The application of Internet of Things (IoT) and Machine Learning (ML) in the agricultural industry has led to the emergence of smart farms and precision agriculture. These advancements have paved the way for Cooperative Smart Farming (CSF), where connected farms collaborate and share data for mutual benefit. This data sharing allows ML models to aggregate individual farm data, generating actionable insights that can be utilized by all farms in CSFs. These insights help enhance desired outputs such as crop yield, water resource management, irrigation schedules, and seed applications. However, complications arise in CSFs when some farms fail to transfer high-quality data and instead rely on other farms to feed ML models. Additionally, there is a possibility of rogue farms that aim to snoop on other farms without contributing any data. To address these challenges, this paper analyzes the behavior of participating farms using a game theory approach. Each farm is motivated to maximize its profit within the CSF. The paper first highlights the problem of defective farms in CSFs due to inadequate data quality. It then proposes a ML framework that segregates farms based on their data quality and automatically assigns them to an appropriate CSF cluster. The proposed model rewards farms that provide better data while penalizing those that do not meet requirements or exhibit malicious behavior. This ensures model integrity and improves overall performance while addressing the issue of defective farms. To implement this proposed strategy, AWS cloud and IoT platforms are utilized in conjunction with CSF. Data is collected from different smart member farms and processed using built-in ML models to calculate accuracy for each dataset. K-means clustering algorithm is applied based on accuracy values to create clusters at the cooperative level. If a participant belongs to a cluster with at least one other participant, they are considered part of the cooperative group; otherwise, they are classified as defective. In conclusion, this paper presents a game theoretic analysis for Cooperative Smart Farming which addresses challenges related to defective farm behavior in CSFs through a proposed ML framework and implementation strategy which ensure data quality, model integrity and better performance within CSFs . This research contributes towards advancing smart farming practices and precision agriculture by promoting collaboration between farmers through leveraging IoT technology as well as Machine Learning technologies .
- IoT and ML are used in agriculture to make farms smarter and more precise.
- Connected farms can work together and share data through Cooperative Smart Farming (CSF).
- Sharing data helps ML models give useful advice to all farms in CSFs.
- Some farms may not share good data or behave badly, which can cause problems.
- A special approach called game theory is used to understand farm behavior in CSFs.
Definitions
- IoT: Internet of Things - a system of devices connected to the internet that can communicate with each other
- ML: Machine Learning - a type of artificial intelligence where computers learn from data and make decisions without being explicitly programmed
- Agriculture: the practice of growing crops and raising animals for food or other products
- Precision agriculture: using technology to optimize farming practices, such as using sensors to monitor soil moisture levels
- Cooperative Smart Farming (CSF): a system where connected farms work together and share information
- Data sharing: when information is exchanged between different parties
- Actionable insights: useful information that can be acted upon or used to make decisions
- Challenges: difficulties or problems that need to be overcome
- Malicious behavior: when someone intentionally does something harmful or dishonest
- Game theory: a branch of mathematics that studies strategic decision-making in situations involving multiple participants
Cooperative Smart Farming: Leveraging IoT and Machine Learning to Enhance Agriculture
The application of Internet of Things (IoT) and Machine Learning (ML) in the agricultural industry has led to the emergence of smart farms and precision agriculture. These advancements have paved the way for Cooperative Smart Farming (CSF), where connected farms collaborate and share data for mutual benefit. This data sharing allows ML models to aggregate individual farm data, generating actionable insights that can be utilized by all farms in CSFs. These insights help enhance desired outputs such as crop yield, water resource management, irrigation schedules, and seed applications.
However, complications arise in CSFs when some farms fail to transfer high-quality data and instead rely on other farms to feed ML models. Additionally, there is a possibility of rogue farms that aim to snoop on other farms without contributing any data. To address these challenges, this paper analyzes the behavior of participating farms using a game theory approach. Each farm is motivated to maximize its profit within the CSF.
Problem Statement
The paper first highlights the problem of defective farms in CSFs due to inadequate data quality. It then proposes a ML framework that segregates farms based on their data quality and automatically assigns them to an appropriate CSF cluster. The proposed model rewards farms that provide better data while penalizing those that do not meet requirements or exhibit malicious behavior. This ensures model integrity and improves overall performance while addressing the issue of defective farms.
Proposed Solution
To implement this proposed strategy, AWS cloud and IoT platforms are utilized in conjunction with CSF. Data is collected from different smart member farms and processed using built-in ML models to calculate accuracy for each dataset. K-means clustering algorithm is applied based on accuracy values to create clusters at the cooperative level. If a participant belongs to a cluster with at least one other participant, they are considered part of the cooperative group; otherwise, they are classified as defective .
Conclusion
In conclusion, this paper presents a game theoretic analysis for Cooperative Smart Farming which addresses challenges related
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 transfer high-quality data and rather rely on other farms to feed ML models. Another possibility is the presence of rogue farms in CSFs that want to snoop on other farms without actually contributing any data. In this paper, we analyze the behavior of farms participating in CSFs using game theory approach, where each farm is motivated to maximize its profit. We first present the problem of defective farms in CSFs due to lack of better data, and then propose a ML framework that segregates farms and automatically assign them to an appropriate CSF cluster based on the quality of data they provide. Our proposed model rewards the farms supplying better data and penalize the ones that do not provide required data or are malicious in nature, thus, ensuring the model integrity and better performance all over while solving the defective farms problem.
