Wheat (Triticum aestivum L.) is an essential crop that is widely consumed globally. The tiller density is an important factor affecting wheat yield. Therefore, it is necessary to measure the number of tillers during wheat cultivation and breeding, which requires considerable labor and material resources. At present, there is no effective high-throughput measurement method for tiller number estimation, and the conventional tiller survey method cannot accurately reflect the spatial variation of wheat tiller density within the whole field. Therefore, in order to meet the demand for the thematic map of wheat tiller density at the field scale for the variable operation of nitrogen fertilizer, the multispectral images of wheat in Feekes growth stages 2–3 were obtained by unmanned aerial vehicle (UAV), and the characteristic parameters of the number of tillers were used to construct a model that could accurately estimate the number of tillers. Based on the vegetation index (VIs), this work proposed a gradual change features (GCFs) approach, which can greatly improve the disadvantages of using VIs to estimate tiller number, better reflect the tiller status of the wheat population, and have good results on the estimation of tiller in common models. A Lasso + VIs + GCFs method was constructed for accurate estimation of tiller number in multiple growth periods and fertilizer-treated wheat, with an average RMSE of fewer than 9 tillers per square meter, average MAE less than 8 tillers per square meter, and R² above 0.7. The results of the study not only proposed a high-throughput measurement method for the number of tillers but also provided a reference for the estimation of tiller number and other agronomic parameters.

小麦 ( Triticum aestivum L.) 是一种在全球范围内广泛消费的重要作物。分蘖密度是影响小麦产量的重要因素。因此，在小麦种植育种过程中需要对分蘖数进行测量，需要大量的人力物力资源。目前还没有有效的高通量测量方法来估计分蘖数，常规的分蘖调查方法无法准确反映全田小麦分蘖密度的空间变化。因此，为满足农田尺度小麦分蘖密度专题图对氮肥变施作业的需求，利用无人机（UAV）获得了Feekes生育期2~3期小麦的多光谱图像，并利用分蘖数的特征参数构建了能够准确估计分蘖数的模型。本工作基于植被指数（VIs），提出了渐变特征（GCFs）方法，可以大大改善使用VIs估计分蘖数的弊端，更好地反映小麦种群的分蘖状态，在常用模型中的分蘖估计。构建了 Lasso + VIs + GCFs 方法，用于准确估计多个生长期和施肥小麦的分蘖数，平均 RMSE 小于每平方米 9 分蘖，平均 MAE 小于每平方米 8 分蘖，R 本工作提出了一种渐变特征（GCFs）方法，可以大大改善使用VI估计分蘖数的缺点，更好地反映小麦群体的分蘖状态，在常用模型的分蘖估计上取得了较好的效果。构建了 Lasso + VIs + GCFs 方法，用于准确估计多个生长期和施肥小麦的分蘖数，平均 RMSE 小于每平方米 9 分蘖，平均 MAE 小于每平方米 8 分蘖，R 本工作提出了一种渐变特征（GCFs）方法，可以大大改善使用VI估计分蘖数的缺点，更好地反映小麦群体的分蘖状态，在常用模型的分蘖估计上取得了较好的效果。构建了 Lasso + VIs + GCFs 方法，用于准确估计多个生长期和施肥小麦的分蘖数，平均 RMSE 小于每平方米 9 分蘖，平均 MAE 小于每平方米 8 分蘖，R²高于 0.7。该研究结果不仅提出了一种高通量的分蘖数测量方法，而且为分蘖数等农艺参数的估算提供了参考。