Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Tasks?

Review Computer Science, Interdisciplinary Applications

Emerging Trends in Machine Learning to Predict Crop Yield and Study Its Influential Factors: A Survey

Nishu Bali et al.

Summary: Agriculture plays a crucial role in the development of any nation, and scientists are working on predicting crop yields efficiently. Deep learning and hybrid models have shown promise in crop yield prediction. Factors like temperature, precipitation, and agronomic practices significantly influence crop yields.

ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING (2022)

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Review Multidisciplinary Sciences

Machine learning in plant science and plant breeding

Aalt Dirk Jan van Dijk et al.

Summary: Machine learning has been widely applied in plant science and breeding to extract meaningful patterns from large, complex plant data sets, particularly in connecting genotypes to different levels of phenotypes such as biochemical traits and yield.

ISCIENCE (2021)

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Article Computer Science, Information Systems

The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Governance

Benjamin Leiding et al.

Summary: Current business enactments heavily focus on human-to-human transactions, but the rise of smart devices enables machine-to-machine transactions, laying the foundation for the machine-driven Machine-to-Everything (M2X) Economy. While human transactions are governed by enforceable oral or written contracts, a machine-driven ecosystem requires a digital equivalent accessible to all stakeholders. An electronic contract platform allows for secure and tamper-resistant storage of information in a multi-stakeholder setting, with electronic smart contracts using a blockchain protocol presenting a suitable approach for M2X enactments.

FUTURE INTERNET (2021)

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Article Plant Sciences