Deep learning‐based crop row detection for infield navigation of agri‐robots

de Silva, Rajitha; Cielniak, Grzegorz; Wang, Gang; Gao, Junfeng

doi:10.1002/rob.22238

Cited by 12 publications

(3 citation statements)

References 36 publications

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“…There are also works that, although not originally made for SLAM, are related to visual navigation, such as de Silva et al (2021), which creates a dataset of marked rows in a sugar beet field, Aghi et al (2021), which creates a semantically segmented dataset of vineyard rows, and Smitt et al (2021) which present an automated platform for surveying sweet pepper and tomato crops using a pipe-rail trolley with an array of RGB-D cameras and a tracking camera inside a greenhouse.…”

Section: Related Workmentioning

confidence: 99%

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

Marzoa Tanco,

Trinidad Barnech,

Andrade

et al. 2023

The International Journal of Robotics Research

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The agricultural industry is being transformed, thanks to recent innovations in computer vision and deep learning. However, the lack of specific datasets collected in natural agricultural environments is, arguably, the main bottleneck for novel discoveries and benchmarking. The present work provides a novel dataset, Magro, and a framework to expand data collection. We present the first version of the Magro Dataset V1.0, consisting of nine ROS bags (and the corresponding raw data) containing data collected in apple and pear crops. Data were gathered, repeating a fixed trajectory on different days under different illumination and weather conditions. To support the evaluation of loop closure algorithms, the trajectories are designed to have loop closures, revisiting some places from different viewpoints. We use a Clearpath’s Jackal robot equipped with stereo cameras pointing to the front and left side, a 3D LIDAR, three inertial measurement units (IMU), and wheel encoders. Additionally, we provide calibrated RTK GPS data that can be used as ground truth. Our dataset is openly available, and it will be updated to have more data and variability. Finally, we tested two existing state-of-the-art algorithms for vision and point cloud-based localization and mapping on our novel dataset to validate the dataset’s usability.

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Section: Related Workmentioning

confidence: 99%

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

Marzoa Tanco,

Trinidad Barnech,

Andrade

et al. 2023

The International Journal of Robotics Research

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show abstract

“…In recent years, the combination of deep learning algorithms and vision sensors has received widespread attention in the field of crop row detection. Crop row detection work based on deep learning is mainly implemented by predicting crop row masks as binary images through image segmentation methods [12]. UNet is a relatively common and simple semantic segmentation network in the field of crop row detection [13].…”

Section: Introductionmentioning

confidence: 99%

“…UNet is a relatively common and simple semantic segmentation network in the field of crop row detection [13]. Li et al [14], Yang et al [15], De Silva et al [12], and Diao et al [16] respectively enhanced and optimized the traditional UNet network in different aspects, to improve the segmentation accuracy of crop rows and backgrounds, and reduce the training time. Aiming at the problem of inhomogeneous contours of strawberry crop rows, Ponnambalam et al [17] implemented the identification of strawberry crop rows and the fitting of traversable area trajectories based on an improved SegNet network and an adaptive multi-ROI algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

Shi,

Bai,

Zhou

et al. 2023

Agriculture

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Field crops are usually planted in rows, and accurate identification and extraction of crop row centerline is the key to realize autonomous navigation and safe operation of agricultural machinery. However, the diversity of crop species and morphology, as well as field noise such as weeds and light, often lead to poor crop detection in complex farming environments. In addition, the curvature of crop rows also poses a challenge to the safety of farm machinery during travel. In this study, a combined multi-crop row centerline extraction algorithm is proposed based on improved YOLOv8 (You Only Look Once-v8) model, threshold DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering, least squares method, and B-spline curves. For the detection of multiple crops, a DCGA-YOLOv8 model is developed by introducing deformable convolution and global attention mechanism (GAM) on the original YOLOv8 model. The introduction of deformable convolution can obtain more fine-grained spatial information and adapt to crops of different sizes and shapes, while the combination of GAM can pay more attention to the important feature areas of crops. The experimental results shown that the F1-score and mAP value of the DCGA-YOLOv8 model for Cabbage, Kohlrabi, and Rice are 96.4%, 97.1%, 95.9% and 98.9%, 99.2%, 99.1%, respectively, which has good generalization and robustness. A threshold-DBSCAN algorithm was proposed to implement clustering for each row of crops. The correct clustering rate for Cabbage, Kohlrabi and Rice reaches 98.9%, 97.9%, and 100%, respectively. And LSM and cubic B-spline curve methods were applied to fit straight and curved crop rows, respectively. In addition, this study constructed a risk optimization function for the wheel model to further improve the safety of agricultural machines operating between crop rows. This indicates that the proposed method can effectively realize the accurate recognition and extraction of navigation lines of different crops in complex farmland environment, and improve the safety and stability of visual navigation and field operation of agricultural machines.

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Navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet

Guo,

Diao,

Zhao

et al. 2024

Journal of Field Robotics

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The continuous and close combination of artificial intelligence technology and agriculture promotes the rapid development of smart agriculture, among which the agricultural robot navigation line recognition algorithm based on deep learning has achieved great success in detection accuracy and detection speed. However, there are still many problems, such as the large size of the algorithm is difficult to deploy in hardware equipment, and the accuracy and speed of crop row detection in real farmland environment are low. To solve the above problems, this paper proposed a navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet. First, the Convolution (Conv) module and C2f module of YOLOv8s network are replaced with Depthwise Convolution (DWConv) module and PP‐LCNet module respectively to reduce the parameters (Params) and giga floating‐point operations per second of the network, so as to achieve the purpose of network lightweight. Second, to reduce the precision loss caused by network lightweight, the spatial pyramid pooling fast module in the backbone network is changed to atrous spatial pyramid pooling faster module to improve the accuracy of network feature extraction. Meanwhile, normalization‐based attention module is introduced into the network to improve the network's attention to corn plants. Then the corn plant was located by using the midpoint of the corn plant detection box. Finally, the least square method is used to extract the corn crop row line, and the middle line of the corn crop row line is the navigation line of the corn spraying robot. From the experimental results, it can be seen that the navigation line extraction algorithm proposed in this paper ensures both the real‐time and accuracy of the navigation line extraction of the corn spraying robot, which contributes to the development of the visual navigation technology of agricultural robots.

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Deep learning‐based crop row detection for infield navigation of agri‐robots

Cited by 12 publications

References 36 publications

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

MAgro dataset: A dataset for simultaneous localization and mapping in agricultural environments

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

Navigation line extraction algorithm for corn spraying robot based on YOLOv8s‐CornNet

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