# Study on Lightweight Model of Maize Seeding Object Detection Based on YOLOv7
## 1 Introduction  
Maize is a strategic crop with the largest planting area and production in China. It provides an important guarantee for food security [1]. Rapid calculation of the seedling-emergence rate during seedling stage is crucial for predicting maize yield [2]. The traditional method is to manually calculate in the field, which requires a huge labor cost. To solve this problem, researchers attempt to use visual sensors and computer vision methods to achieve object detection. [PERSON] et al. [3] proposed a new crop-segmentation method (AP-HI) based on computer vision using spatial distribution characteristics to judge whether crops have reached the emergence stage. The average accuracy rate of AP-HI is 96.68%, which is higher than other detection algorithms by 2.37%. [PERSON] et al. [4] employed the conventional Otsu thresholding approach to segment rapeseed plant objects. The average relative error is 6.83%, while R2 is 84.6%. [PERSON] et al. [5] proposed a cotton-overlapping plant identification and counting method based on SVM and the maximum likelihood classification method that achieved an accuracy rate of 91.13%. However, using traditional object-detection methods to identify maize seedlings, the detection accuracy is affected seriously by the background of images. The error rate of object detection will increase with the complexity of the background (weeds, light, planting density, etc.). With the development of deep learning, methods based on Deep Convolutional Neural Networks (DCNN) have gradually replaced traditional methods in the field of object detection. In DCNN, parameters of a multi-layer network are fitted to the features, which can be approximated as expressions by functions. Multi-layer network can be used to extract the features of data and realize the positioning and classification of the objects. Object-detection models based on DCNN can be divided into two types: single stage and two stage. The two-stage object detection model obtains candidate boxes through an additional method (Selective Search [6], RPN [7], etc.) and inputs each candidate region into the deep network to extract features. Classification and boundary regression are performed for each candidate region. Typical two-stage object-detection models include R-CNN [8], Fast R-CNN [9], Faster R-CNN [10], etc. Now, two-stage object-detection models have been widely used in the field of smart agriculture. [PERSON] et al. [11] presented an enhanced version of the Faster R-CNN method to automatically detect and count sugarcane seedlings with the aid of aerial photography. The sugarcane detector had an average accuracy of 93.67%, and the average accuracy after the use of the seedling deduplication algorithm is 96.83%. [PERSON] et al. [12] introduced Mask-RCNN in strawberry robot detection to visualize the positioning of strawberry picking points. The average detection accuracy is 95.78%, and the recall is 95.41%. [PERSON] and colleagues [13] introduced an automatic detection approach for hydroponic lettuce seedlings using the improved Faster R-CNN model. Using the aforementioned enhanced method, the hydroponic lettuce seedling detection accuracy was determined to average 86.2%, surpassing that of other detectors such as RetinaNet, SSD, Cascade RCNN, and FCOS. As reported in their study, [PERSON] and colleagues [14] suggested a Faster RCNN-based approach for detecting various types of tomato diseases. Their experiments demonstrated that this method can effectively identify and differentiate different categories of tomato diseases with high accuracy. [PERSON] and colleagues [15] proposed a method for counting cotton seedlings in field environments that involves using the Faster R-CNN model with an Inception ResNetv2 feature extractor and a Kalman filter. Although two-stage object-detection models have achieved good results in the fields of smart agriculture, there are still many problems remaining.