In response to the increasing global demand for food, feed, fiber, and fuel, digital agriculture is rapidly evolving to meet these demands while reducing environmental impact. This evolution involves incorporating data science, machine learning, sensor technologies, robotics, and new management strategies to establish a more sustainable agricultural framework. So far, machine learning research in digital agriculture has predominantly focused on model-centric approaches, focusing on model design and evaluation. These efforts aim to optimize model accuracy and efficiency, often treating data as a static benchmark. Despite the availability of agricultural data and methodological advancements, a saturation point has been reached, with many established machine learning methods achieving comparable levels of accuracy and facing similar limitations. To fully realize the potential of digital agriculture, it is crucial to have a comprehensive understanding of the role of data in the field and to adopt data-centric machine learning. This involves developing strategies to acquire and curate valuable data and implementing effective learning and evaluation strategies that utilize the intrinsic value of data. This approach has the potential to create accurate, generalizable, and adaptable machine learning methods that effectively and sustainably address agricultural tasks such as yield prediction, weed detection, and early disease identification

翻译：为应对全球对粮食、饲料、纤维和燃料日益增长的需求，数字化农业正在快速发展，在满足这些需求的同时减少环境影响。这一演变涉及整合数据科学、机器学习、传感器技术、机器人技术以及新的管理策略，以建立更可持续的农业框架。迄今为止，数字化农业中的机器学习研究主要侧重于以模型为中心的方法，专注于模型设计与评估。这些努力旨在优化模型的准确性和效率，往往将数据视为静态基准。尽管农业数据可用且方法学有所进步，但已趋近饱和点，许多成熟的机器学习方法已达到相当的准确度水平，并面临相似的局限性。要充分实现数字化农业的潜力，关键在于全面理解数据在该领域中的作用，并采用以数据为中心的机器学习。这包括制定获取和整理有价值数据的策略，以及实施利用数据内在价值的有效学习与评估策略。该方法有潜力创建准确、可泛化且适应性强的机器学习方法，从而有效且可持续地解决农业生产任务，如产量预测、杂草检测和早期病害识别。