19.7.3.7.7 Fruit Detection, Fruit Inspection, Apples, Oranges

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

Tao, Y.,
Spherical Transform of Fruit Images for Online Defect Extraction of Mass Objects,
OptEng(35), No. 2, February 1996, pp. 344-350. BibRef 9602

Grasso, G.M., Recce, M.,
Scene Analysis for An Orange Harvesting Robot,
AIApp(11), No. 3, 1997, pp. 9-15. 9802
BibRef

Recce, M.[Michael], Plebe, A.[Alessio], Tropiano, G.[Giuseppe], Taylor, J.[John],
Video Grading of Oranges in Real-Time,
AIR(12), No. 1-3, February 1998, pp. 117-136.
WWW Link. 1208
BibRef

Plebe, A.[Alessio], Grasso, G.M.[Giorgio M.],
Localization of spherical fruits for robotic harvesting,
MVA(13), No. 2 2001, pp. 70-79.
Springer DOI 0201
BibRef

Harrell, R.C., Slaughter, D.C., Adsit, P.D.,
A fruit-tracking system for robotic harvesting,
MVA(2), No. 2, 1989, pp. 69-80. BibRef 8900

Jiménez, A.R., Jain, A.K., Ceres, R., Pons, J.L.,
Automatic fruit recognition: A survey and new results using Range/Attenuation images,
PR(32), No. 10, October 1999, pp. 1719-1736.
Elsevier DOI Survey, Inspection. BibRef 9910

Jiménez, A.R., Ceres, R., Pons, J.L.,
A vision system based on a laser range-finder applied to robotic fruit harvesting,
MVA(11), No. 6, 2000, pp. 321-329.
Springer DOI 0005
BibRef

Zou, X.B.[Xiao-Bo], Zhao, J.W.[Jie-Wen], Li, Y.X.[Yan-Xiao],
Apple color grading based on organization feature parameters,
PRL(28), No. 15, 1 November 2007, pp. 2046-2053.
Elsevier DOI 0711
Apple; Quality evaluation; Genetic algorithm; Organization feature parameter BibRef

Anami, B.S.[Basavaraj S.], Burkpalli, V.C.[Vishwanath C.],
Recognition and classification of images of fruits' juices based on 3-Sigma approach,
IJCVR(1), No. 2, 2010, pp. 206-217.
DOI Link 1011
BibRef

Blasco, J.[José],
Magnetic resonance imaging detects seeds in mandarins,
SPIE(Newsroom), January 16, 2012.
DOI Link 1201
Seeds in mandarins are difficult to detect with x-ray radiography and computed tomography, but are easily distinguished using magnetic resonance imaging. BibRef

Kapach, K.[Keren], Barnea, E.[Ehud], Mairon, R.[Rotem], Edan, Y.[Yael], Ben-Shahar, O.[Ohad],
Computer vision for fruit harvesting robots: State of the art and challenges ahead,
IJCVR(3), No. 1-2, 2012, pp. 4-34.
DOI Link 1204
BibRef

Liu, S.G.[Shi-Guang], Fan, D.F.[Dong-Fang],
Computer Modeling and Simulation of Fruit Sunscald,
IJIG(15), No. 03, 2015, pp. 1550013.
DOI Link 1506
BibRef

Bohl, E.[Evans], Terraz, O.[Olivier], Ghazanfarpour, D.[Djamchid],
Modeling fruits and their internal structure using parametric 3Gmap L-systems,
VC(31), No. 6-8, June 2015, pp. 819-829.
WWW Link. 1506
BibRef

Dubey, S.R.[Shiv Ram], Jalal, A.S.[Anand Singh],
Apple disease classification using color, texture and shape features from images,
SIViP(10), No. 5, May 2016, pp. 819-826.
WWW Link. 1608
BibRef

Chaw, J.K.[Jun-Kit], Mokji, M.[Musa],
Analysis of produce recognition system with taxonomist's knowledge using computer vision and different classifiers,
IET-IPR(11), No. 3, March 2017, pp. 173-182.
DOI Link 1703
BibRef

Aiadi, O.[Oussama], Kherfi, M.L.[Mohammed Lamine],
A new method for automatic date fruit classification,
IJCVR(7), No. 6, 2017, pp. 692-711.
DOI Link 1711
BibRef

