IP4: Forecasting Phenotypes based on Management Decisions

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Tactical management decisions that concern the day-to-day management of the cropping system like watering, fertilizing, or thinning, have a large impact on the growth of the plants and the quantity and quality of fruits for horticultural crops. In order to support decision makers like farmers, this project aims to develop AI approaches to forecast the future growth of plants based on different tactical management decisions. In this way, the AI models provide a better understanding how tactical management decisions will affect the yield of a horticultural crop. This will be done by forecasting phenotypic traits like height of plants or the quantity and size of fruits that are relevant for the decision maker. For forecasting, we will focus on deep neural networks that forecast phenotypes from sequences of consistent geometric-semantic models of plants, while taking past and planned tactical management decisions into account. Since the amount of tactical management decisions that can be made every day is very large, we also aim to develop a network that is able to propose future tactical management decisions by itself. While the focus of this project is on developing new approaches, the developed techniques have the potential to become powerful tools for agricultural decision makers.