Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture

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 field...

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Published inAgronomy (Basel) Vol. 11; no. 6; p. 1227
Main Authors Linaza, Maria Teresa, Posada, Jorge, Bund, Jürgen, Eisert, Peter, Quartulli, Marco, Döllner, Jürgen, Pagani, Alain, G. Olaizola, Igor, Barriguinha, Andre, Moysiadis, Theocharis, Lucat, Laurent
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2021
Subjects
Agricultural practices
Agricultural production
Agriculture
agronomy
Algorithms
Artificial intelligence
Automation
Climate change
computer vision
Crop diseases
Crops
data analysis
Data collection
Decision making
Digitization
Farm management
Farmers
Farms
Fertilizers
Food
Greenhouse gases
Harvest
Livestock
livestock husbandry
Optimization
Pesticides
Plant diseases
Precision agriculture
Research projects
Robotics
Sensors
soil
Sustainable development
Trends
Water use
Weather
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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.
AbstractList 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.
Author Quartulli, Marco
Pagani, Alain
Lucat, Laurent
Döllner, Jürgen
Barriguinha, Andre
Moysiadis, Theocharis
Linaza, Maria Teresa
Posada, Jorge
Eisert, Peter
G. Olaizola, Igor
Bund, Jürgen
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Snippet One of the main challenges for the implementation of artificial intelligence (AI) in agriculture includes the low replicability and the corresponding...
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StartPage 1227
SubjectTerms Agricultural practices
Agricultural production
Agriculture
agronomy
Algorithms
Artificial intelligence
Automation
Climate change
computer vision
Crop diseases
Crops
data analysis
Data collection
Decision making
Digitization
Farm management
Farmers
Farms
Fertilizers
Food
Greenhouse gases
Harvest
Livestock
livestock husbandry
Optimization
Pesticides
Plant diseases
Precision agriculture
Research projects
Robotics
Sensors
soil
Sustainable development
Trends
Water use
Weather
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Title Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture
URI https://www.proquest.com/docview/2544556054
https://www.proquest.com/docview/2636406720
https://doaj.org/article/eac071940c964e14890490ad219cfb3f
Volume 11
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