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],
Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902
BibRef

Pourdarbani, R.[Razieh], Sabzi, S.[Sajad], Hernández-Hernández, M.[Mario], Hernández-Hernández, J.L.[José Luis], García-Mateos, G.[Ginés], Kalantari, D.[Davood], Molina-Martínez, J.M.[José Miguel],
Comparison of Different Classifiers and the Majority Voting Rule for the Detection of Plum Fruits in Garden Conditions,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Badeka, E.[Eftichia], Kalabokas, T.[Theofanis], Tziridis, K.[Konstantinos], Nicolaou, A.[Alexander], Vrochidou, E.[Eleni], Mavridou, E.[Efthimia], Papakostas, G.A.[George A.], Pachidis, T.[Theodore],
Grapes Visual Segmentation for Harvesting Robots Using Local Texture Descriptors,
CVS19(98-109).
Springer DOI 1912
BibRef

Hamza, R.[Raja], Chtourou, M.[Mohamed],
Design of fuzzy inference system for apple ripeness estimation using gradient method,
IET-IPR(14), No. 3, 28 February 2020, pp. 561-569.
DOI Link 2002
BibRef

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,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007
BibRef

Tsoulias, N.[Nikos], Paraforos, D.S.[Dimitrios S.], Xanthopoulos, G.[George], Zude-Sasse, M.[Manuela],
Apple Shape Detection Based on Geometric and Radiometric Features Using a LiDAR Laser Scanner,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
BibRef

Abd-Elrahman, A.[Amr], Guan, Z.[Zhen], Dalid, C.[Cheryl], Whitaker, V.[Vance], Britt, K.[Katherine], Wilkinson, B.[Benjamin], Gonzalez, A.[Ali],
Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Gill, H.S.[Harmandeep Singh], Khehra, B.S.[Baljit Singh],
Efficient image classification technique for weather degraded fruit images,
IET-IPR(14), No. 14, December 2020, pp. 3463-3470.
DOI Link 2012
BibRef

Ni, X.P.[Xue-Ping], Li, C.Y.[Chang-Ying], Jiang, H.Y.[Huan-Yu], Takeda, F.[Fumiomi],
Three-dimensional photogrammetry with deep learning instance segmentation to extract berry fruit harvestability traits,
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],
ATSS Deep Learning-Based Approach to Detect Apple Fruits,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101
BibRef

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.],
Hair Fescue and Sheep Sorrel Identification Using Deep Learning in Wild Blueberry Production,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Henila, M.[Manickam], Chithra, P.[Palaniappan],
Segmentation using fuzzy cluster-based thresholding method for apple fruit sorting,
IET-IPR(14), No. 16, 19 December 2020, pp. 4178-4187.
DOI Link 2103
BibRef

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,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

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,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

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
BibRef

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,
IET-IPR(15), No. 9, 2021, pp. 1940-1956.
DOI Link 2106
BibRef

Li, G.J.[Guo-Jin], Huang, X.J.[Xiao-Jie], Ai, J.[Jiaoyan], Yi, Z.[Zeren], Xie, W.[Wei],
Lemon-YOLO: An efficient object detection method for lemons in the natural environment,
IET-IPR(15), No. 9, 2021, pp. 1998-2009.
DOI Link 2106
BibRef

Huang, Y.[Yirui], Wang, J.[Juan], Li, N.[Na], Yang, J.[Jing], Ren, Z.[Zhenhui],
Predicting soluble solids content in 'Fuji' apples of different ripening stages based on multiple information fusion,
PRL(151), 2021, pp. 76-84.
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],
YOLOFig detection model development using deep learning,
IET-IPR(15), No. 13, 2021, pp. 3071-3079.
DOI Link 2110
Figs. BibRef

