Yield monitoring is an important parameter to evaluate cotton productivity during cotton harvest. Nondestructive and accurate yield monitoring is of great significance to cotton production. Unmanned aerial vehicle (UAV) remote sensing has fast and repetitive acquisition ability. The visible vegetation indices has the advantages of low cost, small amount of calculation and high resolution. The combination of the UAV and visible vegetation indices has been more and more applied to crop yield monitoring. However, there are some shortcomings in estimating cotton yield based on visible vegetation indices only as the similarity between cotton and mulch film makes it difficult to differentiate them and yields may be saturated based on vegetation index estimates near harvest. Texture feature is another important remote sensing information that can provide geometric information of ground objects and enlarge the spatial information identification based on original image brightness. In this study, RGB images of cotton canopy were acquired by UAV carrying RGB sensors before cotton harvest. The visible vegetation indices and texture features were extracted from RGB images for cotton yield monitoring. Feature parameters were selected in different methods after extracting the information. Linear and nonlinear methods were used to build cotton yield monitoring models based on visible vegetation indices, texture features and their combinations. The results show that (1) vegetation indices and texture features extracted from the ultra-high-resolution RGB images obtained by UAVs were significantly correlated with the cotton yield; (2) The best model was that combined with vegetation indices and texture characteristics RF_ELM model, verification set R² was 0.9109, and RMSE was 0.91277 t.ha⁻¹. rRMSE was 29.34%. In conclusion, the research results prove that UAV carrying RGB sensor has a certain potential in cotton yield monitoring, which can provide theoretical basis and technical support for field cotton production evaluation.

产量监测是棉花收获期间评估棉花生产力的重要参数。无损准确的产量监测对棉花生产具有重要意义。无人机（UAV）遥感具有快速、重复的捕获能力。可见植被指数具有成本低、计算量小、分辨率高等优点。无人机和可见植被指数的结合越来越多地应用于作物产量监测。然而，仅基于可见植被指数估算棉花产量存在一些缺点，因为棉花和地膜之间的相似性使其难以区分它们，并且基于收获附近的植被指数估算的产量可能饱和。纹理特征是另一个重要的遥感信息，可以提供地物的几何信息，扩大基于原始图像亮度的空间信息识别。在这项研究中，棉花树冠的 RGB 图像是在棉花收获前通过携带 RGB 传感器的无人机获取的。从 RGB 图像中提取可见植被指数和纹理特征，用于棉花产量监测。提取信息后，采用不同的方法选择特征参数。基于可见植被指数、纹理特征及其组合，采用线性和非线性方法建立棉花产量监测模型。结果表明：（1）从无人机获得的超高分辨率RGB图像中提取的植被指数和纹理特征与棉花产量显着相关；(2) 最好的模型是结合植被指数和纹理特征的RF_ELM模型，验证集R²为 0.9109，RMSE 为 0.91277 t.ha ^-1。rRMSE 为 29.34%。综上所述，研究结果证明无人机搭载RGB传感器在棉花产量监测方面具有一定的潜力，可为大田棉花产量评价提供理论依据和技术支持。