20.7.3.7.8 Fruit Detection, Fruit Inspection, Apples, Oranges

Chapter Contents (Back)
Fruit. Application, Fruit. For the trees:
See also Citrus Trees, Orchards, Diseases.
See also Apple Trees, Plantations, Analysis, Diseases.

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Hameed, K.[Khurram], Chai, D.[Douglas], Rassau, A.[Alexander],
A comprehensive review of fruit and vegetable classification techniques,
IVC(80), 2018, pp. 24-44.
Elsevier DOI 1812
Recognition, Classification, Fruit, Vegetable, Produce classification, Machine learning BibRef

Lobos, G.A.[Gustavo A.], Escobar-Opazo, A.[Alejandro], Estrada, F.[Félix], Romero-Bravo, S.[Sebastián], Garriga, M.[Miguel], del Pozo, A.[Alejandro], Poblete-Echeverría, C.[Carlos], Gonzalez-Talice, J.[Jaime], González-Martinez, L.[Luis], Caligari, P.[Peter],
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Grapes Visual Segmentation for Harvesting Robots Using Local Texture Descriptors,
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Springer DOI 1912
BibRef

Hamza, R.[Raja], Chtourou, M.[Mohamed],
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Savian, F.[Francesco], Martini, M.[Marta], Ermacora, P.[Paolo], Paulus, S.[Stefan], Mahlein, A.K.[Anne-Katrin],
Prediction of the Kiwifruit Decline Syndrome in Diseased Orchards by Remote Sensing,
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DOI Link 2007
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Tsoulias, N.[Nikos], Paraforos, D.S.[Dimitrios S.], Xanthopoulos, G.[George], Zude-Sasse, M.[Manuela],
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Abd-Elrahman, A.[Amr], Guan, Z.[Zhen], Dalid, C.[Cheryl], Whitaker, V.[Vance], Britt, K.[Katherine], Wilkinson, B.[Benjamin], Gonzalez, A.[Ali],
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Gill, H.S.[Harmandeep Singh], Khehra, B.S.[Baljit Singh],
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Ni, X.P.[Xue-Ping], Li, C.Y.[Chang-Ying], Jiang, H.Y.[Huan-Yu], Takeda, F.[Fumiomi],
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PandRS(171), 2021, pp. 297-309.
Elsevier DOI 2012
3D reconstruction, Mask R-CNN, 2D-3D projection, Blueberry traits BibRef

Biffi, L.J.[Leonardo Josoé], Mitishita, E.[Edson], Liesenberg, V.[Veraldo], dos Santos, A.A.[Anderson Aparecido], Gonçalves, D.N.[Diogo Nunes], Estrabis, N.V.[Nayara Vasconcelos], de Andrade Silva, J.[Jonathan], Osco, L.P.[Lucas Prado], Ramos, A.P.M.[Ana Paula Marques], Centeno, J.A.S.[Jorge Antonio Silva], Schimalski, M.B.[Marcos Benedito], Rufato, L.[Leo], Neto, S.L.R.[Sílvio Luís Rafaeli], Junior, J.M.[José Marcato], Gonçalves, W.N.[Wesley Nunes],
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Hennessy, P.J.[Patrick J.], Esau, T.J.[Travis J.], Farooque, A.A.[Aitazaz A.], Schumann, A.W.[Arnold W.], Zaman, Q.U.[Qamar U.], Corscadden, K.W.[Kenny W.],
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Henila, M.[Manickam], Chithra, P.[Palaniappan],
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DOI Link 2103
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Fan, P.[Pan], Lang, G.D.[Guo-Dong], Yan, B.[Bin], Lei, X.Y.[Xiao-Yan], Guo, P.J.[Peng-Ju], Liu, Z.J.[Zhi-Jie], Yang, F.Z.[Fu-Zeng],
A Method of Segmenting Apples Based on Gray-Centered RGB Color Space,
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DOI Link 2104
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Yan, B.[Bin], Fan, P.[Pan], Lei, X.Y.[Xiao-Yan], Liu, Z.J.[Zhi-Jie], Yang, F.[Fuzeng],
A Real-Time Apple Targets Detection Method for Picking Robot Based on Improved YOLOv5,
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Dewi, T.[Tresna], Mulya, Z.[Zarqa], Risma, P.[Pola], Oktarina, Y.[Yurni],
BLOB analysis of an automatic vision guided system for a fruit picking and placing robot,
IJCVR(11), No. 3, 2021, pp. 315-327.
DOI Link 2106
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Chu, P.Y.[Peng-Yu], Li, Z.J.[Zhao-Jian], Lammers, K.[Kyle], Lu, R.[Renfu], Liu, X.M.[Xiao-Ming],
Deep learning-based apple detection using a suppression mask R-CNN,
PRL(147), 2021, pp. 206-211.
Elsevier DOI 2106
Vision system, Fruit detection, Deep learning, Robotic harvesting, Image segmentation BibRef

