Vineyard yield estimation based on the analysis of high resolution images obtained with artificial illumination at night

Sensors (Switzerland)

Volumn 15, Issue 4, 2015, Pages 8284-8301

  Font, Davinia ^a   Tresanchez, Marcel ^a   Martínez, Dani ^a   Moreno, Javier ^a   Clotet, Eduard ^a   Palacín, Jordi ^a  

^a UNIVERSITY OF LLEIDA   (Spain)

Author keywords

Color features; Precision agriculture; Segmentation techniques; Yield estimation

Indexed keywords

COLOR; IMAGE SEGMENTATION; PIXELS;

COLOR FEATURES; ILLUMINATION CONDITIONS; MORPHOLOGICAL FILTERING; PIXEL-BASED SEGMENTATION; PRECISION AGRICULTURE; SEGMENTATION TECHNIQUES; THRESHOLD SEGMENTATION; YIELD ESTIMATION;

IMAGE ANALYSIS;

EID: 84927603060     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s150408284     Document Type: Article

References (44)

1. Fernández-Pacheco, D.G.; Escarabajal-Henarejos, D.; Ruiz-Canales, A.; Conesa, J.; Molina-Martínez, J.M. A digital image-processing-based method for determining the crop coefficient of lettuce crops in the southeast of Spain. Biosyst. Eng. 2014, 117, 23-34.
2. Ampatzidis, Y.G.; Vougioukas, S.G.; Whiting, M.D.; Zhang, Q. Applying the machine repair model to improve efficiency of harvesting fruit. Biosyst. Eng. 2014, 120, 25-33.
3. Font, D.; Pallejà, T.; Tresanchez, M.; Runcan, D.; Moreno, J.; Martínez, D.; Teixidó, M.; Palacín, J. A Proposal for Automatic Fruit Harvesting by Combining a Low Cost Stereovision Camera and a Robotic Arm. Sensors 2014, 14, 11557-11579.
4. Diago, M.P.; Correa, C.; Millán, B.; Barreiro, P.; Valero, C.; Tardaguila, J. Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions. Sensors 2012, 12, 16988-17006.
5. Rodríguez-Pulido, F.J.; Gómez-Robledo, L.; Melgosa, M.; Gordillo, B.; González-Mireta, M.L.; Heredia, F.J. Ripeness estimation of grape berries and seed by image analysis. Comput. Electron. Agric. 2012, 82, 128-133.
6. Usha, K.; Singh, B. Potential applications of remote sensing in horticulture-A review. Sci. Hortic. 2013, 153, 71-83.
7. Pascual, M.; Villar, J.M.; Rufat, J.; Rosell, J.R.; Sanz, R.; Arno, J. Evaluation of peach tree growth characteristics under different irrigation strategies by LIDAR system: Preliminary results. Acta Hortic. 2011, 889, 227-232.
8. Zhang, B.; Huang, W.; Li, J.; Zhao, C.; Fan, S.; Wu, J.; Liu, C. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res. Int. 2014, 62, 326-343.
9. Diago, M.P.; Fernandes, A.M.; Millan, B.; Tardaguila, J.; Melo-Pinto, P. Identification of grapevine varieties using leaf spectroscopy and partial least squares. Comput. Electron. Agric. 2013, 99, 7-13.
10. Clingeleffer, P.; Petrie, P.; Dunn, G.; Martin, S.; Krstic, M.; Welsh, M. Final Report to grape and wine research & development corporation: Crop control for consistent supply of quality winegrapes. CSIRO Division of Horticulture: Victoria, Australia, 2005.
11. Cunha, M.; Abreu, I.; Pinto, P.; Castro, R. Airborne Pollen Samples for Early-Season Estimates of Wine Production in a Mediterranean Climate of Northern Portugal. Am. J. Enol. Vitic. 2003, 54, 189-194.
12. Gil, E.; Landers, A.; Gallart, M.; Llorens, J. Development of two portable patternators to improve drift control and operator training in the operation of vineyard sprayers. Span. J. Agric. Res. 2013, 11, 615-625.
13. Herrera, P.J.; Dorado, J.; Ribeiro, Á. A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method. Sensors 2014, 14, 15304-15324.
14. Martínez-Casasnovas, J.A.; Bordes, X. Viticultura de precisión: Predicción de cosecha a partir de variables del cultivo e índices de vegetación. Rev. Teledetec. 2005, 24, 67-71. (In Spanish)
15. Cunha, M.; Marçal, A; Silva, L. Very early season prediction of wine production based on satellite data from VEGETATION. Int. J. Remote Sens. 2010, 31, 3125-3142.
16. Hemming, J.; Ruizendaal, J.; Hofstee, J.W.; van Henten, E.J. Fruit Detectability Analysis for Different Camera Positions in Sweet-Pepper. Sensors 2014, 14, 6032-6044.
17. Cupec, R.; Filko, D.; Vidović, I.; Nyarko, E.K.; Hocenski, Ž. Point Cloud Segmentation to Approximately Convex Surfaces for Fruit Recognition. In Proceedings of the Croatian Computer Vision Workshop, Zagreb, Croatia, 16 September 2014; pp. 56-61.
