Advances in Growing Degree Days Models for Flowering to Harvest: Optimizing Crop Management with Methods of Precision Horticulture—A Review

Abstract

Temperature plays a vital role in plant metabolism, and effective crop temperature appears to be influenced by variables related to climate change. While extreme weather events are widely discussed, the effects of moderate temperature changes pose consistent yet underexplored challenges for farmers. The “growing degree days” (GDD) also termed “heat unit”, is the most widely used approach in agricultural and ecological studies to quantify the relationship between temperature and plant development. This review provides a comprehensive examination of GDD methodology as applied to horticultural crop production, specifically from initial fruit development to fruit maturity, and postharvest. It is the first integrated synthesis of the conceptual evolution, methodological refinement, and broad application of GDD, thereby highlighting the need to optimize GDD approaches in light of emerging technological tools. While the GDD model is valuable for predicting crop development based on heat accumulation, it has limitations in capturing the effects of other environmental factors. Additionally, air temperature may not provide precise data on each plant organ. Recent advances in remote sensing, such as the integration of thermal imaging, RGB cameras, and lidar have enabled the measurement of spatially resolved temperature distribution within crop canopies, including fruit surface temperature. Recent advances, highlighted in the literature, suggest that integrating sensor innovations with machine learning approaches holds high potential for improving the precision of modeling temperature-dependent growth responses and their interactions with other environmental variables. By addressing these challenges and expanding its applications, GDD can continue to serve as an essential tool in promoting sustainable horticultural practices and adapting to global warming.

Plant phenotyping relevance

GDDを用いて温度から作物の発育段階・成熟を推定する方法論を中心にレビューしており、植物状態の計算的な表現型推定に該当する。

Code and data availability

This is a review article on growing degree days models with no original phenotyping measurements or computational analysis of its own. The two ATB links (temperature-annotated 3D point clouds dataset and LiDAR/thermal annotation code) appear only as cited references 183 and 191, i.e., prior work by the authors, not as,

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