Hybrid AI model for estimating the canopy photosynthesis of eggplants

Modern models for estimating canopy photosynthetic rates (A_c) 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 (A_L) and effective leaf area index (L_c)), after which A_L and L_c are fed into an artificial neural network (ANN) model to predict A_c. The hybrid model successfully predicted the diurnal cycles of A_c of an eggplant canopy even with a small training dataset and successfully reproduced a typical A_c 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 A_c in actual crop fields, where obtaining a large amount of training data is difficult.