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.
Highlights
The information of interest in the agricultural sector consists of traits or features of systems that vary in space and time
The results show that no specific conclusion can be drawn as to what the best model is, but they clearly show that some machine learning (ML) models such as the random forest, neural networks, linear regression, and gradient boosting tree are used more than the others
Low replicability and the corresponding difficulty in systematic data gathering is a key challenge in agriculture, as no two fields are exactly alike
Summary
The information of interest in the agricultural sector consists of traits or features of systems that vary in space and time. Agriculture is one of the most difficult fields for statistical quantification. Even within a single field, conditions are always changing from one section to the next. The weather is hard to predict, the quality of the soil changes and there is always the possibility of pests and diseases. Many of these traits have been managed by the own experience and expertise of the farmers. They may think the prospects for an upcoming harvest are good, the outcome is always uncertain until the harvest day arrives
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