20.7.3.7.7 Vegetables Detection, Vegetable Inspection, Roots, Tomatoes, Potatoes

Chapter Contents (Back)
Vegetables. Roots. Application, Vegetables.

Heinemann, P.H., Pathare, N.P., Morrow, C.T.,
An Automated Inspection Station for Machine-Vision Grading of Potatoes,
MVA(9), No. 1, 1996, pp. 14-19.
Springer DOI 9608
BibRef

Zhou, L.Y., Chalana, V., Kim, Y.,
PC-Based Machine Vision System for Real-Time Computer-Aided Potato Inspection,
IJIST(9), No. 6, 1998, pp. 423-433. 9812
BibRef

Polder, G., van der Heijden, G.W.A.M., Young, I.T.,
Tomato sorting using independent component analysis on spectral images,
RealTimeImg(9), No. 4, August 2003, pp. 253-259.
Elsevier DOI 0311
BibRef

Zeng, G.[Guang], Birchfield, S.T.[Stanley T.], Wells, C.E.[Christina E.],
Detecting and Measuring Fine Roots in Minirhizotron Images Using Matched Filtering and Local Entropy Thresholding,
MVA(17), No. 4, September 2006, pp. 265-278.
Springer DOI 0608
BibRef

Zeng, G.[Guang], Birchfield, S.T.[Stanley T.], Wells, C.E.[Christina E.],
Rapid automated detection of roots in minirhizotron images,
MVA(21), No. 3, April 2010, pp. xx-yy.
Springer DOI 1003
BibRef

Cai, J.H.[Jin-Hai], Miklavcic, S.,
Surface fitting for individual image thresholding and beyond,
IET-IPR(7), No. 6, August 2013, pp. 596-605.
DOI Link 1312
background-foreground segmentation. botany BibRef

Kumar, P.[Pankaj], Cai, J.H.[Jin-Hai], Miklavcic, S.[Stan],
Root crown detection using statistics of Zernike moments,
ICARCV12(1130-1135).
IEEE DOI 1304
BibRef
And:
Automated Detection of Root Crowns Using Gaussian Mixture Model and Bayes Classification,
DICTA12(1-7).
IEEE DOI 1303
rice, corn, grass. BibRef

Chen, X.M.[Xu-Ming], Yang, S.X.[Simon X.],
A practical solution for ripe tomato recognition and localisation,
RealTimeIP(8), No. 1, March 2013, pp. 35-51.
WWW Link. 1303
BibRef

Iwasaki, F.[Fumiya], Imamura, H.[Hiroki],
A Robust Recognition Method for Occlusion of Mini Tomatoes Based on Hue Information and the Curvature,
IJIG(15), No. 02, 2015, pp. 1540004.
DOI Link 1505
BibRef

Verma, U.[Ujjwal], Rossant, F.[Florence], Bloch, I.[Isabelle],
Segmentation and size estimation of tomatoes from sequences of paired images,
JIVP(2015), No. 1, 2015, pp. 33.
DOI Link 1512
BibRef

Mairhofer, S.[Stefan], Johnson, J.[James], Sturrock, C.J.[Craig J.], Bennett, M.J.[Malcolm J.], Mooney, S.J.[Sacha J.], Pridmore, T.P.[Tony P.],
Visual tracking for the recovery of multiple interacting plant root systems from X-ray CT images,
MVA(27), No. 5, July 2016, pp. 721-734.
Springer DOI 1608
BibRef

Peebles, M., Lim, S.H., Streeter, L., Duke, M., Au, C.K.,
Ground Plane Segmentation of Time-of-Flight Images for Asparagus Harvesting,
IVCNZ18(1-6)
IEEE DOI 1902
Clutter, Cameras, Image segmentation, Real-time systems, Mathematical model, asparagus, ground plane segmentation BibRef

Sun, J.[Jun], He, X.F.[Xiao-Fei], Wu, M.M.[Min-Min], Wu, X.O.[Xia-Ohong], Shen, J.F.[Ji-Feng], Lu, B.[Bing],
Detection of tomato organs based on convolutional neural network under the overlap and occlusion backgrounds,
MVA(31), No. 5, July 2020, pp. Article31.
Springer DOI 2006
BibRef

