Precision agriculture is an advanced concept in the field of modern agriculture, which makes agricultural production more refined and efficient with the help of high-tech means to realize the efficient use of resources and sustainable development of agriculture. In this process, agricultural robots play a pivotal role. Equipped with a variety of sensors and advanced algorithms, these intelligent robots can efficiently complete a variety of farmland operations, such as seeding, weeding, medication, fertilizer application, and harvesting [
1]. Agricultural robots require precise guidance when working on farmland, and in this regard, the main technologies currently relied upon include the global navigation satellite system (GNSS), light detection and ranging (LiDAR), and computer vision. GNSS can provide agricultural robots with precise positional information to assist in localization and navigation.
Table 1 summarizes the advantages and disadvantages of the three agricultural navigation methods, GNSS, LiDAR, and computer vision. In the selection of agricultural navigation technologies, GNSS provides robust support for agricultural operations with its high-precision positioning capabilities. However, it has limitations, such as signal interference under certain weather conditions or when obstructed by backgrounds, which can affect positioning accuracy. Additionally, high-precision receivers tend to have relatively high costs. LiDAR technology, on the other hand, exhibits unique advantages in complex terrain and obstacle detection through its non-contact high-precision measurement. Nonetheless, when faced with severe weather conditions, its ranging and imaging quality can be significantly impacted. In contrast, the advantages of computer vision technology in agricultural navigation are becoming increasingly apparent. It combines advanced image processing and recognition algorithms to analyze farmland images in real time, accurately monitoring crop growth, pest and disease situations, and soil conditions. Furthermore, computer vision technology possesses high flexibility and scalability, enabling integration with various devices such as drones and smart agricultural machinery for comprehensive and multi-angle agricultural monitoring [
2,
3,
4].
With the advancement of deep learning, its rapid image processing capabilities, high accuracy, and robustness have gradually replaced traditional machine learning algorithms. Among these, object detection models such as the two-stage R-CNN model and the single-stage YOLO model [
5,
6], as well as semantic segmentation models like the UNet model [
7], are widely utilized in the agricultural sector. This lays the foundation for deep learning-based visual navigation methods, fulfilling the needs of agricultural production. Secondly, because of their more efficient processing speed, deep learning models can process large amounts of image data quickly on high-performance computing devices. This enables deep learning-based visual navigation methods to realize real-time navigation and operations to meet the needs of agricultural production. Finally, they have a wider range of applications, because deep learning models can process various types of image data, including color images, grayscale images, and multispectral images. This allows deep learning-based visual navigation methods to be applied to various types of agricultural equipment, including tractors, harvesters, and plant protection machines. Deep learning technology has been developing rapidly in recent years, and it has also been widely used in the field of computer vision, while also promoting the rapid development of agricultural robot navigation technology.
Researchers at home and abroad have conducted a large amount of research in the field of agricultural robot navigation based on deep learning, and some of the research results are shown in
Table 2. In order to extract the navigation lines in different field scenes, Yu et al. [
8] compared several semantic segmentation networks, and finally chose the ENet network, which has higher speed and accuracy, to segment the field roads, and at the same time, utilized the improved polygon fitting method to extract the navigation lines. Although the accuracy of the extracted navigation lines was high, the datasets of the above studies were collected in experimental greenhouses, and have not been verified for their effectiveness in a real and complex farmland environment. In order to overcome the influence of rice plant morphological differences on rice row detection, Li et al. [
9] firstly segmented the rice stalks using a transformer-based semantic segmentation network, secondly used the triangulation method to locate the anchors of the rice crop rows, then used the improved clustering algorithm to cluster the anchors of the rice crop rows, and finally detected the rice crop row lines by using the least squares method. In order to detect the boundary lines of farmland in different environments, He et al. [
10] used a combination of an improved UNet network and improved multi-boundary line detection algorithm to detect the boundary lines of farmland areas. In order to detect curved rice crop rows, Liu et al. [
11] finally chose to use single-stage network MobileNet-SSD to detect rice plants through comparative experiments, used the midpoint of the detection frame to locate the feature points of rice crop rows, and finally used the least squares method to detect rice crop rows. In order to detect corn seedlings in different growth stages and in complex farmland environments, Quan et al. [
12] finally chose to replace the backbone network of the two-stage target detection network Faster-RCNN with VGG19 through comparative experiments, so as to realize the accurate identification of corn seedlings. In order to be able to detect corn crop rows in real time, Yang et al. [
13] first used YOLOv5 network to detect the corn crop row segments, after grayscaling and binarization of the crop rows in the detection frame, next used FAST corner point detection technique to locate the crop row feature points, and finally used least squares method to detect the corn crop row lines. In order to accurately recognize crops and weeds, Jiang et al. [
14] combined the proposed graph convolutional network with ResNet-101 network, thus realizing the accurate recognition of crops and weeds. In order to detect corn crop rows in complex farmland environments, Diao et al. [
15] combined the spatial pyramid pooling structure ASPP with the UNet network for more accurate segmentation of crop rows and backgrounds; the improved vertical projection method was then used to locate the crop row feature points, and finally the least squares method was used to detect corn crop row lines. Zhang et al. [
16] first used the improved YOLOv3 network to detect rice crop row segments, then clustered and grayscaled the detection frames, used the SUSAN corner detection algorithm to locate the crop row feature points, and finally used the least squares method to detect the rice crop rows. Yang et al. [
17] first used the modified UNet network to segment the crop rows, then used the left and right edge centerline method to locate the feature points of the crop rows and performed a clustering operation on the feature points, and finally used the least squares method to detect the crop row lines. In order to detect crop rows in complex farmland environments, Hu et al. [
18] first detected crop row segments using the improved YOLOv4 network, then performed clustering operations on the detection frames and localized the crop row feature points using the mean value method, and finally detected the crop row lines using the least squares method. Bah et al. [
19] first segmented the crop rows using the improved SegNet network, and then detected the crop row lines using the Hough transform. In order to reduce the impact of a complex paddy field environment on rice crop row detection, Wang et al. [
20] first used the improved YOLOv5 network to detect rice crop row segments, and then used the improved centerline recognition algorithm to detect rice crop rows. Although all of the above deep learning-based navigation line recognition algorithms for agricultural robots can better recognize crop row lines, the experimental conditions of the algorithms are relatively homogeneous, and their effectiveness in detecting crop rows at different growth stages has not been verified.
In order to solve the above problems, an algorithm for recognizing corn crop rows during different growth stages is proposed in this paper, based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages is constructed in this paper; secondly, the improved YOLOv8s network is utilized to detect the corn crop row segments: then the crop rows and the backgrounds in the detection frame are segmented using the improved supergreen method; and finally, the corn crop row lines are detected using the proposed local–global detection method.