# Detection of Miss-Seeding of Sweet Corn in a Plug Tay Using a Residual Attention Network
## 4 Conclusions  
To support the automatic detection of the missed sowing of corn seeds in a plug tray, we proposed a CBAM ResNet45 model. The CBAM ResNet45 obtains the channel features and spatial information from images. The model was trained using the images captured from the plug trays. In the process of creating the dataset, augmentation techniques were used to expand the dataset. To improve the recognition and classification performance of the model, we optimized the number of filters in the last convolutional layer of the model and also the position of the BN layer. The CBAM ResNet45 model has been experimentally tested and compared with other models, and the recognition accuracy of the method proposed in this paper reached 98%, which is better than that of the other tested neural networks. These results show that this method could be successfully applied to classify the seeds. The conclusions are as follows:  
1. Cost-saving and robust anti-interference: The method proposed in this paper did not need special instruments to collect the images, and it could directly classify the original images after simple pre-processing. Compared with the traditional machine vision-based classification, the model proposed in this paper eliminated the influence of subjective factors on threshold segmentation using pixel values and had a strong anti-interference ability.
2. Under the unchanged conditions for the training time and testing time, the CBAM-ResNet45 model enabled the neural network to adjust the weights of the channel flow  
Figure 12: Characteristics captured by the CBAM-ResNet45 model.  
and the spatial flow, extract more information about objects from the channel and space, and improve the accuracy of the recognition and classification of the network.
3. The results of the visualization experiments further verified that CBAM ResNet45 focused more on the concentrated feature areas. Moreover, the model also has disadvantages, e.g., the area of the corn seed was small. Moreover, when the plug tray moves with the conveyor belt, the vibration of the conveyor belt causes the falling of the proportion of the soil from the tray and its loss, which results in covering a part of the seed area or directly burying the seed so that the bare seed area becomes small. This leads to the problem of image misrecognition. Since it is difficult to avoid losing the soil and covering seeds caused by the vibration of the conveyor belt, the CBAM-ResNet45 model needs to be improved to correctly classify the pictures with small corn seed areas.  
This method provides a basis for automatic missing seeding detection in a plug tray. We will continue to conduct research on the classification and identification of seeds in small areas. The generalization ability of the CBAM-ResNet45 model needs to be improved to ensure its success.  
Conceptualization, L.G., F.H. and J.B.; methodology, L.G.; software, L.G. and J.B; validation, L.G.; formal analysis, L.G., F.H. and J.B.; investigation, F.H. and D.M.; resources, F.H.; data curation, J.X. and B.D.; writing--original draft preparation, L.G.; writing--review and editing, F.H., J.Z. and J.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Major Science and Technology Innovation Project of Shandong Province (2019 JZZY020603), Shandong Provincial Natural Science Foundation (ZR2022 MC152) and Qingdao Science and Technology to Benefit the people Demonstration guide Special project (22-3-7-xdrny-18-nsh).  
Not applicable.  
The data that support the findings of the study are available from the corresponding author, [PERSON], upon reasonable request. We have uploaded the dataset to Baidu cloud drive. The link is: [[https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) SeFIJUZejz3 PcK3 OOEaVGOA]([https://pan.baidu.com/s/1](https://pan.baidu.com/s/1) SeFIJUZejz3 PcK3 OOEaVGOA).  
The authors declare no conflict of interest.