Name
Title | ||
|---|---|---|
A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements. |
Abstract | ||
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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((R)) 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 (R-2), 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12060906 | REMOTE SENSING |
Keywords | DocType | Volume |
spectroscopy,proximal sensor,macronutrient,micronutrient,artificial intelligence | Journal | 12 |
Issue | Citations | PageRank |
6 | 1 | 0.35 |
References | Authors | |
0 | 15 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lucas Prado Osco | 1 | 1 | 0.35 |
| Ana Paula Marques Ramos | 2 | 1 | 0.35 |
| Mayara Maezano Faita Pinheiro | 3 | 1 | 0.35 |
| Érika Akemi Saito Moriya | 4 | 1 | 0.35 |
| Nilton Nobuhiro Imai | 5 | 63 | 10.24 |
| Nayara Estrabis | 6 | 1 | 0.35 |
| Felipe Ianczyk | 7 | 1 | 0.35 |
| Fábio Fernando de Araújo | 8 | 1 | 0.35 |
| Veraldo Liesenberg | 9 | 1 | 0.35 |
| Lúcio André de Castro Jorge | 10 | 1 | 0.35 |
| Jonathan Li | 11 | 798 | 119.18 |
| Lingfei Ma | 12 | 28 | 6.87 |
| Wesley Nunes Gonçalves | 13 | 12 | 11.51 |
| José Marcato Junior | 14 | 1 | 0.35 |
| José Eduardo Creste | 15 | 4 | 1.07 |