A Game Theoretic Analysis for Cooperative Smart Farming
Deepti Gupta, Paras Bhatt, Smriti Bhatt

TL;DR
This paper uses game theory to analyze cooperative smart farming, proposing a machine learning framework that incentivizes farms to share high-quality data and mitigates malicious behavior, enhancing overall system performance.
Contribution
It introduces a game theoretic model for cooperative smart farming and a ML-based clustering approach to ensure data quality and system integrity.
Findings
The model effectively segregates farms based on data quality.
Farms supplying better data are rewarded, improving overall data integrity.
The approach mitigates malicious farms and enhances system performance.
Abstract
The application of Internet of Things (IoT) and Machine Learning (ML) to the agricultural industry has enabled the development and creation of smart farms and precision agriculture. The growth in the number of smart farms and potential cooperation between these farms has given rise to the Cooperative Smart Farming (CSF) where different connected farms collaborate with each other and share data for their mutual benefit. This data sharing through CSF has various advantages where individual data from separate farms can be aggregated by ML models and be used to produce actionable outputs which then can be utilized by all the farms in CSFs. This enables farms to gain better insights for enhancing desired outputs, such as crop yield, managing water resources and irrigation schedules, as well as better seed applications. However, complications may arise in CSF when some of the farms do not…
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