Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders.

ABSTRACT: Crop models have been developed for wide research purposes and scales, but they have low compatibility due to the diversity of current modeling studies. Improving model adaptability can lead to model integration. Since deep neural networks have no conventional modeling parameters, diverse input and output combinations are possible depending on model training. Despite these advantages, no process-based crop model has been tested in full deep neural network complexes. The objective of this study was to develop a process-based deep learning model for hydroponic sweet peppers. Attention mechanism and multitask learning were selected to process distinct growth factors from the environment sequence. The algorithms were modified to be suitable for the regression task of growth simulation. Cultiva

SUBMITTER: Moon T 

PROVIDER: S-EPMC10202189 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature