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Z. Hong, Z. Kalbarczyk, R. K. Iyer, “A Data-Driven Approach to Soil Moisture Collection and Prediction,” 2016 IEEE International Conference on Smart Computing (SMARTCOMP), St. Louis, MO, 1-6, 2016.

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Article

Intelligent Smart Agriculture Methane Crop Yields Detection and Prediction for Machine Learning Techniques

1Research Scholar, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India

2Professor, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India


Applied Ecology and Environmental Sciences. 2022, Vol. 10 No. 8, 509-518
DOI: 10.12691/aees-10-8-3
Copyright © 2022 Science and Education Publishing

Cite this paper:
S. P. Ramesh, Dr. Muthusamy Periyasamy. Intelligent Smart Agriculture Methane Crop Yields Detection and Prediction for Machine Learning Techniques. Applied Ecology and Environmental Sciences. 2022; 10(8):509-518. doi: 10.12691/aees-10-8-3.

Correspondence to: S.  P. Ramesh, Research Scholar, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India. Email: spramesh.me@gmail.com

Abstract

The MEVM (methane energy value model) was created for several energy crops. Machine learning has been created with big data developments and better enrolling than make new open doors for data genuine science in the multi-disciplinary agri-business progresses territory. By applying machine learning to sensor data, farm the chief's frameworks are forming into ceaseless artificial intelligence engaged tasks that give rich recommendations and encounters to farmer decision help and action. IoT contraptions give data about the nature of developing fields and a short time later make a move dependent upon the farmer input. The arrangement endeavors to mastermind diverse possible unstructured associations of crude data, assembled from different kinds of IoT devices, united and advanced self-ruling style using the upside of model changes and model-driven designing to change data in a coordinated structure.

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