Crop Yield Prediction in Precision Agriculture

Dear Colleagues,

Crop yield prediction is one of the challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. The prediction of crop yield is based on soil, meteorological, environmental, and crop parameters. Decision support models are broadly used to extract significant crop features for prediction. Precision agriculture focuses on monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact.

Crop yield simulations help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, the impact of crop yield variability, and other field conditions over the growing season.

Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using "simpler" statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. The latter has the advantage of being able to handle many parameters indefinitely in time and space, i.e., big data databases created using precision management tools and data collection capabilities can be used in the areas of the meteorology, technology, and soil-related information, including characterizing different plant species. 

This Special Issue aims to discuss various yield prediction methods, the adaptation of big data, and the use of interseason databases from different platforms in crop yield forecast. Studies focused on applications regarding prediction methods, data fusion, and the adaptation of big data are invited for submission.

Prof. Dr. Miklós Neményi
Dr. Anikó Nyéki
Guest Editors

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