20.7.3.7 Agriculture, Inspection -- Food Products, Plants, Farms

Dipix Technologies,
2001
WWW Link. Vendor, Inspection. Industrial inspection systems for food industry.

Ellips B.V.,
1989.
WWW Link. Vendor, Inspection. Industrial inspection systems for food industry.

JLI Vision,
1985. Founded as Jørgen Læssøe Ingeniørfirma Aps
WWW Link. Vendor, Inspection. Industrial inspection systems for food industry.

Tillett and Hague Technology Ltd,
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WWW Link. Vendor, Inspection. Automation for agriculture, e.g. visual guided spraying.

Buhler Sortex,
2010
WWW Link. Vendor, Inspection. Food inspection and sorting using images.

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Pandit, R.B.[Ram Bhuwan], Tang, J.[Juming], Liu, F.[Frank], Mikhaylenko, G.[Galina],
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PR(40), No. 12, December 2007, pp. 3667-3676.
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Sun, D.W.[Da-Wen], (Ed.)
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Bossu, J.[Jérémie], Gee, C.[Christelle], Truchetet, F.[Frederic],
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IEEE DOI 1102
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Galleguillos, C.[Carolina], McFee, B.[Brian], Lanckriet, G.R.G.[Gert R.G.],
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Galleguillos, C.[Carolina], McFee, B.[Brian], Belongie, S.J.[Serge J.], Lanckriet, G.R.G.[Gert R.G.],
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CVPR11(2665-2672).
IEEE DOI 1106
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Multi-class object localization by combining local contextual interactions,
CVPR10(113-120).
IEEE DOI 1006
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Rabinovich, A.[Andrew], Vedaldi, A.[Andrea], Galleguillos, C.[Carolina], Wiewiora, E.[Eric], Belongie, S.J.[Serge J.],
Objects in Context,
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IEEE DOI 0710
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Merler, M.[Michele], Galleguillos, C.[Carolina], Belongie, S.J.[Serge J.],
Recognizing Groceries in situ Using in vitro Training Data,
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Galleguillos, C.[Carolina], Faymonville, P.[Peter], Belongie, S.J.[Serge J.],
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Galleguillos, C.[Carolina], Rabinovich, A.[Andrew], Belongie, S.J.[Serge J.],
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Hyperspectral imaging based on diffused laser light for prediction of astaxanthin coating concentration,
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Baravalle, R.[Rodrigo], Delrieux, C.[Claudio], Gomez, J.[Juan],
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Story, D.[David], Kacira, M.[Murat],
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Chi, J.N.[Jian-Ning], Eramian, M.G.,
Enhancement of Textural Differences Based on Morphological Component Analysis,
IP(24), No. 9, September 2015, pp. 2671-2684.
IEEE DOI 1506
image enhancement BibRef

Yi, X.[Xin], Eramian, M.G.[Mark G.], Wang, R.J.[Ruo-Jing], Neufeld, E.[Eric],
Identification of Morphologically Similar Seeds Using Multi-kernel Learning,
CRV14(143-150)
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Accuracy BibRef

Impoco, G., Tuminello, L.,
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CVIU(140), No. 1, 2015, pp. 144-152.
Elsevier DOI 1509
Image analysis BibRef

Impoco, G.[Gaetano], Licitra, G.[Giuseppe],
An Interactive Level Set Approach to Semi-automatic Detection of Features in Food Micrographs,
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Behmann, J.[Jan], Mahlein, A.K.[Anne-Katrin], Paulus, S.[Stefan], Dupuis, J.[Jan], Kuhlmann, H.[Heiner], Oerke, E.C.[Erich-Christian], Plümer, L.[Lutz],
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MVA(27), No. 5, July 2016, pp. 611-624.
Springer DOI 1608
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Benoit, L.[Landry], Benoit, R.[Romain], Belin, É.[Étienne], Vadaine, R.[Rodolphe], Demilly, D.[Didier], Chapeau-Blondeau, F.[François], Rousseau, D.[David],
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Springer DOI 1608
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Augustin, M.[Marco], Haxhimusa, Y.[Yll], Busch, W.[Wolfgang], Kropatsch, W.G.[Walter G.],
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Santos, T.T.[Thiago T.], Rodrigues, G.C.[Gustavo C.],
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Springer DOI 1608
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Kosmala, M.[Margaret], Crall, A.[Alycia], Cheng, R.[Rebecca], Hufkens, K.[Koen], Henderson, S.[Sandra], Richardson, A.D.[Andrew D.],
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Zheng, P.[Peng], Zhao, Z.Q.[Zhong-Qiu], Gao, J.[Jun], Wu, X.D.[Xin-Dong],
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Elsevier DOI 1612
Sparse representation BibRef

