Quantifying canopy biophysical parameters is critical to agricultural research and farm management. In this study, strawberry dry biomass and leaf area were modeled statistically using high spatial and temporal resolution imagery. A mobile field data acquisition system was used to acquire thousands of very high resolution (~0.5 mm) close-range images seven times throughout the strawberry growing season. Ortho-mosaics and dense point clouds were generated through Structure from Motion (SfM) and used in Object-Based Image Analysis (OBIA) at the sub-leaf level to extract canopy structure variables such as planimetric canopy area, canopy average height, and canopy smoothness metric. Regression analysis was carried out using these image-derived canopy variables as predictors to model leaf area ($R^2$ = 0.79; ten-fold cross-validation RMSE = 0.056 m²) and dry biomass ($R^2$ = 0.84; ten-fold cross-validation RMSE = 7.72 g) obtained through destructive measurements. Results indicate consistent predictive power through the season and across 17 strawberry genotypes. The study showed that the canopy smoothness metric developed in this study as an indicator of canopy density could complement other variables (planimetric canopy area, canopy average height) that describe canopy geometric properties.

量化冠层生物物理参数对农业研究和农场管理至关重要。在这项研究中，草莓干生物量和叶面积使用高空间和时间分辨率图像进行统计学建模。在整个草莓生长期中，使用了移动现场数据采集系统来七次采集数千个高分辨率（〜0.5 mm）的近距离图像。通过运动结构（SfM）生成正交马赛克和密集点云，并将其用于子叶级的基于对象的图像分析（OBIA）中以提取冠层结构变量，例如平面冠层面积，冠层平均高度和冠层平滑度指标。使用这些图像来源的冠层变量作为模型叶面积的预测因子进行回归分析（${\mathrm{[R}}^2$ = 0.79; 十倍交叉验证RMSE = 0.056 m ²）和干燥生物质（${\mathrm{[R}}^2$ = 0.84; 通过破坏性测量获得的十倍交叉验证（RMSE = 7.72 g）。结果表明，整个季节和17种草莓基因型都具有一致的预测能力。研究表明，这项研究中开发的冠层平滑度度量可以作为冠层密度的指标，可以补充描述冠层几何特性的其他变量（平面冠层面积，冠层平均高度）。