Rice seedling row detection based on morphological anchor points of rice stems

Li, Dongfang; Li, Boliao; Long, Sifang; Feng, Huaiqu; Xi, Te; Kang, Shuo; Wang, Jun

doi:10.1016/j.biosystemseng.2022.12.012

Cited by 21 publications

(8 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wei et al [14] proposed a lightweight CNN GhostNet based on row anchor selection for crop row recognition early in the growth stage, and the recognition achieved the accuracy of 97.90% in the test. Li et al [15] used a semantic segmentation model based on the Transformer to recognize the triangular morphological mask on the stems of individual rice seedlings. Then a clustering algorithm based on the dynamic search direction was proposed to divide the sparsely distributed anchor points into rows while completing the seedling row fitting.…”

Section: Review Of Methodsmentioning

confidence: 99%

An identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion

Wang,

Yu,

Wang

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

In this paper, an identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion is proposed, which can accurately obtain the lateral deviation between the weeding components and the seedling rows under different soil hardness in paddy fields to avoid crushing seedlings. The proposed method first fuses the RGB images with depth images to obtain the three-dimensional point cloud of seedlings, establishes a visual calibration system to calibrate the positions of the weeding component at the limit positions in the camera coordinate system, then obtains the relative pose relationship between the camera coordinate system and the ground coordinate system based on the inertial measurement unit (IMU) to solve the influence of the altitude change of the camera on the identification of the lateral correction amount, and finally calculates the lateral correction amount based on the lateral deviation model in the ground coordinate system. The experimental platforms for the visual calibration of the weeding components and the identification of the lateral correction amount were established. The experimental results showed that the mean positioning error of the weeding components was 2.766 mm, the mean identification error of the lateral correction amount did not exceed 0.22 cm, and the standard deviation of the identification error did not exceed 0.18 cm.

show abstract

Section: Review Of Methodsmentioning

confidence: 99%

An identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion

Wang,

Yu,

Wang

2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…After obtaining the crop location points, the next step is to group them into different crop rows. Cluster analysis is a common method to determine the true crop rows based on the distance relationship between coordinate points and estimated crop rows [42,43]. Given that the crop rows extend in the positive x-direction, computing clustering using only the y-values allows the points to be grouped into different rows.…”

Section: Crop Position Cluster Analysismentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Accurate crop recognition is a prerequisite for intelligent agricultural machine operations, such as crop navigation, automatic row alignment of weeding components and precise drug application. [1][2][3][4][5][6][7] However, unlike dry-field environments, the rice-growing environment is very specific, with complex backgrounds, aquatic plants and visual disturbance conditions. 8,9 Current mainstream visual perception methods do not meet the recognition requirements for rice plants, making it difficult to apply and promote mature intelligent technologies and equipment in paddy field environments.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Accurate recognition of rice plants based on visual and tactile sensing

Chen,

Dang,

Zhang

et al. 2024

J Sci Food Agric

View full text Add to dashboard Cite

BACKGROUNDCrop recognition is the basis of intelligent agricultural machine operations. Visual perception methods have achieved high recognition accuracy. However, the reliability of such methods is difficult to guarantee because of the complex environment of paddy fields. Tactile sensing methods are not affected by background or environmental interference, and have high reliability. However, in an ideal environment, the recognition accuracy is not as high as that of the visual method.RESULTSTo balance the accuracy and reliability of rice plant recognition, a combined visual–tactile method was proposed in this study. A rice plant recognition device was developed with a poly(vinylidene fluoride) sensor embedded inside the device as a tactile perceptron and a graphic designed as a visual perceptron. The primary role of the tactile perceptron is to initially recognize rice plants and provide a time point for image capture for visual perception. The main role of the visual perceptron is to extract features from the captured images and recognize rice plants again. The results of tactile and visual recognition were eventually fused to achieve accurate recognition of rice plants.CONCLUSIONThe contact speed between the recognition perceptron and rice–weed was selected for the field performance test based on the real situation of paddy field operation. The results showed that the accuracy and reliability of rice plant recognition decreased as the travelling speed of the paddy field operation machine increased. The results of this study provide a basis for intelligent farm machinery operations in rice fields. © 2024 Society of Chemical Industry.

show abstract

Rice seedling row detection based on morphological anchor points of rice stems

Cited by 21 publications

References 23 publications

An identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion

An identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion

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

Accurate recognition of rice plants based on visual and tactile sensing

Contact Info

Product

Resources

About