Zhao, Z.Q.[Zhong-Qiu], Hong, Y.[Yan], Zheng, P.[Peng], Wu, X.D.[Xin-Dong],
Plant identification using triangular representation based on salient points and margin points,
ICIP15(1145-1149)
IEEE DOI 1512
Feature extraction BibRef

Bell, J.[Jonathan], Dee, H.M.[Hannah M.],
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Li, D.W.[Da-Wei], Xu, L.H.[Li-Hong], Tang, X.S.[Xue-Song], Sun, S.Y.[Shao-Yuan], Cai, X.[Xin], Zhang, P.[Peng],
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Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
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Kusumoto, R.[Riko], Han, X.H.[Xian-Hua], Chen, Y.W.[Yen-Wei],
Hybrid Aggregation of Sparse Coded Descriptors for Food Recognition,
ICPR14(1490-1495)
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Kim, D.W.[Dong-Wook], Yun, H.S.[Hee Sup], Jeong, S.J.[Sang-Jin], Kwon, Y.S.[Young-Seok], Kim, S.G.[Suk-Gu], Lee, W.S.[Won Suk], Kim, H.J.[Hak-Jin],
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Xu, J.[Jing], Ma, L.[Long], Wu, J.[Jie], Xu, X.M.[Xiao-Meng], Sun, Y.[Ye], Liu, Q.A.[Qi-Ang], Pan, L.Q.[Lei-Qing], Tu, K.[Kang],
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Feature extraction, Geometry, Machine learning, Food packaging, maximally stable extremal regions BibRef

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Tonioni, A.[Alessio], di Stefano, L.[Luigi],
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Elsevier DOI 1905
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Wang, L.[Long], Sun, J.[Jun], Wu, X.H.[Xiao-Hong], Shen, J.F.[Ji-Feng], Lu, B.[Bing], Tan, W.J.[Wen-Jun],
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Ma, X.[Xu], Liu, Y.[Yong],
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Elsevier DOI 2012
Crop type mapping, Deep learning, Google Earth Engine, Google Street View, Ground referencing BibRef

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Rabab, S.[Saba], Breen, E.[Edmond], Gebremedhin, A.[Alem], Shi, F.[Fan], Badenhorst, P.[Pieter], Chen, Y.P.P.[Yi-Ping Phoebe], Daetwyler, H.D.[Hans D.],
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Kasthuri, N., Devi, T.M.[T. Meera], Shangar, A.T.[Arivazhagan T.], Yashwin, R., Shabhareesh, J.S.,
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Kuang, Z.K.[Zhan-Kun], Yu, X.Y.[Xiang-Yang], Guo, Y.C.[Yu-Chen], Cai, Y.[Yefan], Hong, W.B.[Wei-Bin],
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Hartley, Z.K.J.[Zane K. J.], Lind, R.J.[Rob J.], Smith, N.[Nicholas], Collison, B.[Bob], French, A.P.[Andrew P.],
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CVPPA23(678-684)
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Körschens, M.[Matthias], Bucher, S.F.[Solveig Franziska], Römermann, C.[Christine], Denzler, J.[Joachim],
Unified Automatic Plant Cover and Phenology Prediction,
CVPPA23(685-693)
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Villalpando, A.P.[Antonio Pico], Kubisch, M.[Matthias], Colliaux, D.[David], Hanappe, P.[Peter], Hafner, V.V.[Verena V.],
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CVPPA23(694-701)
IEEE DOI 2401
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Rodrigues, J.P.[João Pedro], Pacheco, O.R.[Osvaldo Rocha], Correia, P.L.[Paulo Lobato],
Seabream Freshness Classification Using Vision Transformers,
CIARP23(I:510-525).
Springer DOI 2312
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Costa, D.[Dinis], Silva, C.[Catarina], Costa, J.[Joana], Ribeiro, B.[Bernardete],
Improving Pest Detection via Transfer Learning,
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Anagnostopoulou, D.[Dafni], Retsinas, G.[George], Efthymiou, N.[Niki], Filntisis, P.[Panagiotis], Maragos, P.[Petros],
A Realistic Synthetic Mushroom Scenes Dataset,
AgriVision23(6282-6289)
IEEE DOI 2309
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Retsinas, G.[George], Efthymiou, N.[Niki], Maragos, P.[Petros],
Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding,
AgriVision23(6264-6271)
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Miranda, M.[Miro], Drees, L.[Lukas], Roscher, R.[Ribana],
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ICPR22(5118-5124)
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Measurement, Image synthesis, Ecosystems, Predictive models, Generative adversarial networks, Task analysis BibRef

