1. A machine learning framework to predict nutrient content in valencia-orange leaf hyperspectral measurements
- Author
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Nayara Vasconcelos Estrabis, Mayara Maezano Faita Pinheiro, Wesley Nunes Gonçalves, Érika Akemi Saito Moriya, José Eduardo Creste, Ana Paula Marques Ramos, Felipe Ianczyk, Lúcio André de Castro Jorge, Lingfei Ma, Nilton Nobuhiro Imai, Lucas Prado Osco, Fabio Fernando de Araujo, Jonathan Li, José Marcato Junior, Veraldo Liesenberg, Universidade Federal de Mato Grosso do Sul (UFMS), University of Western São Paulo (UNOESTE), Universidade Estadual Paulista (Unesp), Santa Catarina State University (UDESC), Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA), University of Waterloo (UW), and LUCIO ANDRE DE CASTRO JORGE, CNPDIA.
- Subjects
spectroscopy ,Artificial intelligence ,010504 meteorology & atmospheric sciences ,macronutrient ,0211 other engineering and technologies ,Macronutrient ,proximal sensor ,micronutrient ,artificial intelligence ,02 engineering and technology ,Machine learning ,computer.software_genre ,01 natural sciences ,Spectral line ,Micronutrient ,lcsh:Science ,Spectroscopy ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Mathematics ,Spectral signature ,Artificial neural network ,business.industry ,Hyperspectral imaging ,Spectral bands ,Random forest ,Support vector machine ,Spectroradiometer ,Proximal sensor ,General Earth and Planetary Sciences ,lcsh:Q ,business ,computer - Abstract
Made available in DSpace on 2020-12-12T02:00:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-03-01 This paper presents a framework based on machine learning algorithms to predict nutrient content in leaf hyperspectral measurements. This is the first approach to evaluate macro-and micronutrient content with both machine learning and reflectance/first-derivative data. For this, citrus-leaves collected at a Valencia-orange orchard were used. Their spectral data was measured with a Fieldspec ASD FieldSpec® HandHeld 2 spectroradiometer and the surface reflectance and first-derivative spectra from the spectral range of 380 to 1020 nm (640 spectral bands) was evaluated. A total of 320 spectral signatures were collected, and the leaf-nutrient content (N, P, K, Mg, S, Cu, Fe, Mn, and Zn) was associated with them. For this, 204,800 (320 x 640) combinations were used. The following machine learning algorithms were used in this framework: k-Nearest Neighbor (kNN), Lasso Regression, Ridge Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and Random Forest (RF). The training methods were assessed based on Cross-Validation and Leave-One-Out. The Relief-F metric of the algorithms' prediction was used to determine the most contributive wavelength or spectral region associated with each nutrient. This approach was able to return, with high predictions (R2), nutrients like N (0.912), Mg (0.832), Cu (0.861), Mn (0.898), and Zn (0.855), and, to a lesser extent, P (0.771), K (0.763), and S (0.727). These accuracies were obtained with different algorithms, but RF was the most suitable to model most of them. The results indicate that, for the Valencia-orange leaves, surface reflectance data is more suitable to predict macronutrients, while first-derivative spectra is better linked to micronutrients. A final contribution of this study is the identification of the wavelengths responsible for contributing to these predictions Federal University of Mato Grosso do Sul (UFMS) Environmental and Regional Development University of Western São Paulo (UNOESTE) Department of Cartographic Science São Paulo State University (UNESP) Department of Agronomy University of Western São Paulo (UNOESTE) Forest Engineering Department Santa Catarina State University (UDESC) National Research Center of Development of Agricultural Instrumentation Brazilian Agricultural Research Agency (EMBRAPA) Department of Geography and Environmental Management and Department of Systems Design Engineering University of Waterloo (UW) Department of Cartographic Science São Paulo State University (UNESP)
- Published
- 2020