A challenge in modern farming is to find a sustainable way of achieving sufficient production. Precision in dosage, timing and allocation of water, biocides, fertilizer and other inputs is essential, as are such management actions as harvesting, pruning and weeding. Despite the increasing availability of sensor and actuator technologies, decision-making is still largely left to the farmer. This is creating a strong demand for support in operational management.

This paper presents an overview of methods involving the use of technology and data to develop model-based management support and automation for productive and input-efficient farming. For each method, the main advantages and drawbacks relating to typical farm characteristics are discussed and summarized. Three case studies are presented, to illustrate the design steps involved in developing a model, observer and controller. The overall design procedure is summarized in a flowchart, and serves as a basic guide for method selection and model development.

现代农业面临的挑战是寻找实现足够产量的可持续方式。水，杀生物剂，肥料和其他投入物的剂量，时间和分配的精确度至关重要，收获，修剪和除草等管理措施也是如此。尽管传感器和执行器技术的可用性不断提高，但决策仍主要留给农民。这就产生了对运营管理支持的强烈需求。

本文概述了涉及使用技术和数据来开发基于模型的管理支持和用于生产性和投入型高效农业的自动化方法的概述。对于每种方法，都讨论并总结了与典型农场特征有关的主要优点和缺点。提出了三个案例研究，以说明开发模型，观察者和控制器所涉及的设计步骤。整个设计过程总结在流程图中，并作为方法选择和模型开发的基本指南。