Crop Growth Modeling-a New Data-driven Approach

Abstract

Deficit irrigation strategies have been employed in mitigation of challenges related to efficient water use in crop production. Various models have been developed to represent the behavior of plants under water stress conditions. This work presents a state-machine-based model that defines plant behavior in terms of states and transitions which are determined by both current and historical water status of the plant. The model is employed in prediction of the growth of maize plants under different irrigation treatments during the vegetative stage. The new approach provides an accurate estimation of the growth performance of maize plants during the early vegetative phase, allowing it to be used in evaluation of effects of different irrigation treatments and in design of irrigation-based plant control.