18. Teixido, M.; Font, D.; Palleja, T.; Tresanchez, M.; Nogues, M.; Palacin, J. Definition of Linear Color Models in the RGB Vector Color Space to Detect Red Peaches in Orchard Images Taken under Natural Illumination. Sensors 2012, 12, 7701-7718.
19. Jiménez, A.R.; Ceres, R.; Pons, J.L. A survey of computer vision methods for locating fruit on trees. Trans. ASAE 2000, 43, 1911-1920.
20. Yamamoto, K.; Guo, W.; Yoshioka, Y.; Ninomiya, S. On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods. Sensors 2014, 14, 12191-12206.
21. Sites, P.; Delwiche, M.J. Computer vision to locate fruit on a tree. Trans. ASAE 1988, 31, 255-263.
22. Bulanon, D.M.; Burks, T.F.; Alchanatis, V. Image fusion of visible and thermal images for fruit detection. Biosyst. Eng. 2009, 103, 12-22.
23. Teixido, M.; Font, D.; Palleja, T.; Tresanchez, M.; Nogues, M.; Palacin, J. An Embedded Real-Time Red Peach Detection System Based on an OV7670 Camera, ARM Cortex-M4 Processor and 3D Look-Up Tables. Sensors 2012, 12, 14129-14143.
24. Song, Y.; Glasbey, C.A.; Horgan, G.W.; Polder, G.; Dieleman, J.A.; van der Heijden, G.W.A.M. Automatic fruit recognition and counting from multiple images. Biosyst. Eng. 2014, 118, 203-215.
25. Bai, X.D.; Cao, Z.G.; Wang, Y.; Yu, Z.H.; Zhang, X.F.; Li, C.N. Crop segmentation from images by morphology modeling in the CIE L*a*b* color space. Comput. Electron. Agric. 2013, 99, 21-34.
26. Cubero, S.; Diago, M.P.; Blasco, J.; Tardáguila, J.; Millán, B.; Aleixos, N. A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis. Biosyst. Eng. 2014, 117, 62-72.
27. Reis, M.J.C.S.; Morais, R.; Peres, E.; Pereira, C.; Contente, O.; Soares, S.; Valente, A.; Baptista, J.; Ferreira, P.J.S.G.; Cruz, J.B. Automatic detection of bunches of grapes in natural environment from color images. J. Appl. Log. 2012, 10, 285-290.
28. Nuske, S.; Wilshusen, K.; Achar, S.; Yoder, L.; Narasimhan, S.; Singh, S. Automated Visual Yield Estimation in Vineyards. J. Field Robot. 2014, 31, 837-860.
29. National Grape Registry. Available online: http://ngr.ucdavis.edu/index4.cfm (accessed on 11 December 2014).
30. Nuske, S.; Achar, S.; Bates, T.; Narasimhan, S.; Singh, S. Yield Estimation in Vineyards by Visual Grape Detection. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, USA, 25-30 September 2011; pp. 2352-2358.
31. Liu, S.; Marden, S.; Whitty, M. Towards Automated Yield Estimation in Viticulture. In Proceedings of Australasian Conference on Robotics and Automation, Sidney, Australia, 2-4 December 2013.
32. Linker, R.; Kelman, E. Apple detection in nightime tree images using the geometry of light patches around highlights. Comput. Electron. Agric. 2015, in press.
33. Otsu, N. A Threshold Selection Method from Gray-Level Histograms. Automatica 1975, 11, 23-27.
34. Smith, A.R. Color gamut transformation pairs. ACM Siggraph Comput. Graph. 1978, 12, 12-19.
35. Slaughter, D.C.; Harrel, R.C. Color vision in robotic fruit harvesting. Trans. ASAE 1987, 30, 1144-1148.
36. Dunn, G.M.; Martin, S.R. Yield prediction from digital image analysis: A technique with potential for vineyard assessments prior to harvest. Aust. J. Grape Wine Res. 2004, 10, 196-198.
37. Feng, G.; Qixin, C. Study on Color Image Processing Based Intelligent Fruit Sorting System. Fifth World Congress on Intelligent Control Automation, Hangzhou, China, 15-19 June 2004; pp. 4802-4805.
38. Ohta, Y.I.; Kanade, T.; Sakai, T. Color information for region segmentation. Comput. Graph. Image Process. 1980, 13, 222-241.
39. Jack, K. Color spaces. In Digital Video and DSP, 1st ed.; Newnes: Burlington, VT, USA, 2008; pp. 15-29.
40. Cheng, H.D.; Jiang, X.H.; Sun, Y.; Wang, J. Color image segmentation: advances and prospects. Pattern Recognit. 2001, 34, 2259-2281.
41. Mahalanobis, P.C. On the generalized distance in statistics. Proc. Natl. Inst. Sci. India 1936, 2, 49-55.
42. Slaughter, D.C. Environmentally adaptive segmentation algorithm for outdoor image segmentation. Comput. Electron. Agric. 1998, 21, 153-168.
43. Slaughter, D.C.; Harrel, R.C. Discriminating fruit for robotic harvest using color in natural outdoor scenes. Trans. ASAE 1989, 32, 757-763.
44. Blom, P.E.; Tarara, J.M. Trellis Tension Monitoring Improves Yield Estimation in Vineyards. Hortscience 2009, 44, 678-685.