Prediction of macronutrients in plant leaves using chemometric analysis and wavelength selection |
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Authors: | Malmir Mohammad Tahmasbian Iman Xu Zhihong Farrar Michael B Bai Shahla Hosseini |
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Institution: | 1.Environmental Futures Research Institute, School of Science and Environment, Griffith University, Nathan, Brisbane, QLD, 4111, Australia ;2.Department of Agronomy, Faculty of Agriculture, Tarbiat Modares University, Tehran, 14115-336, Iran ;3.NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW, 2650, Australia ;4.Graham Centre for Agricultural Innovation, Charles Sturt University, Wagga Wagga, New South Wales, 2650, Australia ;5.Genecology, Faculty of Science, Health, Education and Engineering, University of the Sunshine Coast, QLD, Maroochydore DC, 4558, Australia ;6.School of Health, Medical and Applied Sciences, Central Queensland University, Bundaberg, Queensland, 4670, Australia ; |
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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 methodsWe 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 discussionUsing 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). ConclusionsVisible-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. |
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