AI in Agriculture: Advanced Smart Irrigation for Enhanced Crop Yields

Integrating Artificial Intelligence (AI) into the process of agriculture is changing the way this business is taking place. The work presented here concentrates on designing and realizing an advanced AI driven smart irrigation system for tackling the main issues like water scarcity and inefficient irrigation practices that diminish crop productivity and waste resources. The proposed smart irrigation system will leverage machine learning algorithms, predictive analytics, as well as other AI technologies and real time data in the soil moisture, weather conditions, crop health and water usage. It will use predictive models and ensure that exact, timely irrigation is used, hemmed in to specific crop need requirements in order to minimize wasted water and increase water use efficiency for optimum crop growth. The evaluations of the system effectiveness are by simulations and field trial as part of the research. Water consumption, crop yield, and resource utilization efficiency will be analyzed to the utmost degree. The expected outcomes are that a significant reduction in water usage will be realized, development of best practices for smart irrigation in agriculture, and increased crop yields. The purpose of this study is to modernize existing irrigation practices in order to secure food and sustainable agriculture. The findings offer great insights and provide a generalisable framework to deploy the AI based smart irrigation systems to bring benefits to the farmers, policymakers and other stakeholders in the agricultural domain.

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Publication Details

Journal
SHS Web of Conferences
Published
2025-01-01
DOI
https://doi.org/10.1051/shsconf/202521601065
Citations
1
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
1.75
Controls
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article

AI in Agriculture: Advanced Smart Irrigation for Enhanced Crop Yields

F. Rahman
1 citations
SHS Web of Conferences
Smart Agriculture and AI
1.75
article

AI in Agriculture: Advanced Smart Irrigation for Enhanced Crop Yields

F. Rahman
article en
1 citations

Abstract

Integrating Artificial Intelligence (AI) into the process of agriculture is changing the way this business is taking place. The work presented here concentrates on designing and realizing an advanced AI driven smart irrigation system for tackling the main issues like water scarcity and inefficient irrigation practices that diminish crop productivity and waste resources. The proposed smart irrigation system will leverage machine learning algorithms, predictive analytics, as well as other AI technologies and real time data in the soil moisture, weather conditions, crop health and water usage. It will use predictive models and ensure that exact, timely irrigation is used, hemmed in to specific crop need requirements in order to minimize wasted water and increase water use efficiency for optimum crop growth. The evaluations of the system effectiveness are by simulations and field trial as part of the research. Water consumption, crop yield, and resource utilization efficiency will be analyzed to the utmost degree. The expected outcomes are that a significant reduction in water usage will be realized, development of best practices for smart irrigation in agriculture, and increased crop yields. The purpose of this study is to modernize existing irrigation practices in order to secure food and sustainable agriculture. The findings offer great insights and provide a generalisable framework to deploy the AI based smart irrigation systems to bring benefits to the farmers, policymakers and other stakeholders in the agricultural domain.

SHS Web of ConferencesVol. 216
Kalinga University (IN)
Openalex Percentile: Top 15%
Smart Agriculture and AI
1.75
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