Abstract Heat and light-instigated abiotic stresses during summer can cause several physiological disorders in perennial specialty crops. Such stressors increase the fruit surface temperature (FST) and prolonged exposure above a critical FST can result in sunburn. Sunburn in apple may cause considerable crop loss and reduce fresh produce marketability. Existing approaches for sunburn prediction and management, based on atmospheric temperature data, are often unreliable and inefficient for timely actuation of remedial measures. Therefore, this study focuses on the development of a non-invasive and real-time sunburn monitoring tool. We developed a crop physiology sensing (CPS) unit that uses visible-infrared imagery and in-field weather data for FST monitoring, the prime indicator of sunburn susceptibility. The CPS unit consists of a thermal-red-green-blue and all-in-one weather sensor integrated with a single-board computer. Acquired imagery data was analyzed in real-time using a custom-developed algorithm in python ‘OpenCV’ library to estimate imager-based FST. The algorithm was optimized for processing the data on a single-board computer with limited computational resources. Moreover, the processing unit was configured to acquire in-field weather data and to utilize a temperature dynamics weather model for weather-based FST estimation. Two automated CPS units were deployed in the commercial orchards of cv ‘Honeycrisp’ and ‘Cosmic Crisp™’ during the 2019 production season. For each cultivar, field data was collected for three days between 12 and 5 pm at 5-minute intervals. A contact type thermal probe of accuracy ±0.4 °C was also utilized for ground truth apple FST (FSTa) measurements. Furthermore, imagery data was analyzed to derive mean FST (FSTi), maximum FST (FSTi-max), and mean FST of the 10%, 15% and 20% hottest part of the fruit surface (i.e. FST10, FST15, and FST20, respectively). The results showed significant differences between FSTi, FSTi-max, FST10, FST15, and FST20 for Honeycrisp (F4,162 = 73.4, p

中文翻译：

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田间作物生理传感辅助实时苹果果实表面温度监测以预测晒伤

摘要 夏季热和光引发的非生物胁迫会导致多年生特种作物出现多种生理障碍。此类压力因素会增加水果表面温度 (FST)，长时间暴露在临界 FST 以上会导致晒伤。苹果的晒伤可能会导致相当大的作物损失并降低新鲜农产品的适销性。现有的基于大气温度数据的晒伤预测和管理方法通常不可靠且效率低下，无法及时采取补救措施。因此，本研究的重点是开发一种非侵入性的实时晒伤监测工具。我们开发了一种作物生理传感 (CPS) 单元，该单元使用可见红外图像和田间天气数据进行 FST 监测，这是晒伤敏感性的主要指标。CPS 单元由一个与单板计算机集成的热-红-绿-蓝和多合一天气传感器组成。使用 Python 'OpenCV' 库中的自定义开发算法实时分析获取的图像数据，以估计基于成像器的 FST。该算法针对在计算资源有限的单板计算机上处​​理数据进行了优化。此外，处理单元被配置为获取现场天气数据并利用温度动态天气模型进行基于天气的 FST 估计。在 2019 年的生产季节，在 cv 'Honeycrisp' 和 'Cosmic Crisp™' 的商业果园中部署了两个自动化 CPS 装置。对于每个栽培品种，在下午 12 点到 5 点之间以 5 分钟的间隔收集田间数据，持续三天。精度±0的接触式热探头。4 °C 也用于地面实况苹果 FST (FSTa) 测量。此外，对图像数据进行分析，以推导出水果表面 10%、15% 和 20% 最热部分（即 FST10、FST15 和 FST20，分别）。结果表明，蜜脆的 FSTi、FSTi-max、FST10、FST15 和 FST20 之间存在显着差异（F4,162 = 73.4，p