# Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon

^{*}

## Abstract

**:**

^{2}) and mean square error (MSE) between the predicted values and measured values of nitrogen concentration were adopted to evaluate the models’ accuracy. The values were R

^{2}= 0.567 and MSE = 0.429 for BPNN model; R

^{2}= 0.376 and MSE = 0.628 for CNN model; R

^{2}= 0.686 and MSE = 0.355 for deep convolution neural network (DCNN) model; and R

^{2}= 0.904 and MSE = 0.123 for the hybrid model DCNN–LSTM. Therefore, DCNN–LSTM shows the highest accuracy in predicting the nitrogen content of muskmelon. Our findings highlight a base for achieving a convenient, precise and intelligent diagnosis of nitrogen nutrition in muskmelon.

## 1. Introduction

## 2. Materials and Methods

_{1}, 2.7 g nitrogen/pot; T

_{2}, 5.4 g nitrogen/pot; T

_{3}, 8.1 g nitrogen/pot; and T

_{4}, 10.8 g nitrogen/pot. The amount of phosphorus and potassium added per pot was 5.2 and 9.0 g, respectively, in all the pots. The sources of N, P and K fertilizers were calcium nitrate, potassium nitrate, magnesium nitrate and potassium dihydrogen phosphate. The total N fertilizer was applied at six growing stages of muskmelon, namely pre-planting (10%), seedling stage (5%), vine elongation stage (10%), initial fruit stage (35%), fruit expanding stage (35%) and mature stage (5%). All other nitrogen fertilizer applications were applied with drip irrigation, for except pre-planting (10%).

## 3. Data Collection

#### 3.1. Measurement of Nitrogen Concentration in Plants

#### 3.2. Leaf Image Acquisition

#### 3.3. Collecting Meteorological Data of Greenhouse

#### 3.4. Establishment of Machine Learning (ML) Model

#### Extraction of Phenotypical Features

## 4. Result

#### 4.1. Phenotypical Feature Parameters Screening

#### 4.2. Establishment of Backpropagation Neural Network (BPNN)

#### 4.3. Establishment of Deep Learning Models

#### 4.3.1. Image Preprocessing

#### 4.3.2. Data Preprocessing of Environmental Factors

#### 4.4. Establishment of CNN Model

^{2}and MSE methods were used for model evaluation.

#### 4.5. Establishment of DCNN Model

#### 4.6. Establishment of DCNN–LSTM Model

^{2}and MSE method were used for the model evaluation.

#### 4.7. Evaluation of Models

^{2}) and mean square error (MSE) between predicted and measured values were used for model evaluation. In general, the higher R

^{2}value and the lower MSE are considered as more accurate and the best model.

^{2}= 0.567 and MSE = 0.429 for the BPNN model, R

^{2}= 0.376 and MSE = 0.628 for R

^{2}= 0.686 for the CNN model, R

^{2}= 0.686 and MSE = 0.355 for the DCNN model, and R

^{2}= 0.904 and MSE = 0.123 for the DCNN–LSTM model. With mean square error (MSE) as the loss function, the loss results of three deep learning models (DCNN and DCNN–LSTM) are shown in Figure 14B. For all models, the prediction accuracy improved to some extent with the increase of iterative training, but after reaching a certain number of trainings, the accuracy did not show a significant increase, or even decrease. At the same time, the more iterative training times required more time for model calculation. For the deep learning model, the model loss was very high in the initial iterative training. With the increase of the number of training iterations, the training sets in model loss showed a sharp drop in the beginning, but later did not show decline in falling and tended to the flat. The model loss of the test set also gradually decreased, but later tended to the flat and showed similar trends of the training set of model loss. In general, the test set was shown to have slightly higher model loss than the training set.

## 5. Discussion

^{2}= 0.567, MSE = 0.429) was constructed by adopting machine vision technology to extract and process the phenotypic features of leaf images. Then a CNN nitrogen nutrition diagnosis model (R

^{2}= 0.376, MSE = 0.628) was constructed. For the CNN model, the original leaf image was directly put as input into the model and preprocessed. By increasing the depth of CNN, we built DCNN (R

^{2}= 0.686, MSE = 0.355) for nitrogen nutrition diagnosis. Furthermore, based on DCNN, a hybrid model, namely DCNN–LSTM, was constructed, and R

^{2}= 0.904 and MSE = 0.123 were used as the evaluation indexes’ values. For the model, TEP, instead of time series, was used as a time variable.

