Rain-induced cracking of sweet cherry (Prunus avium L.) fruit causes substantial economic loss to tree fruit growers annually. Increased fruit surface wetness triggers absorption of water in maturing fruits and leads to fruit cracking. This study was undertaken to develop and evaluate thermal-RGB imagery and in-field weather sensing derived wetness prediction models as tools to help mitigate cracking. We developed two cultivar-specific cherry wetness prediction models, one with only the weather data and other combined with the imagery data derived fruit surface temperature (FST). The suitability and accuracy of such models was validated for two cherry cultivars (cv. ‘Selah’ and ‘Skeena’). The FST and weather data derived model indicated an improved wetness prediction for both the cultivars. Strong relations (R² = 0.80 and 0.86) and marginal prediction errors (Root Mean Squared Error = 8.7% and 3.5%) were observed between measured and predicted wetness for ‘Selah’ and ‘Skeena’ cultivars, respectively. However, weaker relations (R²= 0.66 and 0.53) were observed, when a model for a particular cultivar was validated against other cultivar, indicating the need of cultivar specific models. Such models can be integrated with a decision support system and crop protection (e.g. rainwater removal, chemical spraying) techniques, for improved crop loss management.

雨水引起的甜樱桃（Prunus avium L.）果实开裂每年都会给果树种植者带来巨大的经济损失。果实表面湿度的增加会触发成熟果实吸收水分并导致果实开裂。进行这项研究是为了开发和评估热 RGB 图像和现场天气传感衍生的湿度预测模型，作为帮助减轻开裂的工具。我们开发了两种特定品种的樱桃湿度预测模型，一种仅包含天气数据，另一种结合图像数据得出的果实表面温度 (FST)。这些模型的适用性和准确性在两个樱桃品种 ( cv.'Selah' 和 'Skeena'）。FST 和天气数据派生模型表明两个品种的湿度预测都有所改进。 在 'Selah' 和 'Skeena' 品种的测量和预测湿度之间分别观察到强关系（R ² = 0.80 和 0.86）和边际预测误差（均方根误差 = 8.7% 和 3.5%）。然而，当针对其他品种验证特定品种的模型时，观察到较弱的关系（R ² = 0.66 和 0.53），表明需要品种特定的模型。这些模型可以与决策支持系统和作物保护（例如雨水清除、化学喷洒）技术相结合，以改进作物损失管理。