A canopy photosynthesis model based on a highly generalizable artificial neural network incorporated with a mechanistic understanding of single-leaf photosynthesis

Abstract

Crop productivity is largely dependent on canopy photosynthesis, which is difficult to measure at farming sites. Therefore, real-time estimation of the canopy photosynthetic rate (Ac) is expected to facilitate effective farm management. For the estimation of Ac, two types of mathematical models (i.e., process-based models and empirical models) have been used, although both types have their own weaknesses. Process-based models inevitably require many model parameters that are difficult to identify, while empirical models, including artificial neural network (ANN) models, have a low predictive ability outside of the range of training datasets. To overcome these weaknesses, we developed a hybrid canopy photosynthesis model that included components of both process-based models and ANN models. In this hybrid model, the single-leaf photosynthetic rate (AL) and leaf area index (LAI) were first estimated from information easily obtainable at farming sites: AL was estimated by the process-based model of AL (i.e., the biochemical photosynthesis model of Farquhar et al. (1980)) from environmental data (photosynthetic photon flux density (PPFD), air temperature (Tₐ), humidity, and atmospheric CO₂ concentration (Ca)), and the LAI was estimated by an analysis of crop canopy imagery. As highly explainable information for Ac, the estimated AL and LAI were input into the ANN model to estimate Ac. As such, the ANN model learned the logical relationships between the inputs (AL and LAI) and the output (Ac). Detailed validation analysis using nine spinach Ac datasets revealed that the hybrid ANN model can estimate Ac accurately throughout the whole growth period, even when training and test datasets were obtained in different seasons under different CO₂ concentrations and based on training datasets of only three days. This study highlights the high generalizability of the hybrid ANN model, which is a prerequisite for practical application in environmentally controlled crop production.

Plant phenotyping relevance

作物キャノピー光合成速度を推定するハイブリッドモデルを開発し、キャノピー画像からのLAI推定と光合成速度推定を詳細に検証しており、表現型取得・推定手法が中心である。

Code and data availability

The supplied blocks describe greenhouse canopy photosynthesis measurements and hybrid ANN modeling, but contain no public phenotype dataset, image/sensor data deposit, author analysis code, or trained model release. The only availability note is generic supplementary material via the article DOI, with no explicit data-

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