## 6. Conclusions

^{2}= 0.686, MSE = 0.355) in the prediction of plant nitrogen concentration in muskmelon production in the greenhouse. These findings indicate the great potential of deep learning technology in crop nutrition diagnosis and provide a technique and reference for real-time, convenient, accurate and nondestructive nitrogen nutrition diagnosis in greenhouse muskmelon production. The study lays the foundation for the intelligent monitoring of nitrogen nutrition in plants.

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Systematic overview based on the deep learning approaches for the prediction of nitrogen nutrition diagnosis. The proposed system consists of several steps to collect the dataset and provides classification and prediction of results.

**Figure 2.**(

**A**) Transplanting at three leaves stage. (

**B**) Using single vine and pruning to remove redundant side vines. (

**C**) Topping performed at 20–22 leaves. (

**D**) Schematic diagram of the experimental greenhouse.

**Figure 3.**In Step 1, input of images into the models; in Step 2, performing data curation by augmentation, standardization, normalization and annotation; in Step 3, input the weather data and measure nitrogen (N) percentage, combine with data curation in Step 2 and made a Dataset 2 and Dataset 3 by using training and validation testing data; in Steps 4 and 5, Dataset 2 and Dataset 3 used to get clear connected object by convolutional and pooling layers, and then predicted the nitrogen concentration in convolution neural network (CNN), deep-learning convolution neural network (DCNN) and hybrid long short-term memory (DCNN–LSTM) model by training and validation testing of model loss; in Step 6, evaluated the CNN, DCNN and DCNN–LSTM models to select the best model among them on the basis of coefficients of determination (R

^{2}) and mean square error (MSE) in muskmelon for nitrogen diagnosis.

**Figure 4.**Workflow of leaf image analysis. Note: (

**A**–

**D**) 1st–4th fully expanded leaf; 1, to convert the image from RGB to HSV and extract the saturation channel; 2, to threshold the saturation image; 3, “median_blur”; 4, to convert RGB to LAB and extract the blue channel; 5, to threshold the blue image; 6, to join the thresholded saturation and blue-yellow images; 7; to convert RGB to LAB and extract the green-magenta channels; 8, blue-yellow channels; 9, to threshold the green-magenta images; 10, to threshold the blue images; 11, to join the thresholded saturation; 12, to join the blue-yellow images; 13, to decide which objects to be kept; 14, to apply mask; 15, to find shape properties and output shape image; and 16, to shape properties relative to user boundary line.

**Figure 6.**Significance analysis of phenotypic features with a p-value threshold of 0.01. Note: Solid and hollow squares indicate the conditions p < 0.01 and p > 0.01, respectively. Horizontal line represents p = 0.01, i.e., −lg (p) = 2.

**Figure 7.**Scatter plots showing projections of the top 3 PCs based on PCA of image-based data. Note: Component scores (shown in points) are shown with different colors with same shape according to the phenotypical features. Component loading vectors (represented in lines) of all features were superimposed proportionally to their contribution.

**Figure 9.**Schematic input and output volumes of each layer of convolutional neural network (CNN) model.

**Figure 10.**Feature maps of deep convolutional neural network (DCNN) model. Note: (

**A**) input data; (

**B**–

**F**) feature maps of the 1st–5th convolutional layers.

**Figure 11.**Schematic view of recurrent neural network (RNN) model. In graph, arrow on left side indicates neurons of CNN; x (t) and y (t) are put as an input and output step respectively. The right-side graph shows the RNN expansion architecture. Respectively, u, v and w are the weight matrices corresponding to the input, output and hidden states. The same time states share one weight matrix, which greatly reduces the number of parameters in the model to be learned.

