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.

中文翻译：

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面向可持续精准农业的数据驱动人工智能应用

在农业中实施人工智能 (AI) 的主要挑战之一包括低可复制性和相应的系统数据收集难度，因为没有两个领域是完全相同的。因此，在不同领域、天气条件和耕作技术的几个试点实验的比较增强了集体知识。因此，这项工作以作者在几个欧洲国家实施和验证的研究项目的形式对最近的研究活动进行了总结，目的是展示已经取得的成果、当前的调查和仍然存在的技术挑战。作为一个总体结论，可以提到的是，即使在某些情况下处于初级阶段，人工智能技术也可以改善农场层面的决策支持，监测条件并优化生产，使农民能够为每种作物应用最佳数量的投入，从而提高产量并减少用水量和温室气体排放。这项工作的未来扩展将包括基于自主和智能机器人的新概念，用于植物和土壤样本检索，以及有效的牲畜管理。