Tripathi, M.K.[Mukesh Kumar], Maktedar, D.D.[Dhananjay D.],
Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm,
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Huang, Y.[Yirui], Wang, J.[Juan], Li, N.[Na], Yang, J.[Jing], Ren, Z.[Zhenhui],
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Elsevier DOI 2110
RGB imaging, Soluble solids content, L*a*b* color space, Stacked autoencoder, Back propagation neural network BibRef

Lawal, O.M.[Olarewaju Mubashiru], Zhao, H.M.[Hua-Min],
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Mao, W.J.[Wen-Ju], Liu, H.[Heng], Hao, W.[Wei], Yang, F.[Fuzeng], Liu, Z.J.[Zhi-Jie],
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Qi, X.K.[Xiao-Kang], Dong, J.[Jingshi], Lan, Y.[Yubin], Zhu, H.[Hang],
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Luo, L.[Lili], Chang, Q.R.[Qin-Rui], Gao, Y.F.[Yi-Fan], Jiang, D.Y.[Dan-Yao], Li, F.L.[Fen-Ling],
Combining Different Transformations of Ground Hyperspectral Data with Unmanned Aerial Vehicle (UAV) Images for Anthocyanin Estimation in Tree Peony Leaves,
RS(14), No. 9, 2022, pp. xx-yy.
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Antioxidants found in red, purple, and blue fruits and vegetables. BibRef

Singh, R.[Rishipal], Rani, R.[Rajneesh], Kamboj, A.[Aman],
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Hao, J.[Jinglei], Zhao, Y.Q.[Yong-Qiang], Peng, Q.[Qunnie],
A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits,
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Zheng, C.W.[Cai-Wang], Abd-Elrahman, A.[Amr], Whitaker, V.[Vance], Dalid, C.[Cheryl],
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Zhang, C.X.[Chen-Xi], Kang, F.[Feng], Wang, Y.X.[Ya-Xiong],
An Improved Apple Object Detection Method Based on Lightweight YOLOv4 in Complex Backgrounds,
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Zheng, Z.Z.[Zhou-Zhou], Hu, Y.[Yaohua], Qiao, Y.C.[Yi-Chen], Hu, X.[Xing], Huang, Y.X.[Yu-Xiang],
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Shiu, Y.S.[Yi-Shiang], Lee, R.Y.[Re-Yang], Chang, Y.C.[Yen-Ching],
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Liu, C.[Chao], Cao, Y.F.[Yi-Fei], Wu, E.[Ejiao], Yang, R.S.[Ri-Sheng], Xu, H.[Huanliang], Qiao, Y.S.[Yu-Shan],
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Gill, J.[Jasmeen], Singh, R.P.[Ravinder Pal],
Non-Invasive Grading and Sorting of Mango (Mangifera indica L.) Using Antlion Optimizer-Based Artificial Neural Networks,
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Aldandan, A.[Amnah], Al Ghanim, S.[Sajeda], Alhashim, H.[Hawraa], Ali, M.A.S.[Mona A.S.],
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Deep Learning for Apple Fruit Quality Inspection using X-Ray Imaging,
CVPPA23(552-560)
IEEE DOI 2401
BibRef

Louëdec, J.L.[Justin Le], Cielniak, G.[Grzegorz],
Key Point-based Orientation Estimation of Strawberries for Robotic Fruit Picking,
CVS23(148-158).
Springer DOI 2312
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Paulo, D.J.[Diogo J.], Neves, C.M.B.[Cláudia M. B.], Wessel, D.F.[Dulcineia Ferreira], Neves, J.C.[João C.],
Wildfruip: Estimating Fruit Physicochemical Parameters from Images Captured in the Wild,
CIARP23(I:314-326).
Springer DOI 2312
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Gerstenberger, M.[Michael], Maaß, S.[Steffen], Eisert, P.[Peter], Bosse, S.[Sebastian],
A Differentiable Gaussian Prototype Layer for Explainable Fruit Segmentation,
ICIP23(2665-2669)
IEEE DOI 2312
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Xia, G.[Geng], Dan, J.[Jiang], Jinyu, H.[Hao], Jiming, H.[Hu], Xiaoyong, S.[Sun],
Research on Fruit Counting of Xanthoceras Sorbifolium Bunge Based on Deep Learning,
ICIVC22(790-798)
IEEE DOI 2301
Deep learning, Industries, Training, Image color analysis, Shape, Production, Predictive models, Xanthoceras sorbifolium Bunge, mixed data set BibRef