Morellos, A.[Antonios], Tziotzios, G.[Georgios], Orfanidou, C.[Chrysoula], Pantazi, X.E.[Xanthoula Eirini], Sarantaris, C.[Christos], Maliogka, V.[Varvara], Alexandridis, T.K.[Thomas K.], Moshou, D.[Dimitrios],
Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis-NIR Spectroscopy,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006
BibRef

Soltaninejad, M., Sturrock, C.J., Griffiths, M., Pridmore, T.P., Pound, M.P.,
Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks,
IP(29), 2020, pp. 6667-6679.
IEEE DOI 2007
Image segmentation, Computed tomography, Soil, Image resolution, Decoding, Biomedical imaging, plant phenotyping BibRef

Yu, G.[Guohao], Zare, A.[Alina], Sheng, H.[Hudanyun], Matamala, R.[Roser], Reyes-Cabrera, J.[Joel], Fritschi, F.B.[Felix B.], Juenger, T.E.[Thomas E.],
Root identification in minirhizotron imagery with multiple instance learning,
MVA(31), No. 6, August 2020, pp. Article43.
Springer DOI 2008
BibRef

Abdulridha, J.[Jaafar], Ampatzidis, Y.[Yiannis], Qureshi, J.[Jawwad], Roberts, P.[Pamela],
Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009
BibRef

Siddiquee, K.N.[Kazy Noor_e_Alam], Islam, M.S.[Md. Shabiul], Ud Dowla, M.Y.[Mohammad Yasin], Rezaul, K.M.[Karim Mohammed], Grout, V.[Vic],
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning,
IET-IPR(14), No. 11, September 2020, pp. 2442-2456.
DOI Link 2009
BibRef

Jayanthi, M.G., Shashikumar, D.R.[Dandinashivara Revanna],
Cucumber disease detection using adaptively regularised kernel-based fuzzy C-means and probabilistic neural network,
IJCVR(10), No. 5, 2020, pp. 385-411.
DOI Link 2009
BibRef

Zhao, J.S.[Jiang-San], Kechasov, D.[Dmitry], Rewald, B.[Boris], Bodner, G.[Gernot], Verheul, M.[Michel], Clarke, N.[Nicholas], Clarke, J.H.L.[Ji-Hong Liu],
Deep Learning in Hyperspectral Image Reconstruction from Single RGB images: A Case Study on Tomato Quality Parameters,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Appeltans, S.[Simon], Pieters, J.G.[Jan G.], Mouazen, A.M.[Abdul M.],
Detection of Leek Rust Disease under Field Conditions Using Hyperspectral Proximal Sensing and Machine Learning,
RS(13), No. 7, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Farrar, M.B.[Michael B.], Wallace, H.M.[Helen M.], Brooks, P.[Peter], Yule, C.M.[Catherine M.], Tahmasbian, I.[Iman], Dunn, P.K.[Peter K.], Bai, S.H.[Shahla Hosseini],
A Performance Evaluation of Vis/NIR Hyperspectral Imaging to Predict Curcumin Concentration in Fresh Turmeric Rhizomes,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Lu, Y.W.[Ya-Wen], Wang, Y.X.[Yu-Xing], Parikh, D.[Devarth], Khan, A.[Awais], Lu, G.Y.[Guo-Yu],
Simultaneous Direct Depth Estimation and Synthesis Stereo for Single Image Plant Root Reconstruction,
IP(30), 2021, pp. 4883-4893.
IEEE DOI 2106
Image reconstruction, Estimation, Solid modeling, Shape, Cameras, Periodic structures, single image depth estimation BibRef

Fernández, C.I.[Claudio I.], Leblon, B.[Brigitte], Wang, J.F.[Jin-Fei], Haddadi, A.[Ata], Wang, K.[Keri],
Detecting Infected Cucumber Plants with Close-Range Multispectral Imagery,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Al-gaashani, M.S.A.M.[Mehdhar S. A. M.], Shang, F.J.[Feng-Jun], Muthanna, M.S.A.[Mohammed S. A.], Khayyat, M.[Mashael], El-Latif, A.A.A.[Ahmed A. Abd],
Tomato leaf disease classification by exploiting transfer learning and feature concatenation,
IET-IPR(16), No. 3, 2022, pp. 913-925.
DOI Link 2202
BibRef

