A Deep Learning-Based Vision System Combining Detection and Tracking for Fast On-Line Citrus Sorting

Chen, Yaohui; An, Xiaosong; Shu-min, Gao; Li, Shanjun; Kang, Hanwen

doi:10.3389/fpls.2021.622062

Cited by 27 publications

(22 citation statements)

References 29 publications

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“…Convolution Neural Network (CNN) achieves superior performance in computer vision tasks such as classification ( Chen et al, 2021 ), object detection ( Kang and Chen, 2020a ), and segmentation ( Kang and Chen, 2020b ). Here, we applied a 50-layer CNN model Residual-Network (ResNet-50) to directly predict the storage time of the oranges based on the images obtained, which was also treated as a classification task.…”

Section: Methodsmentioning

confidence: 99%

Non-destructive Storage Time Prediction of Newhall Navel Oranges Based on the Characteristics of Rind Oil Glands

Shu-min

Kang

et al. 2022

Front. Plant Sci.

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How to non-destructively and quickly estimate the storage time of citrus fruit is necessary and urgent for freshness control in the fruit market. As a feasibility study, we present a non-destructive method for storage time prediction of Newhall navel oranges by investigating the characteristics of the rind oil glands in this paper. Through the observation using a digital microscope, the oil glands were divided into three types and the change of their proportions could indicate the rind status as well as the storage time. Images of the rind of the oranges were taken in intervals of 10 days for 40 days, and they were used to train and test the proposed prediction models based on K-Nearest Neighbors (KNN) and deep learning algorithms, respectively. The KNN-based model demonstrated explicit features for storage time prediction based on the gland characteristics and reached a high accuracy of 93.0%, and the deep learning-based model attained an even higher accuracy of 96.0% due to its strong adaptability and robustness. The workflow presented can be readily replicated to develop non-destructive methods to predict the storage time of other types of citrus fruit with similar oil gland characteristics in different storage conditions featuring high efficiency and accuracy.

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Section: Methodsmentioning

confidence: 99%

Non-destructive Storage Time Prediction of Newhall Navel Oranges Based on the Characteristics of Rind Oil Glands

Shu-min

Kang

et al. 2022

Front. Plant Sci.

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“…Some researchers used a modified ResNet-50 model to extract the features of tomato surface defects and classify images of tomato defects ( Da Costa et al, 2020 ). Chen et al (2021) established an online citrus sorting system, shown in Figure 17 , and a detector named Mobile-citrus based on Mobile-V2 to identify surface defects in citrus. Then, the arms of robots arms pick out the defective ones with the linear Kalman filter model used in predicting the future path of the fruits.…”

Section: Convolutional Neural Network-based Fresh Fruit Detectionmentioning

confidence: 99%

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

et al. 2022

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As one of the representative algorithms of deep learning, a convolutional neural network (CNN) with the advantage of local perception and parameter sharing has been rapidly developed. CNN-based detection technology has been widely used in computer vision, natural language processing, and other fields. Fresh fruit production is an important socioeconomic activity, where CNN-based deep learning detection technology has been successfully applied to its important links. To the best of our knowledge, this review is the first on the whole production process of fresh fruit. We first introduced the network architecture and implementation principle of CNN and described the training process of a CNN-based deep learning model in detail. A large number of articles were investigated, which have made breakthroughs in response to challenges using CNN-based deep learning detection technology in important links of fresh fruit production including fruit flower detection, fruit detection, fruit harvesting, and fruit grading. Object detection based on CNN deep learning was elaborated from data acquisition to model training, and different detection methods based on CNN deep learning were compared in each link of the fresh fruit production. The investigation results of this review show that improved CNN deep learning models can give full play to detection potential by combining with the characteristics of each link of fruit production. The investigation results also imply that CNN-based detection may penetrate the challenges created by environmental issues, new area exploration, and multiple task execution of fresh fruit production in the future.

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“…Several types of the 3D visual sensors exist on the market, such as stereo cameras, Light Detection and Ranging sensors (LiDAR) and RGB-D (RGB-depth) cameras (Jayakumari et al 2021;Chen et al 2021). The practical applications of these cameras are shown in Fig 3, with the maximum number of applications focused on the stereo cameras.…”

Section: Sensorsmentioning

confidence: 99%

Intelligent Robots for Fruit Harvesting: Recent Developments and Future Challenges

Zhou

Wang

et al. 2021

Preprint

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Intelligent robots for fruit harvesting have been actively developed over the past decades to bridge the increasing gap between feeding a rapidly growing population and limited labour resources. Despite significant advancements in this field, widespread use of harvesting robots in orchards is yet to be seen. To identify the challenges and formulate future research and development directions, this work reviews the state-of-the-art of intelligent fruit harvesting robots by comparing their system architectures, visual perception approaches, fruit detachment methods and system performances. The potential reasons behind the inadequate performance of existing harvesting robots are analysed and a novel map of challenges and potential research directions is created, considering both environmental factors and user requirements.

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A Deep Learning-Based Vision System Combining Detection and Tracking for Fast On-Line Citrus Sorting

Cited by 27 publications

References 29 publications

Non-destructive Storage Time Prediction of Newhall Navel Oranges Based on the Characteristics of Rind Oil Glands

Non-destructive Storage Time Prediction of Newhall Navel Oranges Based on the Characteristics of Rind Oil Glands

Application of Convolutional Neural Network-Based Detection Methods in Fresh Fruit Production: A Comprehensive Review

Intelligent Robots for Fruit Harvesting: Recent Developments and Future Challenges

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