Onsite age discrimination of an endangered medicinal and aromatic plant species Valeriana jatamansi using field hyperspectral remote sensing and machine learning techniques

ABSTRACT

Valeriana jatamansi Jones is an aromatic herb well known for its essential oil contents, and its high medicinal and commercial values. The amount of essential oils present in it increases with maturity (age) of the plant. In this study, Hyperspectral remote sensing data recorded in the field using Analytical Spectral Devices (ASD) handheld spectroradiometer was used to discriminate the age (6, 12, 24 and 36 months) of V. jatamansi. Principal Component Analysis (PCA) was used for feature selection and 06 machine learning classifiers were used to classify the plant based on their ages, i.e., Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), Boosting Decision Tree (BDT), Decision Tree (DT) and k-Nearest Neighbourhood (kNN). For comparison, these classifiers were applied on full range of spectral reflectance data without feature selection and on feature-selected data using PCA. It was found that the accuracies of ANN, RF, BDT, SVM, DT and kNN were 91, 85, 57, 78, 35 and 42%, respectively for non-feature selected datasets. The accuracies of ANN and DT classifiers were, respectively, increased by 100% and 75% after applying PCA. The ANN classifiers resulted in 100% overall accuracy with a Kappa coefficient (K) of 1. The wavelength regions 860, 870 to 874, 876 to 885 nm in near-infrared (NIR), and 747 to 756 nm (red-edge) were identified as regions suitable for discriminate the age groups of V. jatamansi. The final trained model thus prepared was again validated on 60 plants (with different age group) grown in its natural habitat and the obtained accuracy was 88% (K = 0.84). Thus, the present study have provided a rapid technology for onsite identification of age of V. jatamansi in the field itself. The developed technology thus provides a scientific way for harvesting of this plant at its optimum age avoiding its wastage. The results of this study can also be applied to other endangered and valuable plants by way of finding its optimum growth stages for its harvesting.

Acknowledgment

We would like to thank Dr. Sanjay Kumar, Director, CSIR-IHBT, Palampur, India for his guidance and support in carrying out this research. Staff members of Environmental technology and Agrotechnology divisions are acknowledged for their help in field work and laboratory. The authors would like to thank referees for their valuable suggestions towards the improvement of manuscript. The CSIR-IHBT communication number is 4576.

Disclosure statement

The authors declare no conflicts of interest.

Additional information

Funding

The research was funded by National Medicinal Plant Board (NMPB), Ministry of AYUSH, Government of India, New Delhi under a project entitled ‘Development of geo-tagged digital database and spectral library of medicinal plants in protected cultivation in the foothills of western Himalaya (GAP-0223)’.