Li Yun-Fan, Wu Chen, Jia Hong-Mei, Chen Xi, Xing Jin-Niu, Gao Wei-Ping, Yan Zhu-Yun
State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
School of Pharmacy/School of Modern Chinese Medicine Industry, Chengdu University of Traditonal Chinese Medicine, Chengdu, China.
PeerJ. 2025 Apr 7;13:e19264. doi: 10.7717/peerj.19264. eCollection 2025.
Accurate predicting the yield and quality of medicinal materials before harvest can effectively guide post-harvest process, including processing and storage, thereby ensuring the final quality of medicinal materials. Currently, traditional experimental methods for yield and quality estimation are inadequate to offer reliable guidance for harvesting and processing of medicinal plan. Uncrewed aerial vehicle (UAV) multispectral can quickly and accurately estimate the yield and quality of field crops. Based on the UAV multispectral data of Hort. obtained about half a month before and near harvest, this study predicted the rhizome yield and the content of active components such as ferulic acid, Z-ligustilide and senkyunolide A. Additionally, the quality discriminant models of chuanxiong rhizoma were constructed according to the ferulic acid content index stipulated in Pharmacopoeia of the People's Republic of China (2020). The results performed on the independent validation set show that the best prediction effects of fresh weight and dry weight of rhizome were NRMSE = 23.76%, MAPE = 14.75% and NRMSE = 34.65%, MAPE = 21.73%, respectively. And the best predictive effects of ferulic acid, Z-ligustilide and senkyunolide A were as follows: NRMSE = 13.35%, MAPE = 10.25%; NRMSE = 34.35%, MAPE = 23.40%; and NRMSE = 45.26%, MAPE = 25.48%. Furthermore, the quality discriminant models XGBoost and AdaBoost had effective performances (Accuracy = 0.7083, AUC = 0.7214). These results suggest that UAV multispectral can be effectively employed to predict both yield and quality before harvest, thereby guiding the harvest and processing of . .
收获前准确预测药用植物的产量和质量能够有效指导收获后的加工和储存等过程，从而确保药材的最终质量。目前，传统的产量和质量估算实验方法不足以对药用植物的收获和加工提供可靠指导。无人机多光谱技术能够快速、准确地估算田间作物的产量和质量。基于收获前约半个月和临近收获时获取的川芎无人机多光谱数据，本研究预测了根茎产量以及阿魏酸、Z-藁本内酯和川芎内酯A等活性成分的含量。此外，根据《中华人民共和国药典》（2020年版）规定的阿魏酸含量指标构建了川芎的质量判别模型。在独立验证集上的结果表明，根茎鲜重和干重的最佳预测效果分别为：NRMSE = 23.76%，MAPE = 14.75%和NRMSE = 34.65%，MAPE = 21.73%。阿魏酸、Z-藁本内酯和川芎内酯A的最佳预测效果如下：NRMSE = 13.35%，MAPE = 10.25%；NRMSE = 34.35%，MAPE = 23.40%；以及NRMSE = 45.26%，MAPE = 25.48%。此外，XGBoost和AdaBoost质量判别模型具有有效性能（准确率 = 0.7083，AUC = 0.7214）。这些结果表明，无人机多光谱技术可有效用于收获前预测产量和质量，从而指导川芎的收获和加工。
