Remote sensing techniques and the use of Unmanned Aerial Systems (UAS) have simplified the estimation of yield and plant health in many crops. Family selection in sugarcane breeding programs relies on weighed plots at harvest, which is a labor-intensive process. In this study, we utilized UAS-based remote sensing imagery of plant-cane and first ratoon crops to estimate family yields for a second ratoon crop. Multiple families from the commercial breeding program were planted in a randomized complete block design by family. Standard red, green, and blue imagery was acquired with a commercially available UAS equipped with a Red–Green–Blue (RGB) camera. Color indices using the CIELab color space model were estimated from the imagery for each plot. The cane was mechanically harvested with a sugarcane combine harvester and plot weights were obtained (kg) with a field wagon equipped with load cells. Stepwise regression, correlations, and variance inflation factors were used to identify the best multiple linear regression model to estimate the second ratoon cane yield (kg). A multiple regression model, which included family, and five different color indices produced a significant R² of 0.88. This indicates that it is possible to make family selection predictions of cane weight without collecting plot weights. The adoption of this technology has the potential to decrease labor requirements and increase breeding efficiency.

中文翻译：

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基于遥感影像的再生甘蔗家族产量预测与筛选

遥感技术和无人机系统 (UAS) 的使用简化了对许多作物产量和植物健康的估计。甘蔗育种计划中的家庭选择依赖于收获时称重的地块，这是一个劳动密集型过程。在这项研究中，我们利用基于 UAS 的植物甘蔗和第一批宿根作物的遥感图像来估计第二批宿根作物的家庭产量。来自商业育种计划的多个家庭以家庭的随机完整区组设计进行种植。标准的红色、绿色和蓝色图像是使用配备红-绿-蓝 (RGB) 相机的市售 UAS 获取的。使用 CIELab 颜色空间模型的颜色指数是根据每个图的图像估计的。甘蔗用甘蔗联合收割机机械收割，用配备称重传感器的田间货车获得地块重量（kg）。逐步回归、相关性和方差膨胀因子用于确定最佳多元线性回归模型，以估计第二根宿根甘蔗产量 (kg)。包含家庭和五个不同颜色指数的多元回归模型产生了显着的 R²的 0.88。这表明可以在不收集地块权重的情况下对甘蔗重量进行家族选择预测。采用这项技术有可能减少劳动力需求并提高育种效率。