A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

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.

Leaf Nitrogen Concentration and Plant Height Prediction for Maize Using UAV-Based Multispectral Imagery and Machine Learning Techniques

“…The number of samples implemented was similar to others presented in previous research [12,28], which also discussed the required quantities of input data to train these types of algorithms. With the cross-validation approach, 90% of the 176 samples were used to train the models and 10% to test it.…”

“…In recent years, the increase of market-availability of unmanned aerial vehicles (UAV) encouraged multiple applications in this field. Agriculture remote sensing is a promising field as it supports a multidisciplinary view of different problems related to crop mapping [2] and has been implemented in multiple subjects, such as environment control [3], temporal analysis [4], phenology [5], yield-prediction [6][7][8][9], and nutritional analysis [10][11][12]. These studies revealed the importance of evaluating techniques and sensing data to deal with such tasks.…”

“…Machine learning is considered a subgroup inside of the artificial intelligence area in which algorithms can learn from data and then discover patterns in the dataset, deciding on new and similar information. The algorithms have the potential to model several types of datasets using linear and parametric and nonlinear and nonparametric approaches [12,27,28], including multispectral images [29]. Different machine learning algorithms like random forests (RF), decision trees (DT), artificial neural network (ANN), support vector machines (SVM), among many others, have been adopted to attend various applications in agriculture remote sensing [5,[30][31][32].…”

Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery

“…The use of machine learning methods [ 8 ], such as RF (Random Forest) and SVM (Support Vector Machine) in remote sensing applications has been increasing in the last few years, even in precision farming approaches [ 9 , 10 , 11 ]. Deep learning is a type of machine learning technique that automatically extracts features, and uses a deeper neural network with hierarchical data representation [ 12 ].…”

“…Deep learning was applied in diverse scenarios with crops such as citrus-trees [ 11 , 16 ], canola [ 17 ], rice [ 18 ], corn [ 19 ], bean and spinach [ 20 ], and others. There is still a lack of deep learning-based applications in forage crops, specifically using remote sensing data.…”