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ICIP22(4063-4067)
IEEE DOI 2211
Training, Interpolation, Convolution, Plants (biology), Neural networks, Focusing, Plant identification, attention, convolutional neural network BibRef

Sáez-Pérez, J.[Javier], Gallego, A.J.[Antonio Javier], Valero-Mas, J.J.[Jose J.], Zaragoza, J.C.[Jorge Calvo],
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Danish Fungi 2020: Not Just Another Image Recognition Dataset,
WACV22(3281-3291)
IEEE DOI 2202
Fungi, Training, Visualization, Codes, Metadata, Benchmark testing, Datasets, Evaluation and Comparison of Vision Algorithms Object Detection/Recognition/Categorization BibRef

Pan, H.L.[Hao-Lin], Hétroy-Wheeler, F.[Franck], Charlaix, J.[Julie], Colliaux, D.[David],
Multi-scale Space-time Registration of Growing Plants,
3DV21(310-319)
IEEE DOI 2201
Measurement, Point cloud compression, Shape, Skeleton, Random forests, 3d vision, shape correspondence, space-time registration BibRef

Valencia, Y.M., Majin, J.J., Taveira, V.B., Salazar, J.D., Stivanello, M.E., Ferreira, L.C., Stemmer, M.R.,
A Novel Method for Inspection Defects in Commercial Eggs Using Computer Vision,
ISPRS21(B2-2021: 809-816).
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Qian, J.X.[Jia-Xin], Yu, P.F.[Peng-Fei], Li, H.Y.[Hai-Yan], Li, H.S.[Hong-Song],
Research on Classification of Wild Fungi Based on Improved Resnet50 Network,
ICIVC21(168-173)
IEEE DOI 2112
Fungi, Training, Image recognition, Transfer learning, Usability, Task analysis, Residual neural networks, wild fungi, transfer learning BibRef

Kushida, T.[Takahiro], Tanaka, K.[Kenichiro], Funatomi, T.[Takuya], Tahara, K.[Komei], Kagawa, Y.[Yukihiro], Mukaigawa, Y.[Yasuhiro],
Practical Descattering of Transmissive Inspection Using Slanted Linear Image Sensors,
MVA21(1-5)
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Food production line. Image sensors, Computational modeling, Frequency-domain analysis, Prototypes, Production, Inspection BibRef

Forero, M.G.[Manuel G.], Beltrán, C.E.[Carlos E.], González-Santos, C.[Christian],
Automatic Classification of Zingiberales from RGB Images,
MCPR21(198-206).
Springer DOI 2108
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Murcia, H.[Harold], Sanabria, D.[David], Méndez, D.[Dehyro], Forero, M.G.[Manuel G.],
A Comparative Study of 3D Plant Modeling Systems Based on Low-Cost 2D LiDAR and Kinect,
MCPR21(272-281).
Springer DOI 2108
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Frank, L.[Logan], Wiegman, C.[Christopher], Davis, J.[Jim], Shearer, S.[Scott],
Confidence-Driven Hierarchical Classification of Cultivated Plant Stresses,
WACV21(2502-2511)
IEEE DOI 2106
Deep learning, Plants (biology), Surveillance, Agriculture, Convolutional neural networks BibRef

Leo, M.[Marco], Carcagnì, P.[Pierluigi], Distante, C.[Cosimo],
A Systematic Investigation on end-to-end Deep Recognition of Grocery Products in the Wild,
ICPR21(7234-7241)
IEEE DOI 2105
Systematics, Image recognition, Pipelines, Machine learning, Radiometry, Object recognition BibRef

Ong, J.D.L.[Josh Daniel L.], Abigan, E.G.T.[Erinn Giannice T.], Cajucom, L.G.[Luis Gabriel], Abu, P.A.R.[Patricia Angela R.], Estuar, M.R.J.E.[Ma. Regina Justina E.],
Ensemble Convolutional Neural Networks for the Detection of Microscopic Fusarium Oxysporum,
ISVC20(I:321-332).
Springer DOI 2103
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Huang, S., Luo, P., Wang, Z.,
Analysis and Study of Egg Quality Based on Hyperspectral Image Data of Different Forms of Egg Yolks,
CVIDL20(177-181)
IEEE DOI 2102
data analysis, food processing industry, food products, hyperspectral imaging, image processing, Egg quality analysis BibRef

