Digital farming and smart agriculture: Data driven decisions for sustainable agriculture

Digital farming is transforming agriculture by addressing rising food demand, climate challenges, resource scarcity, and environmental degradation. By integrating technologies such as IoT, UAVs, AI, robotics, and data-driven decision-support systems, it enhances precision in resource use, boosts crop and livestock productivity, and minimizes environmental impacts. IoT-based sensors, automated irrigation, and smart gateways enable real-time monitoring and optimized water and nutrient management, while UAVs facilitate precision spraying, field mapping, and cost-effective input application. AI-driven analytics strengthen yield prediction, disease detection, and overall decision-making. Key applications include precision crop monitoring, variable-rate technologies, predictive modelling, climate-smart advisories, and digital supply chain platforms. However, adoption faces challenges such as high initial costs, limited rural infrastructure, low digital literacy, data privacy concerns, and socio-cultural resistance. Addressing these barriers through government support, affordable solutions, improved connectivity, and farmer training can accelerate progress. Overall, digital farming offers a pathway to climate-smart, economically viable, and resilient agriculture, contributing significantly to sustainable development and global food security.

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Publication Details

Published
2025-12-05
DOI
https://doi.org/10.64643/jatirv1i1-140024-001
Primary Topic
Smart Agriculture and AI
Type
article
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article

Digital farming and smart agriculture: Data driven decisions for sustainable agriculture

Smart Agriculture and AI
article

Digital farming and smart agriculture: Data driven decisions for sustainable agriculture

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Abstract

Digital farming is transforming agriculture by addressing rising food demand, climate challenges, resource scarcity, and environmental degradation. By integrating technologies such as IoT, UAVs, AI, robotics, and data-driven decision-support systems, it enhances precision in resource use, boosts crop and livestock productivity, and minimizes environmental impacts. IoT-based sensors, automated irrigation, and smart gateways enable real-time monitoring and optimized water and nutrient management, while UAVs facilitate precision spraying, field mapping, and cost-effective input application. AI-driven analytics strengthen yield prediction, disease detection, and overall decision-making. Key applications include precision crop monitoring, variable-rate technologies, predictive modelling, climate-smart advisories, and digital supply chain platforms. However, adoption faces challenges such as high initial costs, limited rural infrastructure, low digital literacy, data privacy concerns, and socio-cultural resistance. Addressing these barriers through government support, affordable solutions, improved connectivity, and farmer training can accelerate progress. Overall, digital farming offers a pathway to climate-smart, economically viable, and resilient agriculture, contributing significantly to sustainable development and global food security.

Vol. 1(1)
Navsari Agricultural University (IN)
Openalex Percentile: Top 72%
Smart Agriculture and AI
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Digital farming and smart agriculture: Data driven decisions for sustainable agriculture · (2025) | TGRS Research Map | TGRS