In-season crop growth and yield prediction at high spatial resolution are essential for informing decision-making for precise crop management, logistics and market planning in horticultural crop production. This research aimed to establish a plant-level cabbage yield prediction system by assimilating the leaf area index (LAI) estimated from UAV imagery and a segmentation model into a crop simulation model, the WOrld FOod STudies (WOFOST). The data assimilation approach was applied for one cultivar in five fields and for another cultivar in three fields to assess the yield prediction accuracy and robustness. The results showed that the root mean square error (RMSE) in the prediction of cabbage yield ranged from 1,314 to 2,532 kg ha^–1 (15.8–30.9% of the relative RMSE). Parameter optimisation via data assimilation revealed that the reduction factor in the gross assimilation rate was consistently attributed to a primary yield-limiting factor. This research further explored the effect of reducing the number of LAI observations on the data assimilation performance. The RMSE of yield was only 107 kg ha^–1 higher in the four LAI observations obtained from the early to mid-growing season than for the nine LAI observations over the entire growing season for cultivar ‘TCA 422’. These results highlighted the great possibility of assimilating UAV-derived LAI data into crop simulation models for plant-level cabbage yield prediction even with LAI observations only in the early and mid-growing seasons.

在高空间分辨率下进行季节性作物生长和产量预测对于为园艺作物生产中的精确作物管理、物流和市场规划提供决策信息至关重要。本研究旨在通过将从无人机图像和分割模型估计的叶面积指数 （LAI） 同化到作物模拟模型 WOrld FOod STudies （WOFOST） 中，建立植物级卷心菜产量预测系统。数据同化方法应用于 5 块田地的一个品种和 3 块田地的另一个品种，以评估产量预测的准确性和稳健性。结果表明，预测卷心菜产量的均方根误差 （RMSE） 范围为 1,314 至 2,532 kg ha^–1（相对 RMSE 的 15.8-30.9%）。通过数据同化进行参数优化表明，总同化率的降低因子始终归因于主要的产量限制因子。本研究进一步探讨了减少 LAI 观察次数对数据同化性能的影响。在生长季节早期到中期获得的 4 次 LAI 观测中，产量的 RMSE 仅比品种“TCA 422”在整个生长季节的 9 次 LAI 观测值高 107 kg ^ha-1。这些结果强调了将无人机衍生的 LAI 数据吸收到作物模拟模型中用于植物水平卷心菜产量预测的巨大可能性，即使仅在生长早期和中期进行 LAI 观测也是如此。