Psiroukis, V.[Vasilis], Espejo-Garcia, B.[Borja], Chitos, A.[Andreas], Dedousis, A.[Athanasios], Karantzalos, K.[Konstantinos], Fountas, S.[Spyros],
Assessment of Different Object Detectors for the Maturity Level Classification of Broccoli Crops Using UAV Imagery,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Moualeu-Ngangué, D.[Dany], Bötzl, M.[Maria], Stützel, H.[Hartmut],
First Form, Then Function: 3D Reconstruction of Cucumber Plants (Cucumis sativus L.) Allows Early Detection of Stress Effects through Leaf Dimensions,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203
BibRef

Cen, Y.[Yi], Huang, Y.[Ying], Hu, S.S.[Shun-Shi], Zhang, L.[Lifu], Zhang, J.[Jian],
Early Detection of Bacterial Wilt in Tomato with Portable Hyperspectral Spectrometer,
RS(14), No. 12, 2022, pp. xx-yy.
DOI Link 2206
BibRef

Guo, Y.[Yan], Xia, H.M.[Hao-Ming], Zhao, X.Y.[Xiao-Yang], Qiao, L.X.[Long-Xin], Qin, Y.[Yaochen],
Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef

Croci, M.[Michele], Impollonia, G.[Giorgio], Blandinières, H.[Henri], Colauzzi, M.[Michele], Amaducci, S.[Stefano],
Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209
BibRef


Wang, Y.K.[Yi-Kai], Dong, Y.P.[Yin-Peng], Sun, F.C.[Fu-Chun], Yang, X.[Xiao],
Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos,
ICCV23(13844-13854)
IEEE DOI Code:
WWW Link. 2401
BibRef

Song, H.[Hao], Panjvani, K.[Karim], Liu, Z.G.[Zhi-Gang], Amar, H.[Huzaifa], Kochian, L.[Leon], Ye, S.J.[Sheng-Jian], Yang, X.[Xuan], Feurtado, J.A.[J. Allan], Chavda, K.[Krunal], Huatatoca, K.A.C.[Karina Angela Chimbo], Eramian, M.[Mark],
Plant Root Occlusion Inpainting with Generative Adversarial Network,
CVPPA23(531-539)
IEEE DOI 2401
BibRef

Gillert, A.[Alexander], Peters, B.[Bo], von Lukas, U.F.[Uwe Freiherr], Kreyling, J.[Jürgen], Blume-Werry, G.[Gesche],
Tracking Growth and Decay of Plant Roots in Minirhizotron Images,
WACV23(3688-3697)
IEEE DOI 2302
Codes, Neural networks, User interfaces, Monitoring, Applications: Environmental monitoring/climate change/ecology, Agriculture BibRef

Yang, S.[Si], Zheng, L.H.[Li-Hua], Chen, X.[Xieyuanli], Zabawa, L.[Laura], Zhang, M.[Man], Wang, M.J.[Min-Juan],
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pods,
AgriVision22(1665-1674)
IEEE DOI 2210
Training, Image segmentation, Analytical models, Visualization, Image synthesis, Computational modeling, Transfer learning, Plant phenotyping BibRef

Lu, Y.W.[Ya-Wen], Lu, G.Y.[Guo-Yu],
3D Modeling Beneath Ground: Plant Root Detection and Reconstruction Based on Ground-Penetrating Radar,
WACV22(697-706)
IEEE DOI 2202
Geometry, Ground penetrating radar, Shape, Convolution, Instruments, Neural networks, Remote Sensing , Vision for Aerial/Drone/Underwater/Ground Vehicles BibRef

Masuda, T.[Takeshi],
Leaf Area Estimation by Semantic Segmentation of Point Cloud of Tomato Plants,
CVPPA21(1381-1389)
IEEE DOI 2112
Training, Image resolution, Annotations, Semantics, Estimation, Production facilities BibRef

