Abstract
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 × 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.
Highlights
IntroductionRemote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1,2,3,4], the stress response [5,6,7], plant count [8,9], yield prediction [10,11,12], chlorophyll content [13,14,15], pest and disease identification [16,17], and biomass estimation [18], among others
The proposed approach uses leaf spectral data in the visible and near-infrared regions, and switches between reflectance and its first-derivative data to predict the amount of macro- and micronutrients measured in the laboratory
This method was able to return high predictions (R2) for 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 the Random Forest (RF), Artificial Neural Network (ANN), and k-Nearest Neighbor (kNN) algorithms, among which RF performed the best
Summary
Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1,2,3,4], the stress response [5,6,7], plant count [8,9], yield prediction [10,11,12], chlorophyll content [13,14,15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19,20,21,22,23] This type of sensor can measure the spectral response of a target at very-high resolutions while having a reductive amount of radiometric interference by being near the leaf sample. Due to the high spectral resolution capability of these sensors, studies have been relatively successful in modeling phenomena, such as the ones previously stated, but at the leaf level, like plant stress, yield prediction, nutrient content, chlorophyll, and many other attributes [24,25,26,27] They have the advantage of helping to define, in detail, the appropriate spectral regions to estimate these phenomena. Another type of contribution is that it can assist in creating spectral vegetation indices or other simpler mathematical models that contribute to identifying the different characteristics of plants [13,28]
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