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Machine Learning for a Smart and Sustainable Agriculture

Christophe Maudoux 1 Selma Boumerdassi 1 
1 CEDRIC - ROC - CEDRIC. Réseaux et Objets Connectés
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : Last decade has seen the emerging concept of Smart and Sustainable Agriculture that makes farming more efficient by minimizing environmental impacts. Behind this evolution, we find the scientific concept of Machine Learning. Nowadays, machine learning is everywhere throughout the whole growing and harvesting cycle. Many algorithms are used for predicting when seeds must be planted. Then, data analyses are conducted to prepare soils and determine seeds breeding and how much water is required. Finally, fully automated harvest is planned and performed by robots or unmanned vehicles with the help of computer vision. To reach these amazing results, many algorithms have been developed and implemented. This paper presents how machine learning helps farmers to increase performances, reduce costs and limit environmental impacts of human activities. Then, we describe basic concepts and the algorithms that compose the underlying engine of machine learning techniques. In the last parts we explore datasets and tools used in researches to provide cutting-edge solutions.
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Submitted on : Sunday, December 26, 2021 - 3:10:28 PM
Last modification on : Friday, August 5, 2022 - 2:54:01 PM
Long-term archiving on: : Monday, March 28, 2022 - 9:18:01 AM


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Christophe Maudoux, Selma Boumerdassi. Machine Learning for a Smart and Sustainable Agriculture. SSA 2021, Jun 2021, Virtual conference, France. pp.103-121, ⟨10.1007/978-3-030-88259-4_8⟩. ⟨hal-03502870⟩



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