Artificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategies

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dc.contributor.authorSow, Sumit
dc.contributor.authorRanjan, Shivani
dc.contributor.authorSeleiman, Mahmoud F.
dc.contributor.authorAlkharabsheh, Hiba M.
dc.contributor.authorKumar, Mukesh
dc.contributor.authorKumar, Navnit
dc.contributor.authorPadhan, Smruti Ranjan
dc.contributor.authorRoy, Dhirendra Kumar
dc.contributor.authorNath, Dibyajyoti
dc.contributor.authorGitari, Harun
dc.contributor.authorWasonga, Daniel O.
dc.date.accessioned2024-07-05T06:51:03Z
dc.date.available2024-07-05T06:51:03Z
dc.date.issued2024-05-30
dc.descriptionArticleen_US
dc.description.abstractAgriculture plays a crucial role in the economy, and there is an increasing global emphasis on automating agricultural processes. With the tremendous increase in population, the demand for food and employment has also increased significantly. Agricultural methods traditionally used to meet these requirements are no longer adequate, requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers. Integration of technologies such as the Internet of Things, wireless communication, machine learning, artificial intelligence (AI), and deep learning shows promise in addressing these challenges. However, there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 327 initially identified articles, 180 were deemed pertinent, focusing primarily on AI’s potential in enhancing yield through better management of nutrients, water, and weeds. Taking into account research findings worldwide, we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and determine the best time for irrigation or herbicide application. The present status of AI-driven automation in agriculture holds significant promise for optimizing agricultural input utilization and reducing resource waste, particularly in the context of three pillars of crop management, i.e., nutrient, irrigation, and weed management.en_US
dc.identifier.otherhttp://dx.doi.org/10.32604/phyton.2024.052241
dc.identifier.urihttps://ir-library.ku.ac.ke/handle/123456789/28417
dc.language.isoenen_US
dc.publisherTech Science Pressen_US
dc.subjectAgricultureen_US
dc.subjectartificial intelligenceen_US
dc.subjectcrop managementen_US
dc.subjectnutrienten_US
dc.subjectirrigationen_US
dc.subjectweed managementen_US
dc.subjectresource use efficiencyen_US
dc.titleArtificial Intelligence for Maximizing Agricultural Input Use Efficiency: Exploring Nutrient, Water and Weed Management Strategiesen_US
dc.typeArticleen_US
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