Paturkar, A., Gupta, G.S., Bailey, D.,
Plant Trait Segmentation for Plant Growth Monitoring,
IVCNZ20(1-6)
IEEE DOI 2012
Image segmentation, Machine learning algorithms, Neural networks, Training data, Point cloud segmentation BibRef

Yang, C., Baireddy, S., Chen, Y., Cai, E., Caldwell, D., Méline, V., Iyer-Pascuzzi, A.S., Delp, E.J.,
Plant Stem Segmentation Using Fast Ground Truth Generation,
SSIAI20(62-65)
IEEE DOI 2009
biology computing, botany, image segmentation, learning (artificial intelligence), neural nets, Tomato BibRef

Cai, E., Baireddy, S., Yang, C., Crawford, M., Delp, E.J.,
Deep Transfer Learning For Plant Center Localization,
AgriVision20(277-284)
IEEE DOI 2008
Training, Machine learning, Task analysis, Agriculture, Data models, Training data, Shape BibRef

Louedec, J.L., Montes, H.A., Duckett, T., Cielniak, G.,
Segmentation and detection from organised 3D point clouds: A case study in broccoli head detection,
AgriVision20(285-293)
IEEE DOI 2008
Feature extraction, Sensors, Agriculture, Shape, Task analysis, Head BibRef

Chiu, M.T.[Mang Tik], Xu, X.Q.[Xing-Qian], Wang, K.[Kai], Hobbs, J.[Jennifer], Hovakimyan, N.[Naira], Huang, T.S.[Thomas S.], Shi, H.H.[Hong-Hui], Wei, Y.C.[Yun-Chao], Huang, Z.L.[Zi-Long], Schwing, A.[Alexander], Brunner, R.[Robert], Dozier, I.[Ivan], Dozier, W.[Wyatt], Ghandilyan, K.[Karen], Wilson, D.[David], Park, H.S.[Hyun-Seong], Kim, J.[Junhee], Kim, S.H.[Sung-Ho], Liu, Q.H.[Qing-Hui], Kampffmeyer, M.C.[Michael C.], Jenssen, R.[Robert], Salberg, A.B.[Arnt B.], Barbosa, A.[Alexandre], Trevisan, R.[Rodrigo], Zhao, B.C.[Bing-Chen], Yu, S.Z.[Shao-Zuo], Yang, S.W.[Si-Wei], Wang, Y.[Yin], Sheng, H.[Hao], Chen, X.[Xiao], Su, J.Y.[Jing-Yi], Rajagopal, R.[Ram], Ng, A.[Andrew], Huynh, V.T.[Van Thong], Kim, S.H.[Soo-Hyung], Na, I.S.[In-Seop], Baid, U.[Ujjwal], Innani, S.[Shubham], Dutande, P.[Prasad], Baheti, B.[Bhakti], Talbar, S.[Sanjay], Tang, J.Y.[Jian-Yu],
The 1st Agriculture-Vision Challenge: Methods and Results,
AgriVision20(212-218)
IEEE DOI 2008
Semantics, Image segmentation, Agriculture, Computational modeling BibRef

Phillips, T., Abdulla, W.,
Class Embodiment Autoencoder (CEAE) for classifying the botanical origins of honey,
IVCNZ19(1-5)
IEEE DOI 2004
botany, data compression, feature extraction, image classification, neural nets, CEAE, New Zealand honey, class embodiment autoencoder, hyperspectral imaging BibRef

Koporec, G., Perš, J.,
Deep Learning Performance in the Presence of Significant Occlusions: An Intelligent Household Refrigerator Case,
ACVR19(2532-2540)
IEEE DOI 2004
learning (artificial intelligence), object detection, rendering (computer graphics), deep learning performance, refrigerator BibRef

