# Prediction of yield and quality in medicinal plant Ligusticum chuanxiong Hort. using uncrewed aerial vehicle multispectral measurement

## TL;DR

This study uses drone-based multispectral imaging to predict the yield and quality of a medicinal plant before harvest, improving processing and storage decisions.

## Contribution

The novel use of UAV multispectral data to predict yield and active compound content in Ligusticum chuanxiong Hort. before harvest.

## Key findings

- UAV multispectral data achieved NRMSE of 23.76% for fresh weight and 34.65% for dry weight prediction.
- Ferulic acid prediction reached NRMSE of 13.35%, while senkyunolide A had NRMSE of 45.26%.
- Quality discriminant models XGBoost and AdaBoost achieved accuracy of 0.7083 and AUC of 0.7214.

## Abstract

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 Ligusticum chuanxiong 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 L. chuanxiong.