A segmentation method of bagged green apple image

Lv, Jidong; Wang, Fan; Xu, Liming; Ma, Zhenghua; Yang, Biao

doi:10.1016/j.scienta.2018.11.030

Cited by 49 publications

(22 citation statements)

References 1 publication

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“…After computing the ExGR index, Otsu threshold segmentation was used to separate the vegetation and soil by determining a pixel-value threshold. As a threshold segmentation method, Otsu has good performance, and the optimal threshold value was determined by calculating the minimum intra-class variance and maximum inter-class variance using only the gray level histogram of the image [39]. Many pixels around the tops of tree-crowns were classified as soil due to their high digital number (DN) value, which was close to the DN value of soil.…”

Section: Separation Of Vegetation and Soilmentioning

confidence: 99%

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Dong

Zhang

et al. 2020

Remote Sensing

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The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees.

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Section: Separation Of Vegetation and Soilmentioning

confidence: 99%

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Dong

Zhang

et al. 2020

Remote Sensing

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“…In [13] an automatic seed region growing method based on the region with the combination of the Watershed algorithm with seed region growing algorithm showing good results was proposed. Lv et al [14] give a segmentation method of bagged green apples in which the linear contrast of the RGB colour image was enhanced to improve colour difference. Then, the Otsu method was applied to get the segmented image with good results for more complex fruit regions of bagged green fruits based on the method.…”

Section: Literature Reviewmentioning

confidence: 99%

Segmentation using fuzzy cluster‐based thresholding method for apple fruit sorting

Henila

Chithra

2020

IET image process

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Apple fruit sorting has been an important postharvest process carried on for the sorting of diseased apple fruits. A fuzzy cluster-based thresholding (FCBT) method for segmenting the region of interest from an apple image has been proposed for sorting apples in this study. As the first step, the acquired RGB colour image of an apple fruit was converted into a greyscale image. Then, five different fuzzy cluster bins with overlapped pixel ranges were taken and greypixel values were binned into them. A cluster with the maximum number of pixels was selected for calculating the threshold value. The region of interest from the apple image was then segmented using the proposed FCBT value. Features extracted from the segmented images were given as input to a fully-connected deep neural network for a classification. The performance of the FCBT method was compared with similar greyscale thresholding methods like Otsu's and Kapur's methods. The visual segmentation accuracy and the execution speed showed that the FCBT outperformed the other methods in segmenting the diseased area. A fullyconnected deep neural network model with the FCBT image extracted features as input values gave a 98.33% accuracy rate in sorting the apple images. 10 IET Image Process.

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“…Green pepper and leaves are similar in color, so the recognition by color characteristics cannot ensure the accuracy [20]. In this section, the shape feature is extracted.…”

Section: ) Shape Feature Extractionmentioning

confidence: 99%

Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

Chen

et al. 2019

IEEE Access

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In the green pepper harvesting robot, the color of green pepper is similar to that of leaves, which makes it difficult to recognize the green pepper target. In order to solve this problem, a green pepper recognition method based on least-squares support vector machine optimized by the improved particle swarm optimization (IPSO-LSSVM) is proposed in this paper. Firstly, the green pepper images are segmented by K-Means method under the Lab color space, and the segmentation images of the target and background are obtained. The processed green pepper image was divided into training and testing samples. Then, the shape and texture features of green pepper targets are extracted separately from the training sample using the hu invariant moment and Tamura texture feature. Meanwhile, in order to reduce the complexity of data calculations and improve the efficiency, the extracted feature vectors are normalized. The feature vector is used as the input eigenvector of the least-squares support vector machine (LSSVM). The particle swarm optimization algorithm is used to obtain the optimal regularization parameter and the kernel function width. In order to maintain the particle activity, the mutation strategy is introduced to improve the particle swarm optimization algorithm. The experimental results show that the recognition rate of IPSO-LSSVM is higher than that of other methods, and the recognition accuracy is 89.04%. It could meet the requirements of green pepper identification.

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A segmentation method of bagged green apple image

Cited by 49 publications

References 1 publication

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard

Segmentation using fuzzy cluster‐based thresholding method for apple fruit sorting

Recognition Method of Green Pepper in Greenhouse Based on Least-Squares Support Vector Machine Optimized by the Improved Particle Swarm Optimization

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