# Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Plant Material

#### 2.2. Video Data Acquisition

#### 2.3. Pre-Processing and Segmentation

#### 2.4. Feature Extraction

#### 2.5. Effective Feature Selection

#### 2.6. Classification

- High computation speed;
- Ability to efficiently handle noisy inputs;
- Data-driven nature, thanks to learning from the training data.

#### 2.7. Proposed System for the Classification of Rice and Weed Plants Inside Rice Fields

#### 2.8. Arithmetic and Geometric Means

## 3. Results and Discussion

#### 3.1. Effective Feature Extraction with ANN-PSO

#### 3.2. Classification Using Hybrid Metaheuristic Algorithms

#### 3.2.1. Classification Using Hybrid ANN-BA

#### 3.2.2. Classification Using KNN Classifier

#### 3.2.3. Classification Performance Evaluation by receiver operating characteristic (ROC) Curves

## 4. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Conflicts of Interest

## References

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**Figure 1.**A rail platform for holding the camera and moving it across the field: 3 m length. Note: The growth stage of the rice on the BBCH scale was leaf development and tillering (from 1 week after transplanting to the sixth week) and weeds were leaf development (after three leaves unfolded). Water depth was 10 cm.

**Figure 2.**Segmentation of the green components (weeds and rice crop) of sample frames for (

**a**) wide-leaf weed (Eclipta prostrata), (

**b**) wide-leaf weed (Alisma plantago-aquatica), (

**c**) narrow-leaf weeds (Cyperus difformis and Echinochloa crus-galli), and (

**d**) narrow-leaf weeds (Echinochloa crus-galli and Paspalum distichum).

**Figure 3.**Flowchart of the proposed system for the classification of rice and weed plants inside rice fields by recording stereo video and decomposing the video into right and left channel data.

**Figure 4.**Performance evaluation of the ANN-BA classifier on the test dataset in the four categories (right, left, geometric mean, and arithmetic mean) based on the ROC and ROC-best curves related to the three classes (rice, narrow-leaf weed, and wide-leaf weed).

**Figure 5.**Segmentation and classification of rice and weeds in color and binary images for a frame. (

**a**) Original frame, (

**b**) color model of left channel, (

**c**) binary model of left channel, (

**d**) color model of right channel, (

**e**) binary model of right channel, (

**f**) color model of arithmetic mean, (

**g**) binary model of arithmetic mean, (

**h**) color model of geometric mean, and (

**i**) binary model of geometric mean.

**Table 1.**The parameters of the multi-layer perception (MLP) and particle swarm optimization (PSO) for the hybrid artificial neural network (ANN)-PSO for selecting the most significant features.

MLP Parameters | PSO Parameters |
---|---|

One input layer | Swarm size: 30 |

One hidden layer with 10 neurons | Maximum iteration: 20 |

One output layer with 3 outputs. | Inertia weight damping ratio: 1 |

Classic Levenberg–Marquardt training function | Maximum variation size: 1 |

Minimum variation size: 0 | |

Inertia rate: 1 | |

Velocity Maximum value: | |

0.1×(VarMax-VarMin) | |

Velocity minimum value: -VelMax |

**Table 2.**The most effective features selected by the proposed hybrid ANN-PSO algorithm from the left and right channel data and arithmetic and geometric means.

Category | Selected Effective Features | |||||
---|---|---|---|---|---|---|

Left channel | EXY-YIQ | Elongation Feature | Cluster Prominence-45 | Rn | Inverse Difference-45 | Entropy-45 |

Right channel | Convexity | ExG-RGB | CIVE-HSV | Cluster shade-90 | CIVE-RGB | Difference entropy-0 |

Arithmetic mean | Sum entropy-0 | Information measure of correlation-0 | CIVE-RGB | Autocollelation-90 | Coefficient of variation--90 | WL |

Geometric mean | Inverse difference normalized-135 | WL | CMP | Std-Cb | Entropy | ExM-CMYYY |

**Table 3.**Formal definition of selected features inside the four categories under consideration: description and feature name.

Description | Selected Feature Name |
---|---|

Excess yellow from YIQ color space | EXY-YIQ |

Elongation feature = (L − W)/(L + W) L = length and W = width | Elongation feature |

Clumster prominence = Σ_{i}Σ_{j}(i + j − μ_{i} − μ_{j})^{4}N_{g}(i,j) N _{g} = $\frac{g\left(i,j\right)}{\sum i\sum jg\left(i,j\right)}$ (The normalized co-occurrence matrix) | Cluster prominence |

Rn = R/(R + G + B), (The normalized first component of RGB) | Rn |

Inverse Difference = $\sum}_{i=0}^{n-1}.{\displaystyle \sum}_{j=0}^{n-1}\frac{Ng\left(i,j\right)}{1+\left[i-1\right]$ | Inverse Difference |