Möller, B.[Birgit], Schreck, B.[Berit], Posch, S.[Stefan],
Analysis of Arabidopsis Root Images: Studies on CNNs and Skeleton-Based Root Topology,
CVPPA21(1294-1302)
IEEE DOI 2112
Image segmentation, Time series analysis, Semantics, Systems architecture, Skeleton, Topology BibRef

Gillert, A.[Alexander], Peters, B.[Bo], von Lukas, U.F.[Uwe Freiherr], Kreyling, J.[Jürgen],
Identification and Measurement of Individual Roots in Minirhizotron Images of Dense Root Systems,
CVPPA21(1323-1331)
IEEE DOI 2112
Image segmentation, Head, Semantics, Estimation, Humidity, Length measurement, Skeleton BibRef

Horn, J.[Jannis], Zhao, Y.[Yi], Wandel, N.[Nils], Landl, M.[Magdalena], Schnepf, A.[Andrea], Behnke, S.[Sven],
Robust Skeletonization for Plant Root Structure Reconstruction from MRI,
ICPR21(10689-10696)
IEEE DOI 2105
Soil measurements, Magnetic resonance imaging, Semantics, Soil, Pattern recognition, Task analysis BibRef

Tsironis, V., Bourou, S., Stentoumis, C.,
Tomatod: Evaluation of Object Detection Algorithms on A New Real-world Tomato Dataset,
ISPRS20(B3:1077-1084).
DOI Link 2012
BibRef

Ufuktepe, D.K., Palaniappan, K., Elmali, M., Baskin, T.I.,
Rtip: A Fully Automated Root Tip Tracker For Measuring Plant Growth With Intermittent Perturbations,
ICIP20(2516-2520)
IEEE DOI 2011
Perturbation methods, Microscopy, Tracking, Image resolution, Feature extraction, Kinematics, Plant growth, Shi-Tomasi features, Video Tracking BibRef

Ouhami, M.[Maryam], Es-Saady, Y.[Youssef], El Hajji, M.[Mohamed], Hafiane, A.[Adel], Canals, R.[Raphael], El Yassa, M.[Mostafa],
Deep Transfer Learning Models for Tomato Disease Detection,
ICISP20(65-73).
Springer DOI 2009
BibRef

Masuda, T.,
3D Shape Reconstruction of Plant Roots in a Cylindrical Tank From Multiview Images,
3D-Wild19(2149-2157)
IEEE DOI 2004
cameras, edge detection, feature extraction, image matching, image reconstruction, ray tracing, multiple views BibRef

Tabb, A., Duncan, K.E., Topp, C.N.,
Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets,
WACV18(586-595)
IEEE DOI 1806
Plant roots. computerised tomography, image segmentation, medical image processing, X-ray imaging BibRef

Ding, Y.J.[Yong-Jun], Li, J.Y.[Ji-Ying],
The application of Quantum-inspired ant colony algorithm in automatic segmentation of tomato image,
ICIVC17(341-345)
IEEE DOI 1708
Chaos, Convergence, Image segmentation, Logic gates, Optimization, Sociology, Statistics, image segmentation, quantum ant colony algorithm, quantum individual, tomato, image BibRef

Belan, P.A., Araújo, S.A., Alves, W.A.L.,
An Intelligent Vision-Based System Applied to Visual Quality Inspection of Beans,
ICIAR16(801-809).
Springer DOI 1608
BibRef

Araújo, S.A., Alves, W.A.L., Belan, P.A., Anselmo, K.P.,
A Computer Vision System for Automatic Classification of Most Consumed Brazilian Beans,
ISVC15(II: 45-53).
Springer DOI 1601
BibRef

Janusch, I.[Ines], Kropatsch, W.G.[Walter G.],
Reeb Graphs Through Local Binary Patterns,
GbRPR15(54-63).
Springer DOI 1511
BibRef