**Figure 12.**Schematic view of long-short term memory (LSTM) model: c in the left upper part represents the internal memory of the unit; h on the left lower part represents the hidden state; i, f and o mean input gate, forgetting gate and output gate, respectively; the three parameters were calculated by using the same equation with different parameter matrixes, and then defined the data availability of x (t), h (t − 1) and the current data used for the next l; and g represents the internal hidden state.

**Figure 13.**Schematic view of deep convolutional neural network–long-short term memory (DCNN–LSTM) model.

**Figure 14.**(

**A**) Observed and predicted values. (

**B**) Epoch and loss values of machine learning (ML), convolutional neural network (CNN), deep convolutional neural network (DCNN) and DCNN–long-short term memory (LSTM) model simulations.

Category | Serial No. | Extracted Index | Reference |
---|---|---|---|

Color | 1–3 | blue/green/red mean | |

4–6 | lightness/green-magenta/blue-yellow mean | ||

7–9 | hue/saturation/value mean | ||

Morphology | 10 | area | |

11 | hull-area | ||

12 | solidity | ||

13 | perimeter | ||

14 | width | ||

15 | height | [52] | |

16 | longest-axis | ||

17 | center-of-mass-x | ||

18 | center-of-mass-y | ||

19 | hull-vertices | ||

20 | ellipse-center-x | ||

21 | ellipse-center-y | ||

22 | ellipse-major-axis | ||

23 | ellipse-minor-axis | ||

24 | ellipse-angle | ||

25 | ellipse-eccentricity | ||

Texture | 26 | contrast | |

27 | dissimilarity | ||

28 | homogeneity | ||

29 | ASM | ||

30 | energy | ||

31 | correlation |

Category | No. | Parameters Name | F1 | F2 | F3 |
---|---|---|---|---|---|

Yan Color Special Sign | 2 | Green | −0.488 | 0.855 | −0.067 |

3 | Red | −0.462 | 0.840 | −0.119 | |

4 | Lightness | −0.484 | 0.853 | −0.076 | |

5 | green-magenta | 0.496 | −0.787 | −0.107 | |

6 | blue-yellow | −0.480 | 0.854 | −0.008 | |

9 | Value | −0.486 | 0.856 | −0.068 | |

Shape State Special Sign | 10 | Area | 0.784 | 0.540 | 0.190 |

11 | hull-area | 0.896 | 0.356 | 0.199 | |

12 | Solidity | −0.239 | 0.606 | 0.001 | |

14 | Width | 0.917 | 0.263 | 0.214 | |

15 | Height | 0.897 | 0.304 | 0.208 | |

16 | longest-axis | 0.900 | 0.325 | 0.214 | |

22 | ellipse-major-axis | 0.887 | 0.390 | 0.178 | |

23 | ellipse-minor-axis | 0.880 | 0.383 | 0.214 | |

26 | Contrast | 0.611 | 0.109 | −0.757 | |

Pattern Reason Special Sign | 27 | dissimilarity | 0.721 | 0.093 | −0.676 |

28 | homogeneity | −0.889 | 0.011 | 0.353 | |

29 | ASM | −0.933 | 0.003 | −0.070 | |

30 | Energy | −0.957 | −0.018 | −0.073 | |

31 | correlation | −0.290 | −0.207 | 0.919 |

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## Share and Cite

**MDPI and ACS Style**

Chang, L.; Li, D.; Hameed, M.K.; Yin, Y.; Huang, D.; Niu, Q.
Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon. *Horticulturae* **2021**, *7*, 489.
https://doi.org/10.3390/horticulturae7110489

**AMA Style**

Chang L, Li D, Hameed MK, Yin Y, Huang D, Niu Q.
Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon. *Horticulturae*. 2021; 7(11):489.
https://doi.org/10.3390/horticulturae7110489

**Chicago/Turabian Style**

Chang, Liying, Daren Li, Muhammad Khalid Hameed, Yilu Yin, Danfeng Huang, and Qingliang Niu.
2021. "Using a Hybrid Neural Network Model DCNN–LSTM for Image-Based Nitrogen Nutrition Diagnosis in Muskmelon" *Horticulturae* 7, no. 11: 489.
https://doi.org/10.3390/horticulturae7110489