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Cited by 310Smart Agriculture and AIRemote Sensing in AgricultureAdvanced Neural Network Applications

Data-driven artificial intelligence applications for sustainable precision agriculture

Open access
Linaza, Maria Teresa, Posada, Jorge, Bund, Jürgen, Eisert, Peter, Quartulli, Marco, Döllner, Jürgen, Pagani, Alain, G. Olaizola, Igor +3 more·2021·Agronomy
DOI 10.3390/agronomy11061227
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Abstract

One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding difficulty in systematic data gathering, as no two fields are exactly alike. Therefore, the comparison of several pilot experiments in different fields, weather conditions and farming techniques enhances the collective knowledge. Thus, this work provides a summary of the most recent research activities in the form of research projects implemented and validated by the authors in several European countries, with the objective of presenting the already achieved results, the current investigations and the still open technical challenges. As an overall conclusion, it can be mentioned that even though in their primary stages in some cases, AI technologies improve decision support at farm level, monitoring conditions and optimizing production to allow farmers to apply the optimal number of inputs for each crop, thereby boosting yields and reducing water use and greenhouse gas emissions. Future extensions of this work will include new concepts based on autonomous and intelligent robots for plant and soil sample retrieval, and effective livestock management.

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  • AI and ML algorithms are used to optimize farming operations, such as predicting weather patterns, optimizing water usage, and monitoring crop health.

    “AI and ML algorithms are used to optimize the farming operations, such as predicting weather patterns, optimizing water usage, and monitoring crop health [15] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [15]

    The Introduction describes AI-assisted analysis of field conditions and risks from pests and diseases, while the Conclusions describe AI-based irrigation that reduced water use in strawberry crops. These passages support crop-health monitoring and water-use optimization, but our copy does not show AI or ML being used to predict weather patterns. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Robotics is used to automate tasks in agriculture, such as planting, harvesting, and spraying.

    “Robotics is used to automate tasks in agriculture, such as planting, harvesting, and spraying.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [15]

    Section 2.3 says agricultural robots autonomously perform tasks such as weeding and irrigating, supporting the general point about automation. The Introduction identifies harvesting as a future role for robotic systems, but the paper does not identify planting or spraying as robotic tasks; its mention of spraying in the Conclusions concerns AI applications generally. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • AI and ML are being used to develop robots that can autonomously navigate and manage crops, increasing efficiency and reducing labor costs.

    “AI and ML are being used to develop robots that can autonomously navigate and manage crops, increasing efficiency and reducing labor costs.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [15]

    Section 2.3 says AI and robotics help or replace manual intervention and that robots autonomously weed, irrigate, and manage individual plants. Section 3.6 (p. 11) describes navigation and auto-steering for agricultural robots. The passages do not establish that these robots increase efficiency or reduce labor costs. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Another challenge is the need for robots to be able to operate safely and effectively in a wide range of environments.

    “Another challenge is the need for robots to be able to operate safely and effectively in a wide range of environments [15] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [15]

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Cites

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Research works printed but not linked: 24

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Authors
Linaza, M. T., Posada, J., Bund, J., Eisert, P., Quartulli, M., Döllner, J., Pagani, A., G. Olaizola, I., Barriguinha, A., Moysiadis, T., & Lucat, L.
Affiliations
  • Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), 20009 San Sebastian, Spain
  • Vicomtech (ES)
  • GraphicsVision.AI, 67663 Kaiserslautern, Germany
  • Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (DE)
  • Hasso Plattner Institute (DE)
  • German Research Centre for Artificial Intelligence (DE)
  • Agri-Ciencia, 7005-841 Évora, Portugal
  • Future Intelligence, 15341 Athens, Greece
  • Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
ORCID
Venue
Agronomy
Publisher
MDPI AG
Published
2021-06-17
Volume
11
Issue
6
Pages
1227
ISSN
2073-4395 (ISSN-L)
DOI
10.3390/agronomy11061227
OpenAlex
W3173822881
Cited by
310
References
0
Citations by year
2026: 58 · 2025: 97 · 2024: 83 · 2023: 44 · 2022: 25 · 2021: 3
FWCI
22.60
Type
journal article
Language
en
Open access
Licence
Locations
  • Agronomy (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (submittedVersion, other-oa)
  • publish.UP (University of Potsdam) (submittedVersion, cc-by)
  • Fraunhofer-Publica (Fraunhofer-Gesellschaft) (submittedVersion)
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
artificial intelligence in agriculture, precision agriculture, sustainable agriculture, agricultural decision support, crop yield, water use reduction, greenhouse gas emissions, field monitoring, autonomous agricultural robots, livestock management, data-driven agriculture

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