Rossi, L.[Leonardo], Valenti, M.[Marco], Legler, S.E.[Sara Elisabetta], Prati, A.[Andrea],
LDD: A Grape Diseases Dataset Detection and Instance Segmentation,
CIAP22(II:383-393).
Springer DOI 2205
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Aguiar, H.T.[Henrique Tavares], Vasconcelos, R.C.S.[Raimundo C. S.],
Identification of External Defects on Fruits Using Deep Learning,
IbPRIA22(565-575).
Springer DOI 2205
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Ranjard, L.[Louis], Bristow, J.[James], Hossain, Z.[Zulfikar], Orsi, A.[Alvaro], Kirkwood, H.J.[Henry J.], Gilman, A.[Andrew],
Likelihood-free Bayesian inference framework for sizing kiwifruit from orchard imaging surveys,
IVCNZ21(1-6)
IEEE DOI 2201
Monte Carlo methods, Shape, Imaging, Estimation, Stochastic processes, Crops, high-throughput phenotyping, sequential Monte Carlo BibRef

Halstead, M.[Michael], Denman, S.[Simon], Fookes, C.[Clinton], McCool, C.[Chris],
Fruit Detection in the Wild: the Impact of Varying Conditions and Cultivar,
DICTA20(1-8)
IEEE DOI 2201
Training, Image segmentation, Computational modeling, Digital images, Training data, Pressing, Machine learning BibRef

Couasnet, G.[Geoffroy], El Abidine, M.Z.[Mouad Zine], Laurens, F.[François], Dutagaci, H.[Helin], Rousseau, D.[David],
Machine learning meets distinctness in variety testing,
CVPPA21(1303-1311)
IEEE DOI 2112
Image color analysis, Machine vision, Supervised learning, Pipelines, Machine learning, Information retrieval BibRef

Fei, Z.H.[Zheng-Hao], Olenskyj, A.[Alex], Bailey, B.N.[Brian N.], Earles, M.[Mason],
Enlisting 3D Crop Models and GANs for More Data Efficient and Generalizable Fruit Detection,
CVPPA21(1269-1277)
IEEE DOI 2112
Training, Solid modeling, Visualization, Pipelines, Neural networks, Crops BibRef

Klotz, J.[Jeremy], Rengarajan, V.[Vijay], Sankaranarayanan, A.C.[Aswin C.],
Fine-Grain Prediction of Strawberry Freshness using Subsurface Scattering,
LFFAI21(2328-2336)
IEEE DOI 2112
Scattering, Lighting, Time measurement, Spatial resolution BibRef

Kirk, R.[Raymond], Mangan, M.[Michael], Cielniak, G.[Grzegorz],
Robust Counting of Soft Fruit Through Occlusions with Re-identification,
CVS21(211-222).
Springer DOI 2109
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Kirk, R.[Raymond], Mangan, M.[Michael], Cielniak, G.[Grzegorz],
Non-destructive Soft Fruit Mass and Volume Estimation for Phenotyping in Horticulture,
CVS21(223-233).
Springer DOI 2109
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Zhang, J.[Jihan], Huang, D.M.[Dong-Mei], Hu, T.T.[Ting-Ting], Fuchikami, R.J.[Ryu-Ji], Ikenaga, T.[Takeshi],
Critically Compressed Quantized Convolution Neural Network based High Frame Rate and Ultra-Low Delay Fruit External Defects Detection,
MVA21(1-4)
DOI Link 2109
Deep learning, Quantization (signal), Convolution, Transforms, Production facilities, Delays BibRef

Akai, R.[Ryota], Utsumi, Y.[Yuzuko], Miwa, Y.[Yuka], Iwamura, M.[Masakazu], Kise, K.[Koichi],
Distortion-Adaptive Grape Bunch Counting for Omnidirectional Images,
ICPR21(599-606)
IEEE DOI 2105
Training, Image analysis, Pipelines, Distortion, Density functional theory, Predistortion, Pattern recognition BibRef

van Sint Annaland, Y.[Yerren], Szymanski, L.[Lech], Mills, S.[Steven],
Predicting Cherry Quality Using Siamese Networks,
IVCNZ20(1-6)
IEEE DOI 2012
Industries, Training, Transforms, Predictive models, Labeling, Standards BibRef

