Yield prediction and variety selection are critical components for assessing production and performance in breeding programs and precision agriculture. Since plants integrate their genetics, surrounding environments, and management conditions, crop phenotypes have been measured over cropping seasons to represent the traits of varieties. These days, UAS (unmanned aircraft system) provides a new opportunity to collect high-quality images and generate reliable phenotypic data efficiently. Here, we propose high-throughput phenotyping (HTP) from multitemporal UAS images for tomato yield estimation. UAS-based RGB and multispectral images were collected weekly and biweekly, respectively. The shape of the features of tomatoes such as canopy cover, canopy, volume, and vegetation indices derived from UAS imagery was estimated throughout the entire season. To extract time-series features from UAS-based phenotypic data, crop growth and growth rate curves were fitted using mathematical curves and first derivative equations. Time-series features such as the maximum growth rate, day at a specific event, and duration were extracted from the fitted curves of different phenotypes. The linear regression model produced high values even with different variable selection methods: all variables (0.79), forward selection (0.7), and backward selection (0.77). With factor analysis, we figured out two significant factors, growth speed and timing, related to high-yield varieties. Then, five time-series phenotypes were selected for yield prediction models explaining 65 percent of the variance in the actual harvest. The phenotypic features derived from RGB images played more important roles in prediction yield. This research also demonstrates that it is possible to select lower-performing tomato varieties successfully. The results from this work may be useful in breeding programs and research farms for selecting high-yielding and disease-/pest-resistant varieties.

中文翻译：

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基于无人飞机系统（UAS）的高通量表型（HTP）用于番茄产量估算

产量预测和品种选择是评估育种计划和精确农业中产量和表现的关键组成部分。由于植物整合了遗传学，周围环境和管理条件，因此在整个种植季节都对作物表型进行了测量，以代表品种的性状。如今，UAS（无人飞机系统）提供了收集高质量图像和有效生成可靠表型数据的新机会。在这里，我们提出了从多时相UAS图像进行番茄产量估算的高通量表型（HTP）。每周和每两周收集基于UAS的RGB和多光谱图像。从整个UAS影像中估算出西红柿的特征形状，如冠层覆盖，冠层，体积和植被指数。为了从基于UAS的表型数据中提取时间序列特征，使用数学曲线和一阶导数方程拟合了作物生长和生长速率曲线。从不同表型的拟合曲线中提取时间序列特征，例如最大增长率，特定事件的天数和持续时间。线性回归模型产生高甚至使用不同的变量选择方法也可以得到相同的值：所有变量（0.79），正向选择（0.7）和向后选择（0.77）。通过因素分析，我们得出了与高产品种有关的两个重要因素，即生长速度和时机。然后，为产量预测模型选择了五个时间序列表型，以解释实际收成中65％的方差。从RGB图像派生的表型特征在预测产量中起着更重要的作用。这项研究还表明，可以成功地选择性能较低的番茄品种。这项工作的结果可能对选育高产和抗病/害虫的品种的育种计划和研究农场有用。