Grilli, E.[Eleonora], Battisti, R.[Roberto], Remondino, F.[Fabio],
An Advanced Photogrammetric Solution to Measure Apples,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Li, T.[Tao], Feng, Q.C.[Qing-Chun], Qiu, Q.[Quan], Xie, F.[Feng], Zhao, C.J.[Chun-Jiang],
Occluded Apple Fruit Detection and Localization with a Frustum-Based Point-Cloud-Processing Approach for Robotic Harvesting,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Mao, W.J.[Wen-Ju], Liu, H.[Heng], Hao, W.[Wei], Yang, F.[Fuzeng], Liu, Z.J.[Zhi-Jie],
Development of a Combined Orchard Harvesting Robot Navigation System,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Qi, X.K.[Xiao-Kang], Dong, J.[Jingshi], Lan, Y.[Yubin], Zhu, H.[Hang],
Method for Identifying Litchi Picking Position Based on YOLOv5 and PSPNet,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

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.
DOI Link 2205
Antioxidants found in red, purple, and blue fruits and vegetables. BibRef

Singh, R.[Rishipal], Rani, R.[Rajneesh], Kamboj, A.[Aman],
An Optimized Approach for Intra-Class Fruit Classification Using Deep Convolutional Neural Network,
IJIG(22), No. 3 2022, pp. 2140014.
DOI Link 2206
BibRef

Hao, J.[Jinglei], Zhao, Y.Q.[Yong-Qiang], Peng, Q.[Qunnie],
A Specular Highlight Removal Algorithm for Quality Inspection of Fresh Fruits,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zheng, C.W.[Cai-Wang], Abd-Elrahman, A.[Amr], Whitaker, V.[Vance], Dalid, C.[Cheryl],
Prediction of Strawberry Dry Biomass from UAV Multispectral Imagery Using Multiple Machine Learning Methods,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Tong, S.Y.[Si-Yuan], Yue, Y.[Yang], Li, W.B.[Wen-Bin], Wang, Y.X.[Ya-Xiong], Kang, F.[Feng], Feng, C.[Chao],
Branch Identification and Junction Points Location for Apple Trees Based on Deep Learning,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

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,
RS(14), No. 17, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Zheng, Z.Z.[Zhou-Zhou], Hu, Y.[Yaohua], Qiao, Y.C.[Yi-Chen], Hu, X.[Xing], Huang, Y.X.[Yu-Xiang],
Real-Time Detection of Winter Jujubes Based on Improved YOLOX-Nano Network,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Lu, S.L.[Sheng-Lian], Liu, X.Y.[Xiao-Yu], He, Z.X.[Zi-Xuan], Zhang, X.[Xin], Liu, W.[Wenbo], Karkee, M.[Manoj],
Swin-Transformer-YOLOv5 for Real-Time Wine Grape Bunch Detection,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Shiu, Y.S.[Yi-Shiang], Lee, R.Y.[Re-Yang], Chang, Y.C.[Yen-Ching],
Pineapples' Detection and Segmentation Based on Faster and Mask R-CNN in UAV Imagery,
RS(15), No. 3, 2023, pp. xx-yy.
DOI Link 2302
BibRef

Sekharamantry, P.K.[Praveen Kumar], Melgani, F.[Farid], Malacarne, J.[Jonni],
Deep Learning-Based Apple Detection with Attention Module and Improved Loss Function in YOLO,
RS(15), No. 6, 2023, pp. 1516.
DOI Link 2304
BibRef


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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

Guo, L.[Lintao], Quant, H.[Hunter], Lamb, N.[Nikolas], Lowit, B.[Benjamin], Banerjee, S.[Sean], Banerjee, N.K.[Natasha Kholgade],
Spatiotemporal 3D Models of Aging Fruit from Multi-view Time-Lapse Videos,
MMMod18(I:466-478).
Springer DOI 1802
BibRef

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
BibRef

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
BibRef
And:
Efficient Training of Evolution-Constructed Features,
ISVC15(II: 646-654).
Springer DOI 1601
BibRef

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

Elfiky, N.M.[Noha M.], Akbar, S.A.[Shayan A.], Sun, J.X.[Jian-Xin], Park, J.[Johnny], Kak, A.[Avinash],
Automation of dormant pruning in specialty crop production: An adaptive framework for automatic reconstruction and modeling of apple trees,
PBVS15(65-73)
IEEE DOI 1510
Adaptation models 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
BibRef

Roy, A., Banerjee, S., Roy, D., Mukhopadhyay, A.,
Statistical Video Tracking of Pomegranate Fruits,
NCVPRIPG11(227-230).
IEEE DOI 1205
BibRef

Iqbal, S.M., Gopal, A., Sarma, A.S.V.,
Volume estimation of apple fruits using image processing,
ICIIP11(1-6).
IEEE DOI 1112
BibRef

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
BibRef

Busch, A.[Andrew], Palk, P.[Phillip],
An Image Processing Approach to Distance Estimation for Automated Strawberry Harvesting,
ICIAR11(II: 389-396).
Springer DOI 1106
BibRef

Mao, W.H.[Wen-Hua], Ji, B.P.[Bao-Ping], Zhan, J.C.[Ji-Cheng], Zhang, X.C.[Xiao-Chao], Hu, X.A.[Xiao-An],
Apple Location Method for the Apple Harvesting Robot,
CISP09(1-5).
IEEE DOI 0910
BibRef

Messner, W.[William], Kliethermes, B.,
Augmented Fruit Harvesting,
CMU-RI-TR-09-20, June, 2009.
WWW Link. 1102
BibRef

Wijethunga, P., Samarasinghe, S., Kulasiri, D., Woodhead, I.,
Towards a generalized colour image segmentation for kiwifruit detection,
IVCNZ09(62-66).
IEEE DOI 0911
BibRef
Earlier:
Digital image analysis based automated kiwifruit counting technique,
IVCNZ08(1-6).
IEEE DOI 0811
BibRef

Gómez-Sanchis, J., Camps-Valls, G., Moltó, E., Gómez-Chova, L., Aleixos, N., Blasco, J.,
Segmentation of Hyperspectral Images for the Detection of Rotten Mandarins,
ICIAR08(xx-yy).
Springer DOI 0806
BibRef

Blasco, J.[José], Cubero, S.[Sergio], Arias, R.[Raúl], Gómez, J.[Juan], Juste, F.[Florentino], Moltó, E.[Enrique],
Development of a Computer Vision System for the Automatic Quality Grading of Mandarin Segments,
IbPRIA07(II: 460-466).
Springer DOI 0706
BibRef

Kaewapichai, W.[Watcharin], KaewTrakulPong, P.[Pakorn], Prateepasen, A.[Asa], Khongkraphan, K.[Kittiya],
Fitting a Pineapple Model for Automatic Maturity Grading,
ICIP07(I: 257-260).
IEEE DOI 0709
BibRef

Chalidabhongse, T.H., Yimyam, P., Sirisomboon, P.,
2D/3D Vision-Based Mango's Feature Extraction and Sorting,
ICARCV06(1-6).
IEEE DOI 0612
Sort mangos using detected features. BibRef

Ye, L.[Lei], Cao, L.Z.[Ling-Zhi], Ogunbona, P.[Philip], Li, W.Q.[Wan-Qing],
Description of Evolutional Changes in Image Time Sequences Using MPEG-7 Visual Descriptors,
VLBV05(189-197).
Springer DOI 0509
Fruit rotting is the example. BibRef

Martínez-Usó, A.[Adolfo], Pla, F.[Filiberto], García-Sevilla, P.[Pedro],
Multispectral Image Segmentation by Energy Minimization for Fruit Quality Estimation,
IbPRIA05(II:689).
Springer DOI 0509
BibRef

Laemmer, E., Deruyver, A., Sowinska, M.,
Watershed and adaptive pyramid for determining the apple's maturity state,
ICIP02(I: 789-792).
IEEE DOI 0210
BibRef

Aleixos, N., Blasco, J., Moltó, E., Navarrón, F.,
Assessment of Citrus Fruit Quality Using a Real-time Machine Vision System,
ICPR00(Vol I: 482-485).
IEEE DOI 0009
BibRef

Jiménez, A.R., Ceres, R., Pons, J.L.,
A Machine Vision System Using a Laser Radar Applied to Robotic Fruit Harvesting,
CVBVS99(110).
IEEE DOI BibRef 9900

Fernandez-Maloigne, C., and Laugier, D.,
Automatic Apples Detection for an Agricultural Robot,
IAS93(xx-yy). Recognize Apples. BibRef 9300

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Plants, Flowers, Flower Shape, Flower Color .


Last update:May 22, 2023 at 22:32:27