Strawberries (Fragaria ×ananassa Duch.) are highly perishable fruit. Timely prediction of yield is crucial for labor management and marketing decision-making. This study demonstrates the use of high-resolution ground-based imagery, in addition to previous yield and weather information, for yield prediction throughout the season at different intervals (3–4 days, 1 week, and 3 weeks pre-harvest). Flower and fruit counts, yield, and high-resolution imagery data were collected 31 times for two cultivars (‘Florida Radiance’ and ‘Florida Beauty’) throughout the growing season. Orthorectified mosaics and digital surface models were created to extract canopy size variables (canopy area, average canopy height, canopy height standard deviation, and canopy volume) and visually count flower and fruit number. Data collected at the plot level (6 plots per cultivar, 24 plants per plot) were used to develop prediction models. Using image-based counts and canopy variables, flower and fruit counts were predicted with percentage prediction errors of 26.3% and 25.7%, respectively. Furthermore, by adding image-derived variables to the models, the accuracy of predicting out-of-sample yields at different time intervals was increased by 10–29% compared to those models without image-derived variables. These results suggest that close-range high-resolution images can contribute to yield prediction and could assist the industry with decision making by changing growers’ prediction practices.

中文翻译：

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通过在建模方法中集成地面冠层图像来改善草莓产量预测

草莓（草莓× ananassaDuch。）是极易腐烂的水果。及时预测产量对于劳动力管理和营销决策至关重要。这项研究表明，除了以前的产量和天气信息以外，还使用高分辨率的地面图像来预测整个季节中不同间隔（收获前3-4天，1周和3周）的产量。在整个生长季节，对两个品种（“佛罗里达发光”和“佛罗里达美丽”）的花朵和水果计数，产量和高分辨率图像数据进行了31次采集。创建了正交校正的镶嵌图和数字表面模型，以提取冠层大小变量（冠层面积，平均冠层高度，冠层高度标准差和冠层体积）并直观地计算花朵和果实的数量。在地块级别收集的数据（每个品种6个地块，每个地块有24株植物）用于建立预测模型。使用基于图像的计数和冠层变量，预测花朵和果实的计数分别具有26.3％和25.7％的百分比预测误差。此外，通过将图像衍生变量添加到模型中，与没有图像衍生变量的模型相比，在不同时间间隔预测样本外产量的准确性提高了10–29％。这些结果表明，近距离高分辨率图像可以有助于产量预测，并可以通过改变种植者的预测方式来帮助行业做出决策。通过将图像衍生变量添加到模型中，与没有图像衍生变量的模型相比，预测不同时间间隔的样本外产量的准确性提高了10–29％。这些结果表明，近距离高分辨率图像可以有助于产量预测，并可以通过改变种植者的预测方式来帮助行业做出决策。通过将图像衍生变量添加到模型中，与没有图像衍生变量的模型相比，预测不同时间间隔的样本外产量的准确性提高了10–29％。这些结果表明，近距离高分辨率图像可以有助于产量预测，并可以通过改变种植者的预测方式来帮助行业做出决策。