- freely available
Sensors 2019, 19(23), 5295; https://doi.org/10.3390/s19235295
- Monocular vision imaging: the plants images are captured by a color camera [7,8,9] or scanner [10,11], the determination of plant nutrients based on monocular vision imaging mainly uses the characteristic parameters or combinations of two-dimensional (2D) images (such as [2G − (R + B)]/(R + G + B), R + G − B, etc) in RGB, HSV, YUV, and Lab color space models to establish models for determining the plant nutrients. This method very simple, but it has high requirements for the illumination environment during image collection, and the model has relatively low applicability. Plant nutrient measurements based on a scanner require that plant samples are collected for scanning, which is relatively inefficient, and the types of nutrients that can be measured are very limited [10,11].
- Multispectral imaging: several characteristic bands are selected according to the sensitive characteristics of plant nutrients, such as one or more characteristic special bands in visible and near-infrared bands respectively [12,13,14]. According to the images or reflectance values of multiple characteristic bands of plants, the plant canopy nutrient prediction models are established. Most of the imaging areas are plant canopy images or band reflectance values. Generally, only one plant nutrient content can be measured at one time. The imaging area of this method is very limited, and the stability of the measurement models are greatly affected by the imaging area and the natural light environment.
- Hyperspectral imaging: hyperspectral imaging is mainly used for the selection of the characteristic wavelengths of the plant nutrients [15,16,17]. The characteristic wavelengths that reflect the physicochemical properties of the materials are extracted. Multispectral imaging can be applied for the practical determination of plant nutrients . Characteristic wavelengths are mainly extracted using the artificial neural network (ANN) , random frog (RF) algorithm , correlation coefficient (r) [18,19,20,21,22,23,24], principal component analysis (PCA) [25,26,27], successive projections algorithm (SPA) [26,27,28], uninformative variable elimination (UVE) , segmented principal components analysis (SPCA) , and competitive adaptive reweighted sampling (CARS) [28,30], etc. For the plant nutrient models established according to the characteristic band spectral reflectance or vegetation indices, the modeling methods are mainly divided into linear and nonlinear types. The linear correction methods include linear regression (LR) , multiple linear regression (MLR) [20,24], stepwise regression (SWR) , and partial least squares (PLS) [18,20,21,28,30,32], etc. The nonlinear correction methods include ANN [19,20,27,28,32], the support vector machine (SVM) [19,21], and RF . Among them, PLS and ANN are the most widely applied algorithms.
- Fluorescence imaging: fluorescence imaging system, according to the fluorescence induction curve of plant chlorophyll, collects the fluorescence images of plant leaves or canopy by controlling the intensity of laser source (such as measuring light, actinic light, saturation light), and extracts the fluorescence parameters of plant leaves, such as minimum fluorescence under dark adaptation F0, maximum fluorescence under dark adaptation Fm, etc. Based on fluorescence imaging, models for determining a plant’s physiological information, such as nutrients, diseases, and stress, are established mainly from the plant’s fluorescence properties [33,34]. This technology has high requirements for the imaging environment, excitation light source, and imaging equipment. In addition, it has high operational requirements, uses very expensive equipment, and cannot be widely applied at a large scale.
- Proximal optical sensors: mainly includes chlorophyll meter, reflectance sensor and fluorescence-based flavonols meters. The representative chlorophyll meters mainly include SPAD-502 (Konica Minolta, Tokyo, Japan), DUALEX (Force-A, Orsay, France), MC-100 Chlorophyll Concentration Meter (Apogee Instruments Inc., Logan, UT, USA), etc. The representative reflectance sensor mainly include Crop Circle ACS 430 (Holland Scientific, Lincoln, NE, USA), MSR5/87/16R (CropScan Inc., Rochester, MN, USA), and GreenSeeker (Trimble Inc., Sunnyvale, CA, USA), etc. The fluorescence-based flavonols meters mainly include DUALEX and MULTIPLEX (Force-A), etc. These sensors are mainly used to measure the N content of plant leaves or canopy, which usually only measures the chlorophyll or NDVI value of a small region of the plant canopy at a time, and the plant nutrient distribution is uneven, particularly in the absence of nutrient elements [1,35,36].
