# Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7

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## Abstract

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## 1. Introduction

- We established a low-cost data acquisition system. After passing through the corn combine harvester, the maize kernels are randomly distributed through electromagnetic vibration and sampled by ordinary RGB industrial cameras. Also, we established a standardized maize kernel quality dataset, including four categories: moldy, germinant, intact, and broken.
- A maize kernel quality detection model, YOLOv7-MEF, was developed. In this algorithm, MobileNetV3 was used to replace the original feature extraction backbone network, ESE-Net was integrated to enhance feature extraction, and Focal-EIoU was used to optimize the original loss function. The algorithm is made with high accuracy, fast detection speed, and small model size.
- The self-established maize kernel database was used to evaluate the model, and ablation experiments were carried out to verify the algorithm’s recognition and location effect on low-cost sampling images, providing a theoretical basis for related research.

## 2. Materials and Methods

#### 2.1. Materials

#### 2.1.1. Dataset Acquisition

#### 2.1.2. Dataset Labeling

#### 2.1.3. Data Augmentation

#### 2.2. Training Environment and Methods

#### 2.3. Performance Indexes

_{x}is the precision of a specific class and K

_{class}is the number of quality categories.

## 3. Results

#### 3.1. Comparison of Models

#### 3.2. YOLOv7-Tiny Structure

#### 3.3. YOLOv7-MEF

#### 3.3.1. MobileNetV3

#### 3.3.2. ESE-Net Efficient Attention Mechanism

#### 3.3.3. Focal-EIoU Loss

_{w}and ${C}_{h}$ are, respectively, the width and height of the two rectangles. It can be seen from the formula that EIoU directly took the side length as the penalty term and divided the loss function into three parts: Iou loss, distance loss, and side length loss. EIoU theoretically solved the problem that the width and height of CIoU could not be enlarged or reduced at the same time and optimized and improved CIoU.

#### 3.3.4. YOLOv7-MEF

## 4. Model and Algorithm Test

#### 4.1. Ablation Experiment

#### 4.2. Comparative Analysis of YOLOv7-MEF and YOLOv7-Tiny Model

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 2.**Data augmentation example (

**a**) original image; (

**b**) horizontal flip image; (

**c**) rotate 40° image; (

**d**) vertical flip image; and (

**e**) Gaussian noise image.

**Figure 11.**[email protected]:0.95 and loss curves of the training set for different improved networks.

Category | Number | Training Set | Test Set | Validation Set |
---|---|---|---|---|

Intact | 15,684 | 12,548 | 1568 | 1568 |

Moldy | 16,104 | 12,884 | 1612 | 1612 |

Broken | 16,660 | 13,328 | 1664 | 1664 |

Germinant | 16,296 | 13,036 | 1628 | 1628 |

total | 64,744 | 51,796 | 6472 | 6472 |

Model | Precision | Recall | [email protected] | Model Size/M |
---|---|---|---|---|

Faster-RCNN | 88.51% | 88.54% | 86.56% | 108.29 |

SSD | 92.89% | 92.66% | 92.83% | 92.13 |

YOLOv5 | 89.13% | 91.3% | 91.75% | 27.14 |

YOLOv7 | 97.66% | 91.93% | 94.35% | 73.38 |

YOLOv7x | 98.83% | 88.35% | 96.62% | 138.7 |

YOLOv7-tiny | 97.21% | 92.3% | 94.95% | 11.72 |

Model | Precision | Recall | Model Size/M | FPS |
---|---|---|---|---|

YOLOv7-tiny | 97.21% | 93.14% | 11.72 | 47.62 |

YOLOv7-MobileNetV3 | 93.13% | 81.3% | 8.25 | 64.52 |

YOLOv7-ME | 95.32% | 87.47% | 8.17 | 71.43 |

YOLOv7-MF | 94.43% | 91.3% | 8.23 | 67.11 |

YOLOv7-MEF | 98.94% | 96.42% | 9.1 | 76.92 |

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**MDPI and ACS Style**

Yang, L.; Liu, C.; Wang, C.; Wang, D.
Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7. *Agriculture* **2024**, *14*, 618.
https://doi.org/10.3390/agriculture14040618

**AMA Style**

Yang L, Liu C, Wang C, Wang D.
Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7. *Agriculture*. 2024; 14(4):618.
https://doi.org/10.3390/agriculture14040618

**Chicago/Turabian Style**

Yang, Lili, Chengman Liu, Changlong Wang, and Dongwei Wang.
2024. "Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7" *Agriculture* 14, no. 4: 618.
https://doi.org/10.3390/agriculture14040618