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Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture

Author

Listed:
  • Sakshi Balyan

    (Division of Plant Breeding and Genetic Resource Conservation, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, India)

  • Harsita Jangir

    (Division of Plant Breeding and Genetic Resource Conservation, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, India)

  • Shakti Nath Tripathi

    (Department of Botany, Nehru Gram Bharati Deemed to Be University, Prayagraj 221505, India)

  • Arpita Tripathi

    (Microbial Technology Department, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, India)

  • Tripta Jhang

    (Division of Plant Breeding and Genetic Resource Conservation, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, India)

  • Praveen Pandey

    (Division of Plant Breeding and Genetic Resource Conservation, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow 226015, India)

Abstract

Agriculture is essential to the existence of the human race, as well as the foundation of our civilization, because it provides food, fuel, fiber, and other resources necessary for survival; however, it is facing critical challenges due to anthropogenic climate change, which hampers food and nutritional security. Consequently, the agriculture industry must adjust to farming issues, such as the shift in global temperatures and environmental degradation, the scarcity of farm workers, population growth, and dietary changes. Several measures have been implemented to enhance agricultural productivity, including plant breeding, genetic engineering, and precision agriculture. In recent years, the world has witnessed the burgeoning development of novel scientific innovations and technological advancements enabled by drones, smart sensors, robotics, and remote sensing, resulting in a plethora of revolutionary methods that can be applied to real-time crop modeling, high-throughput phenotyping, weather forecasting, yield prediction, fertilizer application, disease detection, market trading, farming practices, and other environmental practices vital to crop growth, yield, and quality. Furthermore, the rise in big data, advanced analytics, falling technology costs, faster internet connections, increased connectivity, and increases in computational power are all part of the current digitalization wave that has the potential to support commercial agriculture in achieving its goals of smart farming, resilience, productivity, and sustainability. These technologies enable efficient monitoring of crops, soil, and environmental conditions over large areas, providing farmers with data to support precise management that optimizes productivity and minimizes environmental impacts. Though smart farming has significant potential, challenges like high implementation costs, data security concerns, and inadequate digital literacy among farmers remain. In summary, agriculture is rapidly transforming from conventional to digital farming, offering global solutions, efficient resource utilization, and minimized input costs while fostering farmer livelihoods and economic growth. Delivering a comprehensive view of how technology could help in tackling critical issues like environmental degradation and threatened world biodiversity, this perspective emphasizes the perks of digitalization. Future advancements may involve data encryption, digital literacy, and particular economic policies.

Suggested Citation

  • Sakshi Balyan & Harsita Jangir & Shakti Nath Tripathi & Arpita Tripathi & Tripta Jhang & Praveen Pandey, 2024. "Seeding a Sustainable Future: Navigating the Digital Horizon of Smart Agriculture," Sustainability, MDPI, vol. 16(2), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:475-:d:1313559
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    References listed on IDEAS

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    2. Krijn J. Poppe & Sjaak Wolfert & Cor Verdouw & Tim Verwaart, 2013. "Information and Communication Technology as a Driver for Change in Agri-food Chains," EuroChoices, The Agricultural Economics Society, vol. 12(1), pages 60-65, April.
    3. Jig Han Jeong & Jonathan P Resop & Nathaniel D Mueller & David H Fleisher & Kyungdahm Yun & Ethan E Butler & Dennis J Timlin & Kyo-Moon Shim & James S Gerber & Vangimalla R Reddy & Soo-Hyung Kim, 2016. "Random Forests for Global and Regional Crop Yield Predictions," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-15, June.
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