Unoccupied aerial system (UAS) imagery may serve as an additional tool towards management zone delineation. This is because UAS data collection is relatively flexible. However, it is unclear how useful UASs can be towards generating management zones, relative to preexisting tools (e.g. apparent soil electrical conductivity or EC_a). The purpose of this study, therefore, was to evaluate UAS imagery, relative to EC_a, in terms of their ability to: 1) predict cotton traits (i.e. height, seed cotton yield), and 2) define cotton management zones based on these traits. Single-season UAS images from multispectral/thermal sensors were collected and processed into Normalized Difference Vegetation Index (NDVI) and radiometric surface temperature (T_r), respectively. Management zones were also delineated using digital camera (RGB) imagery collected at periods before planting and near harvest. RGB management zones were delineated by a novel open boll mapping approach. In-season NDVI and T_r layers were significant (P < 0.01) predictors of canopy height. Additionally, NDVI and T_r maps produced statistically different management zones during flowering and boll filling growth stages in terms of yield (P = 0.001 or less). Open boll layers were all more accurate predictors of cotton seed yield than EC_a data—these two layers also produced statistically distinct management zones. ANOVA tests revealed that, given EC_a alone, adding UAS information via the RGB open boll map resulted in a significantly different yield prediction model (P < 0.001). These results suggest that UAS imagery can offer valuable information for cotton management zone delineation that other techniques cannot.

无人航空系统 (UAS) 图像可作为管理区划定的附加工具。这是因为 UAS 数据收集相对灵活。然而，相对于先前存在的工具（例如表观土壤电导率或 EC _a），UAS 对生成管理区的有用程度尚不清楚。因此，本研究的目的是评估 UAS 图像相对于 EC _a的能力：1) 预测棉花性状（即高度、籽棉产量），以及 2) 基于这些特性定义棉花管理区性状。来自多光谱/热传感器的单季 UAS 图像被收集并处理成归一化差异植被指数 (NDVI) 和辐射测量表面温度 (T _r）， 分别。还使用在种植前和临近收获期间收集的数码相机 (RGB) 图像划定了管理区域。RGB 管理区由一种新颖的开放棉铃映射方法划定。季节性 NDVI 和 T _r层是冠层高度的显着（P < 0.01）预测因子。此外，就产量而言，NDVI 和 T _r地图在开花和棉铃灌浆生长阶段产生了统计上不同的管理区域（P = 0.001 或更低）。开放的棉铃层都是比 EC _a数据更准确的棉花种子产量预测因子——这两个层也产生了统计上不同的管理区。ANOVA 检验表明，给定 EC _a单独，通过 RGB 开放棉铃图添加 UAS 信息导致产量预测模型显着不同（P < 0.001）。这些结果表明 UAS 图像可以为棉花管理区划定提供其他技术无法提供的有价值的信息。