In this research a machine learning framework was developed for cotton yield estimation using multi-temporal remote sensing data collected from unmanned aircraft system (UAS). The proposed machine learning model was based on an artificial neural network (ANN) and used three types of crop features derived from UAS data to predict the yield, namely; multi-temporal features including canopy cover, canopy height, canopy volume, normalized difference vegetation index (NDVI), excessive greenness index (ExG); non-temporal features including cotton boll count, boll size and boll volume, and irrigation status as a qualitative feature. The model provided low residual values with predicted yield values close to the observed yield values (R² ~ 0.9). ANN model performance was compared with support vector regression (SVR) and random forest regression (RFR). Comparison results revealed that ANN model outperforms SVR and RFR. Additionally, redundant features were removed using correlation analysis, and an optimal subset of features was obtained that included canopy volume, ExG, boll count, boll volume and irrigation status. Moreover, the relative significance of each feature in the optimal input feature subset was determined using sensitivity analysis. It was found that canopy volume and ExG contributed around 50% towards the corresponding yield. Finally, further analysis was performed to investigate how early in the growing season the model can accurately predict yield. It was observed that even at 70 days after planting the model predicted yield with reasonable accuracy (R² of 0.72 over test set). This study revealed that UAS derived multi-temporal data along with non-temporal and qualitative data can be combined within a machine learning framework to provide a reliable estimation of crop yield and provide effective understanding for crop management.

在这项研究中，使用从无人机系统（UAS）收集的多时相遥感数据，开发了一种用于棉花产量估算的机器学习框架。所提出的机器学习模型基于人工神经网络（ANN），并使用从UAS数据得出的三种农作物特征来预测产量，即：多时相特征，包括冠层覆盖，冠层高度，冠层体积，归一化植被指数（NDVI），过度绿色指数（ExG）；非临时性特征，包括棉铃数，棉铃大小和棉铃体积以及灌溉状况作为定性特征。该模型提供了较低的残差值，且预测的收益率值接近观察到的收益率值（R ² 〜0.9）。将ANN模型的性能与支持向量回归（SVR）和随机森林回归（RFR）进行了比较。比较结果表明，人工神经网络模型优于SVR和RFR。另外，使用相关分析去除了多余的特征，并获得了特征的最佳子集，其中包括树冠体积，ExG，棉铃数，棉铃体积和灌溉状况。此外，使用灵敏度分析确定了最佳输入特征子集中每个特征的相对重要性。发现冠层体积和ExG贡献了约50％的相应产量。最后，进行了进一步的分析，以研究该模型在生长季节的早期能够准确地预测产量。据观察，即使在播种后70天，该模型仍以合理的精度预测产量（R测试集超过0.72中的²）。这项研究表明，UAS衍生的多时间数据以及非时间和定性数据可以在机器学习框架内进行组合，以提供可靠的作物产量估算并为作物管理提供有效的了解。