Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL + early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.

中文翻译：

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用于高通量现场表型分析和图像处理的机器学习可以深入了解木薯（Manihot esculenta Crantz）地上和地下性状的关联。

通过大量种质和多种环境进行快速无损测量来预测整个生长季节的木薯根产量是木薯育种计划中的巨大挑战。与等到收获季节不同，使用无人机 (UAV) 的多光谱图像能够测量生长周期不同时间点的冠层指标和植被指数 (VI) 特征。这种具有适当分析框架的资源丰富的时间序列航空图像处理对于从图像数据中自动提取表型特征非常重要。许多研究证明了先进遥感技术与机器学习 (ML) 方法相结合对于准确预测有价值的作物性状的有用性。到目前为止，木薯在基于航空图像的表型分析和机器学习模型测试中几乎没有受到关注。为了加速图像处理，开发了一种名为 CIAT Pheno-i 的自动图像分析框架来提取地块级植被指数/冠层指标。利用多光谱传感器获得的地面实况数据和植被指数，在木薯的不同关键生长阶段构建了多元线性回归模型。此后，结合光谱指数/特征来开发模型并使用不同的机器学习技术预测木薯根产量。我们的结果表明（1）开发的 CIAT pheno-i 图像分析框架比手动方法更容易、更快速。(2)木薯四个物候阶段的相关性分析表明，伸长期(EL)和膨化后期(LBK)是估算地上生物量(AGB)、地下生物量(BGB)和冠层高度最有用的阶段( CH）。(3)多时相分析表明，EL +早期大体积(EBK)阶段的累积图像特征信息显示绿色归一化植被指数(GNDVI)与BGB的显着相关性(r = 0.77)高于单个时间点。在地面测量的冠层高度与膨胀后期 (LBK) 阶段基于无人机 (CHuav) 的测量值 (r = 0.92) 密切相关。在不同的图像特征中，归一化差异红边指数（NDRE）数据被发现与LBK阶段的AGB始终高度相关（r = 0.65至0.84）。(4) 在本研究中使用的四种机器学习算法中，k 最近邻 (kNN)、随机森林 (RF) 和支持向量机 (SVM) 在根产量预测方面表现出最佳性能，最高准确度为 R2 = 0.67，分别为 0.66 和 0.64。本工作中描述的无人机平台、时间序列图像采集、自动图像分析框架 (CIAT Pheno-i) 和用于估计表型性状和根产量的关键植被指数 (VI) 具有作为现代木薯选择工具的巨大潜力世界各地的育种计划，以加速种质和品种选择。