Janusch, I.[Ines], Kropatsch, W.G.[Walter G.], Busch, W.[Wolfgang], Ristova, D.[Daniela],
Representing Roots on the Basis of Reeb Graphs in Plant Phenotyping,
PlantType14(75-88).
Springer DOI 1504
BibRef

Mairhofer, S.[Stefan], Sturrock, C.J.[Craig J.], Bennett, M.J.[Malcolm J.], Mooney, S.J.[Sacha J.], Pridmore, T.P.[Tony P.],
Visual Object Tracking for the Extraction of Multiple Interacting Plant Root Systems,
PlantType14(89-104).
Springer DOI 1504
BibRef

Nakini, T.K.D.[Tushar Kanta Das], de Souza, G.N.[Guilherme N.],
Distortion Correction in 3D-Modeling of Root Systems for Plant Phenotyping,
PlantType14(140-157).
Springer DOI 1504
BibRef

Dacal-Nieto, A.[Angel], Formella, A.[Arno], Carrión, P.[Pilar], Vazquez-Fernandez, E.[Esteban], Fernández-Delgado, M.[Manuel],
Common Scab Detection on Potatoes Using an Infrared Hyperspectral Imaging System,
CIAP11(II: 303-312).
Springer DOI 1109
BibRef
And:
Non-destructive Detection of Hollow Heart in Potatoes Using Hyperspectral Imaging,
CAIP11(II: 180-187).
Springer DOI 1109
BibRef

Barnes, M.[Michael], Cielniak, G.[Grzegorz], Duckett, T.[Tom],
Minimalist AdaBoost for Blemish Identification in Potatoes,
ICCVG10(I: 209-216).
Springer DOI 1009
BibRef
Earlier: A1, A3, A2:
Boosting minimalist classifiers for blemish detection in potatoes,
IVCNZ09(397-402).
IEEE DOI 0911
BibRef

Perciano, T.[Talita], Hirata, R.[Roberto], de Castro Jorge, L.A.[Lúcio André],
Ridge Linking Using an Adaptive Oriented Mask Applied to Plant Root Images with Thin Structures,
CIARP10(378-385).
Springer DOI 1011
BibRef

Wang, H.M.[Hong-Mei],
Impact of Magnetic Field on Mung Bean Ultraweak Luminescence,
CISP09(1-3).
IEEE DOI 0910
BibRef

Qi, L.Y.[Li-Yong], Yang, Q.H.[Qing-Hua], Bao, G.J.[Guan-Jun], Xun, Y.[Yi], Zhang, L.[Libin],
A Dynamic Threshold Segmentation Algorithm for Cucumber Identification in Greenhouse,
CISP09(1-4).
IEEE DOI 0910
BibRef

Mühlich, M.[Matthias], Truhn, D.[Daniel], Nagel, K.[Kerstin], Walter, A.[Achim], Scharr, H.[Hanno], Aach, T.[Til],
Measuring Plant Root Growth,
DAGM08(xx-yy).
Springer DOI 0806
BibRef

Aguilar, M.A., Pozo, J.L., Aguilar, F.J., Sanchez-Hermosilla, J., Pàez, F.C., Negreiros, J.,
3D Surface Modeling of Tomato Plants Using Close-Range Photogrammetry,
ISPRS08(B5: 139 ff).
PDF File. 0807
BibRef

Erz, G.[Gregor], Posch, S.[Stefan],
A Region Based Seed Detection for Root Detection in Minirhizotron Images,
DAGM03(482-489).
Springer DOI 0310
BibRef

Kirchgessner, N., Spies, H., Scharr, H., Schurr, U.,
Root growth analysis in physiological coordinates,
CIAP01(589-594).
IEEE DOI 0210
BibRef

Huang, Q., Jain, A.K., Stockman, G.C., Smucker, A.J.M.,
Automatic image analysis of plant root structures,
ICPR92(II:569-572).
IEEE DOI 9208
BibRef

Lefebvre, M., Gil, S., Glassey, M.A., Baur, C., Pun, T.,
3D Computer Vision for Agrotics: The Potato Operation, An Overview,
ICPR92(I:207-210).
IEEE DOI BibRef 9200

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Fruit Detection, Fruit Inspection, Apples, Oranges .


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