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.

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

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

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.B.[Wen-Bo], 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

Wang, J.J.[Jin-Jie], Ding, J.[Jianli], Ran, S.[Si], Qin, S.F.[Shao-Feng], Liu, B.[Bohua], Li, X.[Xiang],
Automatic Pear Extraction from High-Resolution Images by a Visual Attention Mechanism Network,
RS(15), No. 13, 2023, pp. 3283.
DOI Link 2307
BibRef

Liu, C.[Chao], Cao, Y.F.[Yi-Fei], Wu, E.[Ejiao], Yang, R.S.[Ri-Sheng], Xu, H.[Huanliang], Qiao, Y.S.[Yu-Shan],
A Discriminative Model for Early Detection of Anthracnose in Strawberry Plants Based on Hyperspectral Imaging Technology,
RS(15), No. 18, 2023, pp. 4640.
DOI Link 2310
BibRef

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,
IJIG(23), No. 5 2023, pp. 2350040.
DOI Link 2310
BibRef

Aldandan, A.[Amnah], Al Ghanim, S.[Sajeda], Alhashim, H.[Hawraa], Ali, M.A.S.[Mona A.S.],
Image-based deep learning automated grading of date fruit (Alhasa case study Saudi Arabia),
IJCVR(14), No. 2, 2024, pp. 213-231.
DOI Link 2403
BibRef


Wen, J.[Junhan], Verschoor, C.R.[Camiel R.], Feng, C.M.[Cheng-Ming], Epure, I.M.[Irina-Mona], Abeel, T.[Thomas], de Weerdt, M.[Mathijs],
The Growing Strawberries Dataset: Tracking Multiple Objects with Biological Development over an Extended Period,
WACV24(7089-7099)
IEEE DOI Code:
DOI Link 2404
Measurement, Visualization, Target tracking, Greenhouses, Switches, Biology, Trajectory, Applications, Agriculture, Algorithms, Datasets and evaluations BibRef

Johanson, R.[Robert], Wilms, C.[Christian], Johannsen, O.[Ole], Frintrop, S.[Simone],
S3AD: Semi-supervised Small Apple Detection in Orchard Environments,
WACV24(7061-7070)
IEEE DOI 2404
Training, Runtime, Limiting, Image resolution, Crops, Training data, Object detection, Applications, Agriculture, Algorithms, Image recognition and understanding BibRef

Lu, Y.Z.[Yi-Zhuo],
Research on Small Sample Apple Defect Classification Method Based on Siamese Network,
CVIDL23(489-493)
IEEE DOI 2403
Training, Measurement, Learning systems, Deep learning, Industries, Euclidean distance, Feature extraction, Feature vector BibRef

Mane, S.[Shubham], Bartakke, P.[Prashant], Bastewad, T.[Tulshidas],
DetSSeg: A Selective On-Field Pomegranate Segmentation Approach,
ICCVMI23(1-6)
IEEE DOI 2403
YOLO, Training, Image segmentation, Lighting, Environmental factors, Timing, Quality assessment, Deep learning, Image segmentation, YOLOv5s BibRef

Tempelaere, A.[Astrid], van Doorselaer, L.[Leen], He, J.Q.[Jia-Qi], Verboven, P.[Pieter], Tuytelaars, T.[Tinne], Nicolai, B.[Bart],
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
BibRef

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
BibRef

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
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

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 23, 2024 at 14:31:23