Riegler-Nurscher, P.[Peter], Prankl, J.[Johann], Vincze, M.[Markus],
Tillage Machine Control Based on a Vision System for Soil Roughness and Soil Cover Estimation,
CVS19(201-210).
Springer DOI 1912
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Saberi, A., Khesali, E., Fakhri, M., Enayati, H., Koushapoor, M.,
Design and Evaluation of a Controller to Achieve Optimum Seeding Rate With Specific Spatial Management in Agricultural Machinery,
SMPR19(917-921).
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Deglint, J.L.[Jason L.], Jin, C.[Chao], Wong, A.[Alexander],
Investigating the Automatic Classification of Algae Using the Spectral and Morphological Characteristics via Deep Residual Learning,
ICIAR19(II:269-280).
Springer DOI 1909
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Follmann, P.[Patrick], Drost, B.[Bertram], Böttger, T.[Tobias],
Acquire, Augment, Segment and Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products,
GCPR18(363-376).
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Jiang, Y.J.[Yi-Jun], Schenck, E.[Elim], Kranz, S.[Spencer], Banerjee, S.[Sean], Banerjee, N.K.[Natasha Kholgade],
CNN-Based Non-contact Detection of Food Level in Bottles from RGB Images,
MMMod19(I:202-213).
Springer DOI 1901
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Galati, R., Reina, G., Messina, A., Gentile, A.,
Survey and navigation in agricultural environments using robotic technologies,
AVSS17(1-6)
IEEE DOI 1806
agriculture, farming, image fusion, intelligent robots, mobile robots, robot vision, telerobotics, Wheels BibRef

Bhosle, K., Musande, V.,
Stress Monitoring of Mulberry Plants By Finding Rep Using Hyperspectral Data,
Hannover17(383-386).
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Gao, K., White, T., Palaniappan, K., Warmund, M., Bunyak, F.,
Museed: A mobile image analysis application for plant seed morphometry,
ICIP17(2826-2830)
IEEE DOI 1803
Image analysis, Image edge detection, Image segmentation, Kernel, Mobile applications, Shape, image analysis, mobile application, plant seed morphometry BibRef

Alves, W.A.L.[Wonder A. L.], Gobber, C.F.[Charles F.], Hashimoto, R.F.[Ronaldo F.],
Plant Bounding Box Detection from Desirable Residues of the Ultimate Levelings,
ICIAR18(474-481).
Springer DOI 1807
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CIARP17(220-228).
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Chen, Y., Ribera, J., Boomsma, C., Delp, E.J.[Edward J.],
Locating Crop Plant Centers from UAV-Based RGB Imagery,
CVPPP17(2030-2037)
IEEE DOI 1802
Greenhouses, Plants (biology), Training data, Unmanned aerial vehicles BibRef

Fiorucci, M.[Marco], Fratton, M.[Marco], Dulecha, T.G.[Tinsae G.], Pelillo, M.[Marcello], Pravato, A.[Alberto], Roncato, A.[Alessandro],
A Computer Vision System for the Automatic Inventory of a Cooler,
CIAP17(I:575-585).
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Ahmad, N.M.[Norul Maslissa], Ali, N.M.[Nazlena Mohamad], Baharin, H.[Hanif],
MyRedList: Virtual Application for Threatened Plant Species,
IVIC17(445-454).
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Carstensen, J.M.,
Fast, versatile, and non-destructive biscuit inspection system using spectral imaging,
MVA17(502-505)
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Image color analysis, Imaging, Indexes, Moisture, Moisture measurement, Reflectivity, Training BibRef

Bindlish, E., Abbott, A.L., Balota, M.,
Assessment of Peanut Pod Maturity,
WACV17(688-696)
IEEE DOI 1609
Agriculture, Calibration, Color, Image color analysis, Imaging, Soil, Visualization BibRef

Mendiola-Lau, V.[Victor], Silva Mata, F.J.[Francisco José], Martínez-Díaz, Y.[Yoanna], Bustamante, I.T.[Isneri Talavera], de Marsico, M.[Maria],
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CIARP16(233-240).
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Marin, R.D.C.[Ricardo D. C.], Green, R.D.[Richard D.],
A Hidden Markov Model for modeling and extracting vine structure in images,
ICVNZ15(1-6)
IEEE DOI 1701
feature extraction BibRef

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A Computer Vision Approach for Automatic Measurement of the Inter-plant Spacing,
CIARP15(219-227).
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Liang, B.[Bing], Song, G.X.[Gu-Xin], Li, G.L.[Gong-Li],
Discussion about the effect of digital plants library on the plants landscape restoration in Yuanmingyuan,
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CVS15(456-465).
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Computer Vision Based Autonomous Robotic System for 3D Plant Growth Measurement,
CRV15(290-296)
IEEE DOI 1507
Image reconstruction BibRef

