Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model

Liu, Tianhu; Ehsani, Reza; Toudeshki, Arash; Zou, Xiangjun; Wang, Hongjun

doi:10.1016/j.compind.2018.03.007

Cited by 35 publications

(12 citation statements)

References 11 publications

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“…However, the lower parts of a tree crown with many pendulous branches caused by self-weighting will disrupt the uniform shape. Trunk knots or twigs originating from a trunk will result in horizontal cross sections of trunks without well-defined circle shapes, which will complicate the location of trunks based on circle-detection algorithms such as Hough transform [48] or cylinder fitting [49]. Meanwhile, the accuracy of trunk detection algorithms based on MLS and TLS data would markedly decrease when understory vegetation is present in the forest plot, which results in the generation of much more occlusion.…”

Section: The Advantages Of Our Approachmentioning

confidence: 99%

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Wang

Chen

Cao

et al. 2019

Forests

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Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.

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Section: The Advantages Of Our Approachmentioning

confidence: 99%

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Wang

Chen

Cao

et al. 2019

Forests

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“…The proposed algorithm allows to obtain a recall ratio of 92.14% and an accuracy ratio of 95.49%. Finally, the citrus tree detection using a camera presented in [18], performs with a true-positive ratio of 90.8% and a false positive-ratio of 95.49%.…”

Section: Methodsmentioning

confidence: 99%

“…In [18], the authors segment images of citrus trees to extract the areas corresponding to the fruits and trunks. They use elliptical regions in the YCrCb color space to extract the fruits and trunks areas from the background.…”

Section: B Tree Detectionmentioning

confidence: 99%

Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Durand-Petiteville

Flécher

Cadenat

et al. 2018

IEEE Robot. Autom. Lett.

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This paper deals with autonomous farming and with the autonomous navigation of an agricultural robot in orchards. These latter are typical semi-structured environments where the dense canopy prevents from using GPS signal and embedded sensors are often preferred to localize the vehicle. To move safely in such environments, it is necessary to provide the robot the ability of detecting and localizing trees. This paper focuses on this problem. It presents a low cost but efficient vision-based system allowing to detect accurately, quickly and robustly the trees. It is made of four stereo cameras which provide a point cloud characterizing the environment. The key idea is to find the tree trunks by detecting their shadows which are materialized by concavities in the obtained point cloud. In this way, branches and leaves are not taken into account, improving the detection robustness and therefore the navigation strategy. The method has been implemented using ROS and validated using data sequences taken in several different orchards. The obtained results definitely validate the approach and its performances show that the processing time (around 1ms) is sufficiently short for the data to be used at the control level. A comparison with other approaches from the literature is also provided.

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“…Lin et al [14] excluded backgrounds based on depth filter and Bayes-classifier, then a density clustering method was used to group adjacent points into clusters which might be citrus, and false positives were removed by color, gradient, and geometry feature based SVM classifier with an F1 score of 0.9197. Liu et al [15] proposed a method that constructed a multi-elliptical boundary model in Cr-Cb coordinates to detect citrus fruit and tree trunks in natural light environments with correct and false positive percentages of 90.8% and 11.2%, respectively. An adaptive enhanced red and green chromatic map was generated from an illumination-compensated image by Zhuang et al [16] , then some image segmentation and preprocessing methods like marker-controlled watershed transform and convex hull operation methods were used to locate potential citrus regions.…”

Section: Introductionmentioning

confidence: 99%

Superpixel-based segmentation algorithm for mature citrus

Yang

Chen

Xun

et al. 2020

International Journal of Agricultural and Biological Engineering

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With the decrease of agricultural labors and the increase in production costs, harvesting robots have become a research hotspot in recent years. To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions, an image segmentation algorithm based on superpixel was proposed. Efficient simple linear iterative clustering (SLIC) algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels. The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks (BPNN) classifier in the next step. The adjacency information of superpixels was considered by calculating the global-local binary pattern (LBP) in R component images when extracting texture features. To accelerate the classification process, the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels. These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77% and pixel-level accuracy of 94.96%, while the average time to segment one image was 0.4778 s. Therefore, the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently.

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Detection of citrus fruit and tree trunks in natural environments using a multi-elliptical boundary model

Cited by 35 publications

References 11 publications

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

Tree Detection With Low-Cost Three-Dimensional Sensors for Autonomous Navigation in Orchards

Superpixel-based segmentation algorithm for mature citrus

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