision support systems with field monitoring and predictive analysis capabilities are required for modern agriculture to increase efficiency in the field and proper management at the farm level. This research work proposes F.A.R.M. (Field Analysis and Revenue Management), an Internet of Things (IoT) and machine learning-based approach to data-centric agriculture management. The proposed solution uses an ESP32-based sensing system that includes soil moisture, temperature, humidity, rainfall, flame, and flow rate of water along with Soil Health Card (SHC) data for field analysis. Random Forest is used for crop prediction, LSTM model is used for predicting crop price, and crop yield estimation is done using the Random Forest Regressor. The outputs from the process are then displayed through a monitoring interface that is enabled by the use of ThingSpeak. Sensor decision rules have been implemented to facilitate the assessment of field conditions and monitoring operations. The experimental analysis attained a success rate of 99.32% for crop recommendation, with the LSTM model being able to capture changes in market prices.

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