Machine vision assessment of mango orchard flowering

Computers and Electronics in Agriculture

Volumn 151, Issue , 2018, Pages 501-511

  Wang, Zhenglin ^a   Underwood, James ^b   Walsh, Kerry B ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

^b UNIVERSITY OF SYDNEY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

FORESTRY; FRUITS; MAPPING; ORCHARDS; PIXELS; VEGETATION;

CONSECUTIVE IMAGES; CORRELATION COEFFICIENT; EARLY-FLOWERING; FRUIT MATURATION; MULTI-VIEW DETECTION; REPEATED MEASURES; SVM CLASSIFICATION; THRESHOLDING;

COMPUTER VISION;

AGRICULTURAL TECHNOLOGY; ASSESSMENT METHOD; DETECTION METHOD; DEVELOPMENTAL STAGE; FLOWERING; FRUIT; FRUIT SET; IMAGE CLASSIFICATION; IMAGE PROCESSING; MACHINE LEARNING; ORCHARD; PIXEL; VISION;

EID: 85048892851     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2018.06.040     Document Type: Article

References (21)

1. Adamsen, F.J., Coffelt, T.A., Nelson, J.M., Barnes, E.M., Rice, R.C., Method for using images from a color digital camera to estimate flower number. Crop Sci. 40 (2000), 704–709.
2. Aggelopoulou, A.D., Bochtis, D., Fountas, S., Swain, K.C., Gemtos, T.A., Nanos, G.D., Yield prediction in apple orchards based on image processing. Precis. Agric. 12 (2011), 448–456.
3. Bargoti, S., Underwood, J., 2016. Image classification with orchard metadata. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). pp. 5164–5170.
4. Davenport, T.L., Ying, Z., Kulkarni, V., White, T.L., Evidence for a translocatable florigenic promoter in mango. Scientia Horticult. 110 (2006), 150–159.
5. Diago, M.P., Sanz-Garcia, A., Millan, B., Blasco, J., Tardaguila, J., Assessment of flower number per inflorescence in grapevine by image analysis under field conditions. J. Sci. Food Agric. 94 (2014), 1981–1987.
6. Dorj, U.-O., Lee, M., A New Method for Tangerine Tree Flower Recognition, Computer Applications for Bio-Technology, Multimedia, and Ubiquitous City, 2012, Springer, 49–56.
7. Guo, W., Fukatsu, T., Ninomiya, S., Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images. Plant Methods, 11, 2015, 7.
8. Hočevar, M., Širok, B., Godeša, T., Stopar, M., Flowering estimation in apple orchards by image analysis. Precis. Agric. 15 (2014), 466–478.
9. Johnson, G.I., Hofman, P.J., Postharvest technology and quarantine treatments. Litz, R.E., (eds.) The Mango: Botany, Production and Uses, 2009, CABI, Oxfordshire, UK.
10. Kernot, I., Meurant, N., Holmes, R., MacLeod, N., Fullelove, G., Bally, I., Mango Information Kit. 1999, Queensland Department of Primary Industries, Brisbane, Queensland.
11. Mustard, M.J., Lynch, S., Flower-bud formation and development in Mangifera indica. Bot. Gaz. 108 (1946), 136–140.
12. Núñez-Elisea, R., Davenport, T.L., Flowering of mango trees in containers as influenced by seasonal temperature and water stress. Scientia Horticult. 58 (1994), 57–66.
13. Oppenheim, D., Edan, Y., Shani, G., Detecting tomato flowers in greenhouses using computer vision. World Acad. Sci. Eng. Technol. Int. J. Comput. Electri. Automation Control Inform. Eng. 11 (2017), 104–109.
14. Payne, A., Walsh, K., Machine vision in estimation of fruit crop yield. Dutta Gupta, S., Ibaraki, Y., (eds.) Plant Image Analysis: Fundamentals and Applications, 2014, CRC Press, London, UK, 329–374.
15. Payne, A., Walsh, K., Subedi, P., Jarvis, D., Estimating mango crop yield using image analysis using fruit at ‘stone hardening’ stage and night time imaging. Comput. Electron. Agric. 100 (2014), 160–167.
16. Qureshi, W.S., Payne, A., Walsh, K.B., Linker, R., Cohen, O., Dailey, M.N., Machine vision for counting fruit on mango tree canopies. Precis. Agric. 18 (2017), 224–244.
17. Silwal, A., Davidson, J.R., Karkee, M., Mo, C., Zhang, Q., Lewis, K., Design, integration, and field evaluation of a robotic apple harvester. J. Field Rob. 34 (2017), 1140–1159.
18. Stein, M., Bargoti, S., Underwood, J., Image based mango fruit detection, localisation and yield estimation using multiple view geometry. Sensors, 16, 2016, 1915.
19. Underwood, J.P., Hung, C., Whelan, B., Sukkarieh, S., Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors. Comput. Electron. Agric. 130 (2016), 83–96.
20. Wang, Z., Verma, B., Walsh, K.B., Subedi, P., Koirala, A., 2016. Automated mango flowering assessment via refinement segmentation. In: 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ), IEEE. pp. 1–6.
21. Wang, Z., Walsh, K.B., Verma, B., On-tree mango fruit size estimation using RGB-D images. Sensors, 17, 2017, 2738.