Hybrid AI model for estimating the canopy photosynthesis of eggplants.

Abstract

Modern models for estimating canopy photosynthetic rates (Ac) can be broadly classified into two categories, namely, process-based mechanistic models and artificial intelligence (AI) models, each category having unique strengths (i.e., process-based models have generalizability to a wide range of situations, and AI models can reproduce a complex process using data without prior knowledge about the underlying mechanism). To exploit the strengths of both categories of models, a novel "hybrid" canopy photosynthesis model that combines process-based models with an AI model was proposed. In the proposed hybrid model, process-based models for single-leaf photosynthesis and image analysis first transform raw inputs (environmental data and canopy images) into the single-leaf photosynthetic rate (AL) and effective leaf area index (Lc)), after which AL and Lc are fed into an artificial neural network (ANN) model to predict Ac. The hybrid model successfully predicted the diurnal cycles of Ac of an eggplant canopy even with a small training dataset and successfully reproduced a typical Ac response to changes in the CO2 concentration outside the range of the training data. The proposed hybrid AI model can provide an effective means to estimate Ac in actual crop fields, where obtaining a large amount of training data is difficult.