Machine Intelligence for Sustainable Precision Farming

Dear Colleagues, 

Precision Farming (PF) is a concept for precisely managing inputs and actions via precision technology, in order to optimize the use of resources and labor, and to minimize the impact of farming practices on animal health, human health, and the environment. The role of sensor and actuator technology in providing accurate sensing and actuation is well known, while the necessity of machine intelligence is less well known. In modern farming, precision management is becoming increasingly supported by machines that are operated via machine intelligence, thereby closing the loop between sensing and actuation. Machine intelligence plays a crucial role in the development of economically and ecologically sustainable farming. However, developing machine intelligence for biosystems, characterized by complex dynamics, disturbances, errors, and system limitations, is one of the top challenges in Precision Farming. 

Essentially, machine intelligence for decision support and automation adopts three forms:  

• Observation of the current state of the system, to help a farmer take appropriate action. A computer may help in providing a diagnosis by observing the current state of a system (e.g., the climate inside a greenhouse, the water level in the soil, or the health status of an animal).
• Prediction of how the process will respond to input, and of the corresponding performance—for example, a prediction of yield or energy requirement based on how a climate set-point in an animal house is selected.
• Control algorithms that provide optimized schedules for input and actions with respect to specific management objectives (e.g., high yield, low resource use). They can be used to provide advice, or to fully automate a process (e.g., control of indoor climate). 

This Special Issue focuses on machine intelligence that forms the connection between sensing and actuation in Precision Farming. The scope includes the following aspects:    

• Mathematical and statistical methods for observation, prediction, and control under uncertainty and disturbances, such as adaptive modelling, state identification/recognition, machine learning, control engineering, and data assimilation.
• Improvement of sustainability with respect to animal health, economics, ecology, and resource efficiency.
• (Possible) application to a farming system—this may be any system designed for primary food production (e.g., arable farming, livestock farming, protected horticulture, or fish farming). 

Prof. Dr. Simon van Mourik
Dr. Haris Ahmad Khan
Prof. Dr. Tamás Keviczky
Dr. Duarte Antunes
Guest Editors

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