Hybrid AI model for estimating the canopy photosynthesis of eggplants
Hybrid AI model for estimating the canopy photosynthesis of eggplants
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DOI：
10.1007/s11120-022-00974-z
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发表时间：
2022-10
影响因子：
3.7
通讯作者：
K. Nomura;T. Kaneko;T. Iwao;M. Kitayama;Yudai Goto;M. Kitano
中科院分区：
文献类型：
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作者：
K. Nomura;T. Kaneko;T. Iwao;M. Kitayama;Yudai Goto;M. Kitano
现代估算冠层光合速率（Ac）的模型大致可分为两类，即基于过程的机制模型和人工智能（AI）模型，每一类都有其独特的优势（即基于过程的模型具有广泛的通用性，AI模型可以使用数据再现复杂的过程，而无需事先了解潜在的机制）。为了利用这两类模型的优势，提出了一种将基于过程的模型与人工智能模型相结合的新型“混合”冠层光合作用模型。在该混合模型中，基于过程的单叶光合作用和图像分析模型首先将原始输入（环境数据和冠层图像）转换为单叶光合速率（AL）和有效叶面积指数（Lc），然后将halandlcare送入人工神经网络（ANN）模型进行预测。该混合模型在很小的训练数据集上成功地预测了茄子冠层的日循环，并成功地再现了对训练数据范围外co2浓度变化的典型反应。本文提出的混合人工智能模型可以为难以获得大量训练数据的实际农田估计提供一种有效的手段。
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 whichALandLcare fed into an artificial neural network (ANN) model to predictAc. The hybrid model successfully predicted the diurnal cycles ofAcof an eggplant canopy even with a small training dataset and successfully reproduced a typicalAcresponse to changes in the CO2concentration outside the range of the training data. The proposed hybrid AI model can provide an effective means to estimateAcin actual crop fields, where obtaining a large amount of training data is difficult.