CRowNet: Deep Network for Crop Row Detection in UAV Images

Bah, Mamadou Dian; Hafiane, Adel; Canals, Raphaël

doi:10.1109/access.2019.2960873

Cited by 101 publications

(48 citation statements)

References 42 publications

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“…Precision is the percentage of accurately retrieved crop rows to the total number of retrieved rows. Bah et al combined a convolutional neural network and the Hough transform to retrieve beet rows in RGB images with a row spacing of 50 cm and an image spatial resolution of approximately 1 cm, and they presented a mean recall value of 0.70 and a mean precision value of 0.90 [32]. In this study, we used the CRDA value to evaluate the performance of different methods for crop row detection.…”

Section: Comparison Of Crop Row Detection Results With Those Of Other Studies Using Uav Imagesmentioning

confidence: 99%

A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

Chen

Wang

et al. 2021

Remote Sensing

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Crop row detection using unmanned aerial vehicle (UAV) images is very helpful for precision agriculture, enabling one to delineate site-specific management zones and to perform precision weeding. For crop row detection in UAV images, the commonly used Hough transform-based method is not sufficiently accurate. Thus, the purpose of this study is to design a new method for crop row detection in orthomosaic UAV images. For this purpose, nitrogen field experiments involving cotton and nitrogen and water field experiments involving wheat were conducted to create different scenarios for crop rows. During the peak square growth stage of cotton and the jointing growth stage of wheat, multispectral UAV images were acquired. Based on these data, a new crop detection method based on least squares fitting was proposed and compared with a Hough transform-based method that uses the same strategy to preprocess images. The crop row detection accuracy (CRDA) was used to evaluate the performance of the different methods. The results showed that the newly proposed method had CRDA values between 0.99 and 1.00 for different nitrogen levels of cotton and CRDA values between 0.66 and 0.82 for different nitrogen and water levels of wheat. In contrast, the Hough transform method had CRDA values between 0.93 and 0.98 for different nitrogen levels of cotton and CRDA values between 0.31 and 0.53 for different nitrogen and water levels of wheat. Thus, the newly proposed method outperforms the Hough transform method. An effective tool for crop row detection using orthomosaic UAV images is proposed herein.

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Section: Comparison Of Crop Row Detection Results With Those Of Other Studies Using Uav Imagesmentioning

confidence: 99%

A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

Chen

Wang

et al. 2021

Remote Sensing

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show abstract

“…Hence, the discrimination accuracy and distinctiveness requirements of a crop rows detection algorithm varies depending on the application. Mature results have been presented in the literature with the use of deep convolutional neural networks [4] with the objective of increasing detection accuracy and reducing the fault rate for weeding applications and offline planning. On the other hand, for autonomous navigation applications, crop rows are identified as a local feature in a framework that takes the inaccuracy of feature extraction as an input to a model matching and filtering step [5,17].…”

Section: Related Workmentioning

confidence: 99%

LiDAR-based Structure Tracking for Agricultural Robots: Application to Autonomous Navigation in Vineyards

Nehme¹,

Aubry²,

Solatges³

et al. 2021

J Intell Robot Syst

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“…The results of multiview coplanar extraction using the proposed approach are evaluated by the quantitative metrics, i.e., recall, precision, and intersection over union (IoU), which can be computed as [47] Recall = (R GT ∩ R q )/R GT , (…”

Section: Performance Evaluation Of Multiview Coplanar Extractionmentioning

confidence: 99%

Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

Cheng

et al. 2021

Remote Sensing

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Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building façades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.

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CRowNet: Deep Network for Crop Row Detection in UAV Images

Cited by 101 publications

References 42 publications

A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

A New Method for Crop Row Detection Using Unmanned Aerial Vehicle Images

LiDAR-based Structure Tracking for Agricultural Robots: Application to Autonomous Navigation in Vineyards

Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping

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