Entropy = −ΣΣN_{g}(i,j)log_{2} N_{g}(i,j) | Entropy |

A measure of the curvature | Convexity |

ExG-RGB = 2 × Gn − Rn − Bn, (Excess green) | ExG-RGB |

Color index for extracted vegetation cover in HSV color space | CIVE-HSV |

cluster Shade = ΣΣ(i + j − μ_{i} − μ_{j})^{3}N_{g}(i,j) | cluster Shade |

CIVE-RGB = 0.441 × Rn − 0.811 × Gn + 0.385 × Bn + 18.78 (Color index for extracted vegetation cover) | CIVE-RGB |

Difference entropy = −Σp_{x}_{−}_{y}(i) ln [p_{x}_{−}_{y}(i)], p _{x}_{-}_{y}(k) = $\sum}_{i,j:\left[i-j\right]=k}Ng\left(i,j\right)fork=0,\dots ,Ng-1$ | Difference entropy |

Sum Entropy = −Σp_{x}_{+}_{y}(i)log(p_{x;}_{+}_{y}(i)) p _{x}_{+}_{y}(k) = $\sum}_{i,j:i+j=k}Ng\left(i,j\right)fork=2,3,\dots ,2l$ | Sum Entropy |

IMC = $\frac{ENT-HXY1}{max\left(Hx,Hy\right)}$ HXY1 = $-{\displaystyle \sum}_{i=0}^{n-1}{\displaystyle \sum}_{j=0}^{n-1}Ng\left(i,j\right)\mathrm{ln}\left[Nx\left(i\right).Ny\left(j\right)\right],$ N _{x}(i) = $\sum}_{i=0}^{n-1}Ng\left(i,j\right)$, N_{y}(i) = $\sum}_{j=0}^{n-1}Ng\left(i,j\right)\u2019$, H_{X}: Entropy of N_{x} and H_{y}: Entropy of N_{y} | Information measure of correlation |

Autocorrelation = ΣΣ(ij)N_{g}(i,j) | Autocorrelation |

Standard deviation to mean of co-occurrence matrix | Coefficient of variation |

WL = Width/Length | WL |

IDN = $\sum}_{i=0}^{n-1}.{\displaystyle \sum}_{j=0}^{n-1}\frac{Ng\left(i,j\right)}{1+\frac{{\left[i-1\right]}^{2}}{{L}^{2}}$ | Inverse difference normalized |

CMP = $\frac{{p}^{2}}{4\pi A}$ (Compression) A:area, p:perimeter | CMP |

Standard deviation of Cb from YCbCr color space | Std-Cb |

Excess magenta From CMY color space | ExM-CMYYY |

**Table 4.**The optimized parameters for classification using the hybrid artificial neural network bee algorithm (ANN-BA).

Number of Hidden Layers | Number of Neurons | Transfer Function | Back Propagation Network Training Function | Back Propagation Weight/Bias Learning Function |
---|---|---|---|---|

2 | First layer: 20 Second layer: 12 | First layer: tansig Second layer: satlins | trainrp | learngd |

**Table 5.**Confusion matrices and accuracy of the ANN-BA classifier for the left channel, right channel, arithmetic mean, and geometric mean (test set).

Left channel | Rice | Narrow-leaf weeds | Wide-leaf weeds |
---|---|---|---|

Rice | 89 | 6 | 2 |

Narrow-leaf weeds | 12 | 67 | 6 |

Wide-leaf weeds | 2 | 1 | 56 |

Accuracy = 87.96% | |||

Right channel | Rice | Narrow-leaf weeds | Wide-leaf weeds |

Rice | 86 | 3 | 1 |

Narrow-leaf weeds | 6 | 73 | 10 |

Wide-leaf weeds | 2 | 4 | 46 |

Accuracy = 88.74% | |||

Arithmetic mean | Rice | Narrow-leaf weeds | Wide-leaf weeds |

Rice | 91 | 5 | 1 |

Narrow-leaf weeds | 6 | 69 | 2 |

Wide-leaf weeds | 1 | 3 | 48 |

Accuracy = 92.02% | |||

Geometric mean | Rice | Narrow-leaf weeds | Wide-leaf weeds |

Rice | 91 | 6 | 0 |

Narrow-leaf weeds | 7 | 67 | 3 |

Wide-leaf weeds | 3 | 2 | 47 |

Accuracy = 90.70% |

**Table 6.**Confusion matrices and accuracy of the K-nearest neighbors (KNN) classifier for the left channel, right channel, arithmetic mean, and geometric mean (test set).