- Plant’s electrical signals: there are also methods for measuring plant K and P contents based on the plant’s electrical signals [37,38]. Plant electrical signals are weak and prone to interference from the measuring environment. This measurement method requires the insertion of probes into plant leaves or stems and is thus destructive. Therefore, it is difficult to use this method to measure plant nutrients at a large scale and periodically during actual production.
2. Materials and Methods
2.1. Sample Cultivation
2.2. Instrument and Spectral Data Collection
2.3. Multispectral 3D Point Cloud Modeling
2.4. Determination of the Chemical Values of the NPK Nutrients
2.5. Data Processing and Analysis
2.5.1. Optimal Wavelength Selection
2.5.2. Evaluation of the Accuracy of Multispectral 3D Point Cloud Reconstruction
2.5.3. NPK Model Construction and Evaluation
3. Results and Discussion
3.1. Multispectral 3D Point Cloud Modeling
3.2. Analysis of the Accuracy of the Multispectral 3D Point Cloud Reconstruction of Tomato Plants
3.2.1. Evaluation of the Registration Quality of Spectral Reflectance
3.2.2. Evaluation of the Accuracy of Multispectral 3D Point Cloud Reconstruction
3.3. Construction of NPK Prediction Models
3.4. Model Verification and Analysis
- N models: 3DROI multispectral reflectance values were used as the input values for the N prediction models using BPANN, SVMR, and GPR. The Rc2, Rp2, RMSEC, and RMSEP of the BPANN prediction model were 0.97, 0.99, 1.63 mg/g, and 1.17 mg/g, respectively, and the RE was 2.27%. The Rc2, Rp2, RMSEC, and RMSEP of the SVMR prediction model were 0.87, 0.86, 3.35 mg/g, and 4.39 mg/g, respectively, and the RE was 7.46%. The Rc2, Rp2, RMSEC, and RMSEP of the GPR prediction model were 0.97, 0.94, 1.53 mg/g, and 2.79 mg/g, respectively, and the RE was 4.03%. The BPANN, SVMR, and GPR prediction models of the plant N contents all had good predictive performances. The performances of the BPANN and GPR models were similar, and were better than that of the SVMR model.
- P models: 3DROI multispectral reflectance values were used as the input values for the P prediction models using BPANN, SVMR, and GPR. The Rc2, Rp2, RMSEC, and RMSEP of the BPANN prediction model were 0.98, 0.95, 0.33 mg/g, and 0.52 mg/g, respectively, and the RE was 3.32%. The Rc2, Rp2, RMSEC, and RMSEP of the SVMR prediction model were 0.91, 0.88, 0.77 mg/g, and 0.82 mg/g, respectively, and the RE was 8.92%. The Rc2, Rp2, RMSEC, and RMSEP of the GPR prediction model were 0.93, 0.92, 0.67 mg/g, and 0.62 mg/g, respectively, and the RE was 8.41%. The performance of the BPANN model was the best, followed by that of the GPR and SVMR models.
- K models: 3DROI multispectral reflectance values were used as the input values for the K prediction models using BPANN, SVMR, and GPR. The Rc2, Rp2, RMSEC, and RMSEP of the BPANN prediction model were 0.90, 0.89, 1.75 mg/g, and 1.85 mg/g, respectively, and the RE was 3.27%. The Rc2, Rp2, RMSEC, and RMSEP of the SVMR prediction model were 0.70, 0.66, 3.15 mg/g, and 3.18 mg/g, respectively, and the RE was 5.73%. The Rc2, Rp2, RMSEC, and RMSEP of the GPR prediction model were 0.91, 0.89, 1.86 mg/g, and 1.73 mg/g, respectively, and the RE was 3.32%. The performance of the GPR model was the best, followed by that of the BPANN and SVMR models.
Conflicts of Interest
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|N||PCA-CC-RF||5||451.6, 544.1, 585.7, 696.3 *, 739.0|
|P||PCA-CC-RF||5||446.5, 585.7, 675.1 *, 728.3, 787.3|
|K||PCA-CC-RF||5||497.7, 585.7 *, 617.1, 675.1, 739.0|
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