Journal article
Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection
Journal of Soils and Sediments, Vol.20, pp.249-259
2020
Abstract
Purpose: Fast and real-time prediction of leaf nutrient concentrations can facilitate decision-making for fertilisation regimes on farms and address issues raised with over-fertilisation. Cacao (Theobroma cacao L.) is an important cash crop and requires nutrient supply to maintain yield. This project aimed to use chemometric analysis and wavelength selection to improve the accuracy of foliar nutrient prediction. Materials and methods: We used a visible-near infrared (400-1000 nm) hyperspectral imaging (HSI) system to predict foliar calcium (Ca), potassium (K), phosphorus (P) and nitrogen (N) of cacao trees. Images were captured from 95 leaf samples. Partial least square regression (PLSR) models were developed to predict leaf nutrient concentrations and wavelength selection was undertaken. Results and discussion: Using all wavelengths, Ca (R2CV = 0.76, RMSECV = 0.28), K (R2CV = 0.35, RMSECV = 0.46), P (R2CV = 0.75, RMSECV = 0.019) and N (R2CV = 0.73, RMSECV = 0.17) were predicted. Wavelength selection increased the prediction accuracy of Ca (R2CV = 0.79, RMSECV = 0.27) and N (R2CV = 0.74, RMSECV = 0.16), while did not affect prediction accuracy of foliar K (R2CV = 0.35, RMSECV = 0.46) and P (R2CV = 0.75, RMSECV = 0.019). Conclusions: Visible-near infrared HSI has a good potential to predict Ca, P and N concentrations in cacao leaf samples, but K concentrations could not be predicted reliably. Wavelength selection increased the prediction accuracy of foliar Ca and N leading to a reduced number of wavelengths involved in developed models.
Details
- Title
- Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection
- Authors
- Mohammad Malmir (Author) - Griffith UniversityIman Tahmasbian (Author) - Griffith UniversityZhihong Xu (Author) - Griffith UniversityMichael B Farrar (Author) - University of the Sunshine CoastShahla Hosseini Bai (Author) - University of the Sunshine Coast
- Publication details
- Journal of Soils and Sediments, Vol.20, pp.249-259
- Publisher
- Springer
- Date published
- 2020
- DOI
- 10.1007/s11368-019-02418-z
- ISSN
- 1439-0108
- Organisation Unit
- School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland; School of Health and Sport Sciences - Legacy; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99450715702621
- Output Type
- Journal article
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- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Environmental Sciences
- Soil Science
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