Data-Driven Artificial Intelligence Applications for Sustainable Precision Agriculture

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