Lam, A., Kuno, Y., Sato, I.,
Evaluating freshness of produce using transfer learning,
FCV15(1-4)
IEEE DOI 1506
agricultural products BibRef

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CVPR15(878-886)
IEEE DOI 1510
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ICIP14(1648-1652)
IEEE DOI 1502
Aquaculture BibRef

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ICIP14(679-683)
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Afridi, M.J.[Muhammad Jamal], Liu, X.M.[Xiao-Ming], McGrath, J.M.[J. Mitchell],
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ICPR14(148-153)
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ICPR06(II: 877-880).
IEEE DOI 0609
BibRef

Dahl, A.B.[Anders Bjorholm], Aanæs, H.[Henrik], Larsen, R.[Rasmus], Ersbøll, B.K.[Bjarne K.],
Classification of Biological Objects Using Active Appearance Modelling and Color Cooccurrence Matrices,
SCIA07(938-947).
Springer DOI 0706
Active Appearance Models. AAM for logs and vegetables. BibRef

Kita, N., Kita, Y., Yang, H.Q.[Hai-Quan],
Archiving technology for plant inspection images captured by mobile active cameras '4D visible memory',
3DPVT02(208-213). 0206
BibRef

Tadeo, F., Matia, D., Laya, D., Santos, F., Alvarez, T., Gonzalez, S.,
Detection of Phases in Sugar Crystallization Using Wavelets,
ICIP01(III: 178-181).
IEEE DOI 0108
BibRef

Rodenacker, K., Gais, P., Juetting, U., Hense, B.A.,
(Semi-) Automatic Recognition of Microorganisms in Water,
ICIP01(III: 30-33).
IEEE DOI 0108
BibRef

Davies, R.[Roger], Heleno, P.[Paulo], Correia, B.A.B.[Bento A. Brázio], Dinis, J.[João],
VIP3D: An Application of Image Processing Technology for Quality Control in the Food Industry,
ICIP01(I: 293-296).
IEEE DOI 0108
BibRef

Mallant, J.P.,
Visual Inspection in the Food Industry,
SCIA99(Invited Talk). BibRef 9900

Jones, R.[Ronald], Frydendal, I.[Ib],
Segmentation of Sugar Beets Using Image and Graph Processing,
ICPR98(Vol II: 1697-1699).
IEEE DOI 9808
BibRef

Hahn, F.[Federico], Mota, R.[Rafael],
Nobel Chile Jalapeno sorting using structured laser and neural network classifiers,
CIAP97(II: 517-523).
Springer DOI 9709
BibRef

Gregori, M., Lombardi, L., Savini, M., Scianna, A.,
Autonomous plant inspection and anomaly detection,
CIAP97(II: 509-516).
Springer DOI 9709
BibRef

Bolle, R.M., Connell, J.H., Haas, N., Mohan, R., and Taubin, G.,
VeggieVision: A Produce Recognition System,
WACV96(244-251).
IEEE DOI 9609
BibRef

Garcia-Consuegra, J., Cisneros, G., Martinez, A.,
A methodology for woody crop location and discrimination in remote sensing,
CIAP99(810-815).
IEEE DOI 9909
BibRef

Samal, A., Peterson, B., Holliday, D.J.,
Recognizing plants using stochastic L-systems,
ICIP94(I: 183-187).
IEEE DOI 9411
BibRef

Dobrusin, Y.[Yuri], Edan, Y.[Yael], Grinshpun, J.[Joseph], Peiper, U.M.[Uri M.], Wolf, I.[Isaac], Hetzroni, A.[Amots],
Computer image analysis to locate targets for an agricultural robot,
CAIP93(775-779).
Springer DOI 9309
BibRef

Berke, J.[József], Gyorffy, K.[Katalin], Fischl, G.[Géza], Kárpáti, L.[László], Bakonyi, J.[József],
The application of digital image processing in the evaluation of agricultural experiments,
CAIP93(780-787).
Springer DOI 9309
BibRef

Belaid, A.,
Metrology in quality control of nuts,
ICPR90(I: 636-638).
IEEE DOI 9006
BibRef

Fox, J.S., Weldon, Jr., E., and Ang, M.,
Machine Vision Techniques for Finding Sugarcane Seedeyes,
CVPR85(653-655). (Univ. of Hawaii) Hough. Feature Computation. Interesting use of pyramids and hough transform. BibRef 8500

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Inspection of Food Grains .