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Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production

Author

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  • Awais Ali

    (Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, MI, Italy)

  • Tajamul Hussain

    (Laboratory of Plant Breeding and Climate Resilient Agriculture, Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Songkhla 90110, Thailand)

  • Noramon Tantashutikun

    (Prince of Songkla University, Hat Yai 90110, Thailand)

  • Nurda Hussain

    (Laboratory of Plant Breeding and Climate Resilient Agriculture, Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Songkhla 90110, Thailand)

  • Giacomo Cocetta

    (Department of Agricultural and Environmental Sciences-Production, Landscape, Agroenergy, Università degli Studi di Milano, Via Celoria 2, 20133 Milano, MI, Italy)

Abstract

Technological advancements have led to an increased use of the internet of things (IoT) to enhance the resource use efficiency, productivity, and cost-effectiveness of agricultural production systems, particularly under the current scenario of climate change. Increasing world population, climate variations, and propelling demand for the food are the hot discussions these days. Keeping in view the importance of the abovementioned issues, this manuscript summarizes the modern approaches of IoT and smart techniques to aid sustainable crop production. The study also demonstrates the benefits of using modern IoT approaches and smart techniques in the establishment of smart- and resource-use-efficient farming systems. Modern technology not only aids in sustaining productivity under limited resources, but also can help in observing climatic variations, monitoring soil nutrients, water dynamics, supporting data management in farming systems, and assisting in insect, pest, and disease management. Various type of sensors and computer tools can be utilized in data recording and management of cropping systems, which ensure an opportunity for timely decisions. Digital tools and camera-assisted cropping systems can aid producers to monitor their crops remotely. IoT and smart farming techniques can help to simulate and predict the yield production under forecasted climatic conditions, and thus assist in decision making for various crop management practices, including irrigation, fertilizer, insecticide, and weedicide applications. We found that various neural networks and simulation models could aid in yield prediction for better decision support with an average simulation accuracy of up to 92%. Different numerical models and smart irrigation tools help to save energy use by reducing it up to 8%, whereas advanced irrigation helped in reducing the cost by 25.34% as compared to soil-moisture-based irrigation system. Several leaf diseases on various crops can be managed by using image processing techniques using a genetic algorithm with 90% precision accuracy. Establishment of indoor vertical farming systems worldwide, especially in the countries either lacking the supply of sufficient water for the crops or suffering an intense urbanization, is ultimately helping to increase yield as well as enhancing the metabolite profile of the plants. Hence, employing the advanced tools, a modern and smart agricultural farming system could be used to stabilize and enhance crop productivity by improving resource use efficiency of applied resources i.e., irrigation water and fertilizers.

Suggested Citation

  • Awais Ali & Tajamul Hussain & Noramon Tantashutikun & Nurda Hussain & Giacomo Cocetta, 2023. "Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:2:p:397-:d:1061718
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    References listed on IDEAS

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    Cited by:

    1. Shiang-Jen Wu & Han-Yuan Yang, 2024. "Modeling Reliability Analysis for the Branch-Based Irrigation Water Demands Due to Uncertainties in the Measured Surface Runoff," Agriculture, MDPI, vol. 14(7), pages 1-27, July.
    2. David D. J. Antia, 2023. "Desalination of Saline Irrigation Water Using Hydrophobic, Metal–Polymer Hydrogels," Sustainability, MDPI, vol. 15(9), pages 1-32, April.
    3. Asuamah Yeboah, Samuel, 2023. "Empowering Sustainable Consumption: Harnessing the Potential of Smart Grid Systems and Internet of Things for Environmental Conservation," MPRA Paper 117978, University Library of Munich, Germany, revised 29 May 2023.
    4. Awais Ali & Genhua Niu & Joseph Masabni & Antonio Ferrante & Giacomo Cocetta, 2024. "Integrated Nutrient Management of Fruits, Vegetables, and Crops through the Use of Biostimulants, Soilless Cultivation, and Traditional and Modern Approaches—A Mini Review," Agriculture, MDPI, vol. 14(8), pages 1-28, August.
    5. Godlove Suila Kuaban & Valery Nkemeni & Onyeka J. Nwobodo & Piotr Czekalski & Fabien Mieyeville, 2024. "Internet of Things Adoption in Technology Ecosystems Within the Central African Region: The Case of Silicon Mountain," Future Internet, MDPI, vol. 16(10), pages 1-28, October.
    6. Mark Schoor & Ana Patricia Arenas-Salazar & Benito Parra-Pacheco & Juan Fernando García-Trejo & Irineo Torres-Pacheco & Ramón Gerardo Guevara-González & Enrique Rico-García, 2024. "Horticultural Irrigation Systems and Aquacultural Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint," Agriculture, MDPI, vol. 14(6), pages 1-22, June.
    7. Li Bin & Muhammad Shahzad & Hira Khan & Muhammad Mehran Bashir & Arif Ullah & Muhammad Siddique, 2023. "Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology," Sustainability, MDPI, vol. 15(18), pages 1-18, September.

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