The precision monitoring of film-mulched winter wheat growth facilitates field management optimization and further improves yield. Unmanned aerial vehicle (UAV) is an effective tool for crop monitoring at the field scale. However, due to the interference of background effects caused by soil and mulch, achieving accurate monitoring of crop growth in complex backgrounds for UAV remains a challenge. Additionally, the simultaneous inversion of multiple growth parameters helped us to comprehensively monitor the overall crop growth status. This study conducted field experiments including three winter wheat mulching treatments: ridge mulching, ridge–furrow full-mulching, and flat cropping full-mulching. Three machine learning algorithms (partial least squares, ridge regression, and support vector machines) and deep neural network were employed to process the vegetation indices (VIs) feature data, and the residual neural network 50 (ResNet 50) was used to process the image data. Then the two modalities (VI feature data and image data) were fused to obtain a multi-modal fusion (MMF) model. Meanwhile, a film-mulched winter wheat growth monitoring model that simultaneously predicted leaf area index (LAI), aboveground biomass (AGB), plant height (PH), and leaf chlorophyll content (LCC) was constructed by coupling multi-task learning techniques. The results showed that the image-based ResNet 50 outperformed the VI feature-based model. The MMF improved prediction accuracy for LAI, AGB, PH, and LCC with coefficients of determination of 0.73–0.92, mean absolute errors of 0.29–3.89 and relative root mean square errors of 9.48–12.99%. A multi-task MMF model with the same loss weight distribution ([1/4, 1/4, 1/4, 1/4]) achieved comparable accuracy to the single-task MMF model, improving training efficiency and providing excellent generalization to different film-mulched sample areas. The novel technique of the multi-task MMF model proposed in this study provides an accurate and comprehensive method for monitoring the growth status of film-mulched winter wheat.

覆膜冬小麦生长情况的精准监测，有利于优化田间管理，进一步提高产量。无人机（UAV）是田间作物监测的有效工具。然而，由于土壤和覆盖物造成的背景效应的干扰，无人机在复杂背景下实现作物生长的精确监测仍然是一个挑战。此外，多个生长参数的同时反演有助于我们全面监测作物的整体生长状况。本研究进行了3种冬小麦覆盖处理的田间试验：垄间覆盖、垄沟全覆盖和平作全覆盖。采用三种机器学习算法（偏最小二乘、岭回归和支持向量机）和深度神经网络来处理植被指数（VI）特征数据，并使用残差神经网络50（ResNet 50）来处理图像数据。然后将两种模态（VI特征数据和图像数据）融合以获得多模态融合（MMF）模型。同时，通过耦合多任务学习技术，构建了同时预测叶面积指数（LAI）、地上生物量（AGB）、株高（PH）和叶片叶绿素含量（LCC）的覆膜冬小麦生长监测模型。结果表明，基于图像的 ResNet 50 优于基于 VI 特征的模型。 MMF 提高了 LAI、AGB、PH 和 LCC 的预测精度，确定系数为 0.73-0.92，平均绝对误差为 0.29-3.89，相对均方根误差为 9.48-12.99%。具有相同损失权重分布 ([1/4, 1/4, 1/4, 1/4]) 的多任务 MMF 模型实现了与单任务 MMF 模型相当的精度，提高了训练效率，并为不同的覆膜样品区域。本研究提出的多任务MMF模型新技术为监测覆膜冬小麦的生长状况提供了一种准确、全面的方法。