Crop yield prediction is of great practical significance for farmers to make reasonable decisions, such as decisions on crop insurance, storage demand, cash flow budget, fertilizer, water and other input factors. The traditional yield measurement method is sampling surveys, which require a large area of destructive sampling of cotton fields and consume considerable time and labor costs. This study established a cotton yield estimation model based on time series Unmanned Aerial Vehicle (UAV) remote sensing data. The U-Net semantic segmentation network is used to recognize and extract the boll opening pixels in high-resolution visible images, and the boll opening pixel percentage (BOP) is calculated according to the network extraction results. By combining the multispectral images and the pixel coverage of cotton bolls, a Bayesian regularization BP (back propagation) neural network was used to predict cotton yields. In order to simplify the input parameters of the model, the stepwise sensitivity analysis method is used to eliminate redundant variables and obtain the optimal input feature set. The experimental results show that the R² of the proposed model is 0.853 at the scale of 0.81 m² (average results of ten-fold cross validation). This study provides a method that can simultaneously meet the requirements of large-area and small-scale forecasting of cotton yields and provides a new idea for cotton yield measurement and breeding screening.

作物产量预测对于农民做出合理决策具有重要的现实意义，如作物保险、仓储需求、现金流预算、肥料、水等投入因素的决策。传统的产量测量方法是抽样调查，需要对棉田进行大面积的破坏性抽样，耗费大量的时间和人力成本。本研究建立了基于时间序列无人机（UAV）遥感数据的棉花产量估算模型。U-Net语义分割网络用于在高分辨率可见光图像中识别和提取棉铃开口像素，并根据网络提取结果计算棉铃开口像素百分比（BOP）。通过结合多光谱图像和棉铃的像素覆盖，使用贝叶斯正则化 BP（反向传播）神经网络来预测棉花产量。为了简化模型的输入参数，采用逐步灵敏度分析的方法消除冗余变量，得到最优输入特征集。实验结果表明，R在 0.81 m ²的尺度下，所提出模型的²为 0.853 （十倍交叉验证的平均结果）。本研究提供了一种可以同时满足大面积和小规模棉花产量预测要求的方法，为棉花产量测量和育种筛选提供了新的思路。