Akiva, P., Dana, K., Oudemans, P., Mars, M.,
Finding Berries: Segmentation and Counting of Cranberries using Point Supervision and Shape Priors,
AgriVision20(219-228)
IEEE DOI 2008
Shape, Agriculture, Image segmentation, Image color analysis, Proposals, Cameras, Thermal sensors BibRef

Rojas-Aranda, J.L.[Jose Luis], Nunez-Varela, J.I.[Jose Ignacio], Cuevas-Tello, J.C., Rangel-Ramirez, G.[Gabriela],
Fruit Classification for Retail Stores Using Deep Learning,
MCPR20(3-13).
Springer DOI 2007
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Marcal, A.R.S.[André R. S.], Santos, E.M.D.S.[Elisabete M. D. S.], Tavares, F.[Fernando],
Image Based Estimation of Fruit Phytopathogenic Lesions Area,
IbPRIA19(II:285-295).
Springer DOI 1910
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Guo, L.T.[Lin-Tao], Quant, H.[Hunter], Lamb, N.[Nikolas], Lowit, B.[Benjamin], Banerjee, N.K.[Natasha Kholgade], Banerjee, S.[Sean],
Multi-camera Microenvironment to Capture Multi-view Time-Lapse Videos for 3D Analysis of Aging Objects,
MMMod18(II:381-385).
Springer DOI 1802
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Guo, L.[Lintao], Quant, H.[Hunter], Lamb, N.[Nikolas], Lowit, B.[Benjamin], Banerjee, S.[Sean], Banerjee, N.K.[Natasha Kholgade],
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MMMod18(I:466-478).
Springer DOI 1802
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Lv, J.D.[Ji-Dong], Shen, G.R.[Gen-Rong], Ma, Z.H.[Zheng-Hua],
Acquisition of fruit region in green apple image based on the combination of segmented regions,
ICIVC17(332-335)
IEEE DOI 1708
Clustering algorithms, Image color analysis, Image resolution, apple, harvesting robot, image processing, image, segmentation BibRef

Cornejo, J.Y.R.[Jadisha Yarif Ramírez], Pedrini, H.[Helio],
Automatic Fruit and Vegetable Recognition Based on CENTRIST and Color Representation,
CIARP16(76-83).
Springer DOI 1703
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Puttemans, S.[Steven], Vanbrabant, Y., Tits, L., Goedemé, T.[Toon],
Automated visual fruit detection for harvest estimation and robotic harvesting,
IPTA16(1-6)
IEEE DOI 1703
control engineering computing BibRef

Kadmiry, B., Wong, C.K.[Chee Kit],
Perception scheme for fruits detection in trees for autonomous agricultural robot applications,
ICVNZ15(1-6)
IEEE DOI 1701
agriculture BibRef

Zhang, M.[Meng], Lee, D.J.[Dah-Jye],
Global Evolution-Constructed Feature for Date Maturity Evaluation,
ISVC16(II: 281-290).
Springer DOI 1701
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Efficient Training of Evolution-Constructed Features,
ISVC15(II: 646-654).
Springer DOI 1601
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Janssens, E.[Eline], de Beenhouwer, J.[Jan], van Dael, M.[Mattias], Verboven, P.[Pieter], Nicolai, B.[Bart], Sijbers, J.[Jan],
Neural netwok based X-ray tomography for fast inspection of apples on a conveyor belt system,
ICIP15(917-921)
IEEE DOI 1512
Inline tomography; artificial neural networks; filtered backprojection BibRef

Kuang, H.L.[Hu-Lin], Chan, L.L.H., Yan, H.[Hong],
Multi-class fruit detection based on multiple color channels,
ICWAPR15(1-7)
IEEE DOI 1511
agricultural products BibRef

López-García, F.[Fernando], Andreu-García, G.[Gabriela],
Detection of Visual Defects in Citrus Fruits: Multivariate Image Analysis vs Graph Image Segmentation,
CAIP13(237-244).
Springer DOI 1308
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Roy, A., Banerjee, S., Roy, D., Mukhopadhyay, A.,
Statistical Video Tracking of Pomegranate Fruits,
NCVPRIPG11(227-230).
IEEE DOI 1205
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Iqbal, S.M., Gopal, A., Sarma, A.S.V.,
Volume estimation of apple fruits using image processing,
ICIIP11(1-6).
IEEE DOI 1112
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Gatica, C.G.[C. Gabriel], Best, S.S.[S. Stanley], Ceroni, J.[José], Lefranc, G.[Gaston],
A New Method for Olive Fruits Recognition,
CIARP11(646-653).
Springer DOI 1111
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Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Plants, Flowers, Flower Shape, Flower Color .


Last update:Mar 16, 2024 at 20:36:19