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A Review on Digital Farming using Machine Learning Techniques

N. Yedukondalu, V. Bhuvana Kumar, A. Narayana Rao

20222022 International Conference on Automation, Computing and Renewable Systems (ICACRS)5 citationsDOI

Abstract

To compensate rainfall shortages and improve irrigation delivery for plants development, fresh water is necessary. Agricultural activities use more than 70% of the available freshwater resources. This study highlights the need for effective water management, which involves the application of digitized agricultural technology. In addition to, this study intends to merge multiple machine learning models to provide the best irrigation monitoring process. The emerging trend and application of machine learning models, as well as the application of established machine learning algorithms to perform long-term irrigation management are also analyzed. Finally, this study discusses how digital farming tools, including such smartphone and web applications, may support farmers in managing smart irrigation operations in a cost-effective manner.

Topics & Concepts

Computer scienceAgricultureIrrigationMerge (version control)Economic shortageProcess (computing)Machine learningAgricultural engineeringArtificial intelligenceEngineeringOperating systemPhilosophyBiologyGovernment (linguistics)EcologyInformation retrievalLinguisticsSmart Agriculture and AIWater Quality Monitoring Technologies
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