Left channel | Rice | Narrow-leaf weeds | Wide-leaf weeds |
---|---|---|---|

Rice | 83 | 8 | 6 |

Narrow-leaf weeds | 10 | 65 | 10 |

Wide-leaf weeds | 0 | 0 | 59 |

Accuracy = 85.89% | |||

Right channel | Rice | Narrow-leaf weeds | Wide-leaf weeds |

Rice | 65 | 19 | 6 |

Narrow-leaf weeds | 17 | 60 | 12 |

Wide-leaf weeds | 0 | 0 | 52 |

Accuracy = 76.62% | |||

Arithmetic mean | Rice | Narrow-leaf weeds | Wide-leaf weeds |

Rice | 83 | 8 | 6 |

Narrow-leaf weeds | 11 | 62 | 4 |

Wide-leaf weeds | 3 | 0 | 49 |

Accuracy = 85.84% | |||

Geometric mean | Rice | Narrow-leaf weeds | Wide-leaf weeds |

Rice | 78 | 15 | 4 |

Narrow-leaf weeds | 12 | 60 | 5 |

Wide-leaf weeds | 0 | 0 | 52 |

Accuracy = 84.07% |

**Table 7.**Mean and standard deviation (STD) values of accuracy for the proposed hybrid ANN-BA and the KNN classifiers: three classes and four classifier categories.

Right Channel | |||||
---|---|---|---|---|---|

Hybrid ANN-BA | Mean | STD | KNN | Mean | STD |

Rice | 0.9446 | 0.0212 | Rice | 0.7224 | 0.0265 |

Narrow-leaf weeds | 0.8596 | 0.0314 | Narrow-leaf weeds | 0.6942 | 0.0272 |

Wide-leaf weeds | 0.9323 | 0.0289 | Wide-leaf weeds | 0.9004 | 0.0315 |

Left Channel | |||||

Hybrid ANN-BA | Mean | STD | KNN | Mean | STD |

Rice | 0.9100 | 0.0275 | Rice | 0.8256 | 0.0238 |

Narrow-leaf weeds | 0.8625 | 0.0275 | Narrow-leaf weeds | 0.7948 | 0.0273 |

Wide-leaf weeds | 0.9132 | 0.0376 | Wide-leaf weeds | 0.8961 | 0.0305 |

Arithmetic mean | |||||

Hybrid ANN-BA | Mean | STD | KNN | Mean | STD |

Rice | 0.9563 | 0.0165 | Rice | 0.8091 | 0.0240 |

Narrow-leaf weeds | 0.9330 | 0.0179 | Narrow-leaf weeds | 0.7993 | 0.0254 |

Wide-leaf weeds | 0.9653 | 0.0211 | Wide-leaf weeds | 0.9214 | 0.0272 |

Geometric mean | |||||

Hybrid ANN-BA | Mean | STD | KNN | Mean | STD |

Rice | 0.9414 | 0.0141 | Rice | 0.7745 | 0.0254 |

Narrow-leaf weeds | 0.9387 | 0.0169 | Narrow-leaf weeds | 0.7625 | 0.0258 |

Wide-leaf weeds | 0.9478 | 0.0200 | Wide-leaf weeds | 0.9493 | 0.0234 |

**Table 8.**Mean area under the receiver operating characteristic (ROC) curves (AUC) for the hybrid ANN-BA classifier for rice, narrow-leaf weed, and wide-leaf weed classes: right channel, left channel, arithmetic mean, and geometric mean.

Hybrid ANN-BA | Rice Class | Narrow-Leaf Weeds Class | Wide-Leaf Weeds Class |
---|---|---|---|

Right Channel | 0.9886 | 0.9376 | 0.9561 |

Left Channel | 0.9462 | 0.9106 | 0.9483 |

Arithmetic mean | 0.9731 | 0.9635 | 0.9765 |

Geometric mean | 0.9668 | 0.9638 | 0.9747 |

**Table 9.**Mean area under the ROC curves (AUC) for the KNN classifier for rice, narrow-leaf weed, and wide-leaf weed classes: right channel, left channel, arithmetic mean, and geometric mean.

KNN | Rice Class | Narrow-Leaf Weeds Class | Wide-Leaf Weeds Class |
---|---|---|---|

Right Channel | 0.8008 | 0.7702 | 0.9497 |

Left Cannel | 0.8931 | 0.8567 | 0.9560 |

Arithmetic mean | 0.8793 | 0.8758 | 0.9424 |

Geometric mean | 0.8556 | 0.8393 | 0.9742 |

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## Share and Cite

**MDPI and ACS Style**

Dadashzadeh, M.; Abbaspour-Gilandeh, Y.; Mesri-Gundoshmian, T.; Sabzi, S.; Hernández-Hernández, J.L.; Hernández-Hernández, M.; Arribas, J.I.
Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields. *Plants* **2020**, *9*, 559.
https://doi.org/10.3390/plants9050559

**AMA Style**

Dadashzadeh M, Abbaspour-Gilandeh Y, Mesri-Gundoshmian T, Sabzi S, Hernández-Hernández JL, Hernández-Hernández M, Arribas JI.
Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields. *Plants*. 2020; 9(5):559.
https://doi.org/10.3390/plants9050559

**Chicago/Turabian Style**

Dadashzadeh, Mojtaba, Yousef Abbaspour-Gilandeh, Tarahom Mesri-Gundoshmian, Sajad Sabzi, José Luis Hernández-Hernández, Mario Hernández-Hernández, and Juan Ignacio Arribas.
2020. "Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields" *Plants* 9, no. 5: 559.
https://doi.org/10.3390/plants9050559