IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v3y2020i3p18-245d389437.html
   My bibliography  Save this article

Learning from Data to Optimize Control in Precision Farming

Author

Listed:
  • Alexander Kocian

    (Department of Computer Science, University of Pisa, 56127 Pisa, Italy)

  • Luca Incrocci

    (Department of Agriculture, Food and Environment, University of Pisa, 56124 Pisa, Italy)

Abstract

Precision farming is one way of many to meet a 55 percent increase in global demand for agricultural products on current agricultural land by 2050 at reduced need of fertilizers and efficient use of water resources. The catalyst for the emergence of precision farming has been satellite positioning and navigation followed by Internet-of-Things, generating vast information that can be used to optimize farming processes in real-time. Statistical tools from data mining, predictive modeling, and machine learning analyze patterns in historical data, to make predictions about future events as well as intelligent actions. This special issue presents the latest development in statistical inference, machine learning, and optimum control for precision farming.

Suggested Citation

  • Alexander Kocian & Luca Incrocci, 2020. "Learning from Data to Optimize Control in Precision Farming," Stats, MDPI, vol. 3(3), pages 1-7, July.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:3:p:18-245:d:389437
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/3/3/18/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/3/3/18/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gallardo, Marisa & Elia, Antonio & Thompson, Rodney B., 2020. "Decision support systems and models for aiding irrigation and nutrient management of vegetable crops," Agricultural Water Management, Elsevier, vol. 240(C).
    2. Omolola M. Adisa & Joel O. Botai & Abiodun M. Adeola & Abubeker Hassen & Christina M. Botai & Daniel Darkey & Eyob Tesfamariam, 2019. "Application of Artificial Neural Network for Predicting Maize Production in South Africa," Sustainability, MDPI, vol. 11(4), pages 1-17, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bhoomin Tanut & Rattapoom Waranusast & Panomkhawn Riyamongkol, 2021. "High Accuracy Pre-Harvest Sugarcane Yield Forecasting Model Utilizing Drone Image Analysis, Data Mining, and Reverse Design Method," Agriculture, MDPI, vol. 11(7), pages 1-21, July.
    2. So Pyay Thar & Thiagarajah Ramilan & Robert J. Farquharson & Deli Chen, 2021. "Identifying Potential for Decision Support Tools through Farm Systems Typology Analysis Coupled with Participatory Research: A Case for Smallholder Farmers in Myanmar," Agriculture, MDPI, vol. 11(6), pages 1-20, June.
    3. Mar Carreras-Sempere & Rafaela Caceres & Marc Viñas & Carmen Biel, 2021. "Use of Recovered Struvite and Ammonium Nitrate in Fertigation in Tomato ( Lycopersicum esculentum ) Production for boosting Circular and Sustainable Horticulture," Agriculture, MDPI, vol. 11(11), pages 1-15, October.
    4. Sajith, Gouri & Srinivas, Rallapalli & Golberg, Alexander & Magner, Joe, 2022. "Bio-inspired and artificial intelligence enabled hydro-economic model for diversified agricultural management," Agricultural Water Management, Elsevier, vol. 269(C).
    5. Patryk Hara & Magdalena Piekutowska & Gniewko Niedbała, 2021. "Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data," Land, MDPI, vol. 10(6), pages 1-21, June.
    6. Javier Martínez-Dalmau & Julio Berbel & Rafaela Ordóñez-Fernández, 2021. "Nitrogen Fertilization. A Review of the Risks Associated with the Inefficiency of Its Use and Policy Responses," Sustainability, MDPI, vol. 13(10), pages 1-15, May.
    7. He, Liuyue & Xue, Jingyuan & Wang, Sufen, 2023. "WHCrop: A novel water-heat driven crop model for estimating the spatiotemporal dynamics of crop growth for arid region," Agricultural Water Management, Elsevier, vol. 287(C).
    8. Benjamin Ruch & Margita Hefner & André Sradnick, 2023. "Excessive Nitrate Limits the Sustainability of Deep Compost Mulch in Organic Market Gardening," Agriculture, MDPI, vol. 13(5), pages 1-13, May.
    9. Savvas, Dimitrios & Giannothanasis, Evangelos & Ntanasi, Theodora & Karavidas, Ioannis & Drakatos, Stefanos & Panagiotakis, Ioannis & Neocleous, Damianos & Ntatsi, Georgia, 2023. "Improvement and validation of a decision support system to maintain optimal nutrient levels in crops grown in closed-loop soilless systems," Agricultural Water Management, Elsevier, vol. 285(C).
    10. Incrocci, Luca & Thompson, Rodney B. & Fernandez-Fernandez, María Dolores & De Pascale, Stefania & Pardossi, Alberto & Stanghellini, Cecilia & Rouphael, Youssef & Gallardo, Marisa, 2020. "Irrigation management of European greenhouse vegetable crops," Agricultural Water Management, Elsevier, vol. 242(C).
    11. Blok, Chris & Voogt, Wim & Barbagli, Tommaso, 2023. "Reducing nutrient imbalance in recirculating drainage solution of stone wool grown tomato," Agricultural Water Management, Elsevier, vol. 285(C).
    12. Gallego-Elvira, B. & Reca, J. & Martin-Gorriz, B. & Maestre-Valero, J.F. & Martínez-Alvarez, V., 2021. "Irriblend-DSW: A decision support tool for the optimal blending of desalinated and conventional irrigation waters in dry regions," Agricultural Water Management, Elsevier, vol. 255(C).
    13. Elbeltagi, Ahmed & Deng, Jinsong & Wang, Ke & Hong, Yang, 2020. "Crop Water footprint estimation and modeling using an artificial neural network approach in the Nile Delta, Egypt," Agricultural Water Management, Elsevier, vol. 235(C).
    14. Cahn, Michael & Smith, Richard & Melton, Forrest, 2023. "Field evaluations of the CropManage decision support tool for improving irrigation and nutrient use of cool season vegetables in California," Agricultural Water Management, Elsevier, vol. 287(C).
    15. Campana, P.E. & Lastanao, P. & Zainali, S. & Zhang, J. & Landelius, T. & Melton, F., 2022. "Towards an operational irrigation management system for Sweden with a water–food–energy nexus perspective," Agricultural Water Management, Elsevier, vol. 271(C).
    16. Zinkernagel, Jana & Maestre-Valero, Jose. F. & Seresti, Sogol Y. & Intrigliolo, Diego S., 2020. "New technologies and practical approaches to improve irrigation management of open field vegetable crops," Agricultural Water Management, Elsevier, vol. 242(C).
    17. Tei, Francesco & De Neve, Stefaan & de Haan, Janjo & Kristensen, Hanne Lakkenborg, 2020. "Nitrogen management of vegetable crops," Agricultural Water Management, Elsevier, vol. 240(C).
    18. Gallardo, Marisa & Peña-Fleitas, María Teresa & Giménez, Carmen & Padilla, Francisco M. & Thompson, Rodney B., 2023. "Adaptation of VegSyst-DSS for macronutrient recommendations of fertigated, soil-grown, greenhouse vegetable crops," Agricultural Water Management, Elsevier, vol. 278(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jstats:v:3:y:2020:i:3:p:18-245:d:389437. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.