Recognition of green apples based on fuzzy set theory and manifold ranking algorithm

Sun, Sashuang; Wu, Qian; Jiang, Lin; Lu, Yan; He, Dongjian; Song, Huaibo

doi:10.1016/j.ijleo.2018.03.085

Cited by 28 publications

(11 citation statements)

References 9 publications

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“…Although our human brain is proficient in classifying visual objects through our eyes, how the visual features take effect in the classification is still remaining unclear. Conventional machine learning methods have often been fulfilled directly by applying a broad spectrum of visual features from digital images [ 20 , 25 , 26 , 30 , 31 , 41 , 48 , 49 , 62 , 64 ]. In fact, through long-time observations, experiments and evolutions, machine learning algorithms are subsequently employed to classify visual objects in the given images based on these visual features.…”

Section: Related Workmentioning

confidence: 99%

Apple Ripeness Identification Using Deep Learning

Xiao

Nguyen

Yan

2021

Communications in Computer and Information Science

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Deep learning models assist us in fruit classification, which allow us to use digital images from cameras to classify a fruit and find its class of ripeness automatically. Apple ripeness classification is a problem in computer vision and deep learning for pattern classification. In this paper, the ripeness of apples in digital images will be classified by using convolutional neural networks (CNN or ConvNets) in deep learning. The goal of this project is to verify the capability of deep learning models for fruit classification so as to lessen our human labor. Our experiments consist of four parts, namely, image preprocessing, apple detection, ripeness classification, and resultant evaluations. The contribution of this paper is that the classifiers are able to achieve the best result, i.e., the ripeness class of an apple from a given digital image is able to be precisely predicted. We have optimized the deep learning models and trained the classifiers so as to achieve the best outcome.

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Section: Related Workmentioning

confidence: 99%

Apple Ripeness Identification Using Deep Learning

Xiao

Nguyen

Yan

2021

Communications in Computer and Information Science

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“…Sengupta et al [12] developed an algorithm including Hough circle detection, texture classification based on SVM and other false positive removal techniques to detect immature citrus in green canopy under natural conditions. Sun et al [13] designed an apple target segmentation method by fusing fuzzy set theory and the manifold ranking algorithm to carry out the recognition of green apples in similar background areas. Lv et al [14] conducted the detection of apple fruit by OTSU dynamic threshold segmentation method, edge detection and improved RHT transformation method.…”

Section: Introductionmentioning

confidence: 99%

Apple Detection in Natural Environment Using Deep Learning Algorithms

et al. 2020

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It is a challenging problem to detect the apple in natural environment using traditional object recognition algorithms due to occlusion, fluctuating illumination and complex backgrounds. Deep learning methods for object detection make impressive progress, which can automatically extract the number, pixel position, size and other features of apples from the images. In this paper, four deep learning recognition models, Faster RCNN based on AlexNet, Faster RCNN based on ResNet101, YOLOv3 based on DarkNet53 and improved YOLOv3 were employed to carry out recognition experiments on red and green apple under three illumination and two image sharpness conditions, with the transfer learning to accelerate the training process. The results showed that improved YOLOv3 model had the best recognition effect among the four detection models. F1 value of red apple recognition was 95.0%, 94.6% and 94.1% for normal, insufficient and excessive illumination, respectively, and F1 value of green apple recognition was 94.9%, 94.0% and 91.1%. There were F1 value of 92.8% and 92.1% for red and green apple recognition in blurred images, respectively. Moreover, improved YOLOv3 algorithm still had the better performance for occlusion, spot, overlap and incomplete apples, with a recognition recall rate higher than 88.5%. It can be concluded that improved YOLOv3 algorithm can provide a more efficient way for apple detection in natural environment.

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“…[19][20][21][22][23][24] However, when there is nosignificant color difference between plants and background, there is no robust and high-precision method for maize canopy segmentation. [25] In view of the above segmentation problem, in the article, the HLS-SVM method was used in this study to segment maize canopy image. Firstly, the canopy image of maize at seedling stage was segmented by the HLS mask; secondly, the linear SVM was used to segment the central area of the maize canopy.…”

Section: Introductionmentioning

confidence: 99%

Grayscale distribution of maize canopy based on HLS-SVM method

Wang

Jiang

Zhang

et al. 2020

International Journal of Food Properties

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It is a crucial step locating the maize plant or even its target location precisely during the intelligent agricultural equipment working in farmland. Therefore, the segmentation of plants from the background image is one of the important research contents of agricultural machine vision. Under the background of significant color difference, the current method can effectively complete maize canopy segmentation and plant location identification. However, under the background of no-significant color difference, there is no robust and high-precision method for maize canopy segmentation and plant location identification. In this study, it was found that the grayscale of maize canopy had gradient distribution trend along the radial direction. The Hue Saturation Value color space and Support Vector Machine method was used to segment 600 maize canopy images, then the polynomial regression method was used to find out the functional relationship between grayscale gradient and canopy diameter. The functional relationship gave identification results of canopy central region under different gray gradient distribution. The result provided a theoretical basis for accurate identification and rapid location of maize plant center at seedling stage, and provided accurate position coordinate and yaw information for field navigation of agricultural intelligent equipment such as plant protection UAV.

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Recognition of green apples based on fuzzy set theory and manifold ranking algorithm

Cited by 28 publications

References 9 publications

Apple Ripeness Identification Using Deep Learning

Apple Ripeness Identification Using Deep Learning

Apple Detection in Natural Environment Using Deep Learning Algorithms

Grayscale distribution of maize canopy based on HLS-SVM method

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