Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV

Biosystems Engineering

Volumn 146, Issue , 2016, Pages 16-32

  Senthilnath, J ^a,b   Dokania, Akanksha ^c   Kandukuri, Manasa ^c   K N , Ramesh ^d   Anand, Gautham ^e   Omkar, S N ^a  

^a INDIAN INSTITUTE OF SCIENCE   (India)

^b SOUTH DAKOTA STATE UNIVERSITY   (United States)

^c INDIAN INSTITUTE OF TECHNOLOGY GUWAHATI   (India)

^d BANGALORE UNIVERSITY   (India)

^e National College Basavanagudi   (India)

Author keywords

Spatial segmentation; Spectral clustering; Unmanned aerial vehicle

Indexed keywords

AIRCRAFT DETECTION; ANTENNAS; CLUSTER ANALYSIS; FRUITS; IMAGE SEGMENTATION; MATHEMATICAL MORPHOLOGY; PIXELS; UNMANNED AERIAL VEHICLES (UAV);

BAYESIAN INFORMATION CRITERION; COMPARISON OF PERFORMANCE; EXPECTATION-MAXIMISATION; HIGH SPATIAL RESOLUTION; MORPHOLOGICAL OPERATIONS; SPATIAL SEGMENTATION; SPECTRAL CLUSTERING; SPECTRAL-SPATIAL CLASSIFICATION;

K-MEANS CLUSTERING;

EID: 84983158468     PISSN: 15375110     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.biosystemseng.2015.12.003     Document Type: Article

References (33)

1. Alexandratos, N., Bruinsma, J., World agriculture towards 2030/2050: The 2012 revision. 2012 ESA Work. Pap, 3.
2. Berni, J., Zarco-Tejada, P.J., Suárez, L., Fereres, E., Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. Geoscience and Remote Sensing, IEEE Transactions on 47:3 (2009), 722–738.
3. Córcoles, J.I., Ortega, J.F., Hernández, D., Moreno, M.A., Estimation of leaf area index in onion (Allium cepa L.) using an unmanned aerial vehicle. Biosystems Engineering 115:1 (2013), 31–42.
4. Doraiswamy, P.C., Moulin, S., Cook, P.W., Stern, A., Crop yield assessment from remote sensing. Photogrammetric Engineering & Remote Sensing 69:6 (2003), 665–674.
5. Fawcett, T., ROC Graphs: Notes and practical considerations for researchers. Technical Report HPL-2003-4, HP Labs, 2006.
6. Hannan, M.W., Burks, T.F., Duke, M.B., A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection. Agricultural Engineering International: the CIGR E Journal XI (2009), 1–7.
7. Haralick, R.M., Sternberg, S.R., Zhuang, X., Image analysis using mathematical morphology. Pattern Analysis and Machine Intelligence, IEEE Transactions on 4 (1987), 532–550.
8. Herwitz, S.R., Johnson, L.F., Dunagan, S.E., Higgins, R.G., Sullivan, D.V., Zheng, J., et al. Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support. Computers and Electronics in Agriculture 44:1 (2004), 49–61.
9. Hunt, E.R., Hively, W.D., Fujikawa, S.J., Linden, D.S., Daughtry, C.S., McCarty, G.W., Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing 2:1 (2010), 290–305.
10. Johnson, D.M., An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment 141 (2014), 116–128.
11. Kohonen, T., The self-organizing map. Proceedings of the IEEE 78:9 (1990), 1464–1480.
12. Kooistra, L., Suomalainen, J., Franke, J., Bartholomeus, H., Mücher, S., Becker, R., Monitoring agricultural crops using a light-weight hyperspectral mapping system for unmanned aerial vehicles. EGU General Assembly Conference Abstracts, Vol. 16, 2014, May, 2790.
13. Launay, M., Guerif, M., Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agriculture, Ecosystems & Environment 111:1 (2005), 321–339.
14. Li, H., Zhang, K., Jiang, T., The regularized EM algorithm. Proceedings of the 20th National Conference on Artificial Intelligence, 2005, 807–812.
15. Lobell, D.B., The use of satellite data for crop yield gap analysis. Field Crops Research 143 (2013), 56–64.
16. Mo, X., Liu, S., Lin, Z., Xu, Y., Xiang, Y., McVicar, T.R., Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain. Ecological Modelling 183:2 (2005), 301–322.
17. Patel, H.N., Jain, R.K., Joshi, M.V., Fruit detection using improved multiple features based algorithm. International Journal of Computer Applications 13:2 (2011), 1–5.
18. Regunathan, M., Lee, W.S., Citrus fruit identification and size determination using machine vision and ultrasonic sensors. ASAE Annual International Meeting, 2005, July.
19. Saberioon, M.M., Amin, M.S.M., Anuar, A.R., Gholizadeh, A., Wayayok, A., Khairunniza-Bejo, S., Assessment of rice leaf chlorophyll content using visible bands at different growth stages at both the leaf and canopy scale. International Journal of Applied Earth Observation and Geoinformation 32 (2014), 35–45.
20. Sagar, B.S.D., Mathematical morphology in geomorphology and GISci. 2013, CRC Press.
21. Schwarz, G., Estimating the dimension of a model. The Annals of Statistics 6:2 (1978), 461–464.
22. Selim, S.Z., Ismail, M.A., K-means-type algorithms: a generalized convergence theorem and characterization of local optimality. Pattern Analysis and Machine Intelligence, IEEE Transactions on 1 (1984), 81–87.
23. Seng, W.C., Mirisaee, S.H., (2009, August). A new method for fruits recognition system. Electrical engineering and informatics ICEEI'09. International Conference on, Vol. 1, 2009, IEEE, 130–134 August.
24. Senthilnath, J., Omkar, S.N., Diwakar, P.G., Mani, V., Nitin, K., Shreyas, P.B., Crop stage classification of hyperspectral data using unsupervised techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 6:2 (2013), 861–866.
25. Senthilnath, J., Shivesh, B., Omkar, S.N., Diwakar, P.G., Mani, V., An approach to multi-temporal MODIS image analysis using image classification and segmentation. Advances in Space Research 50:9 (2012), 1274–1287.
26. Shi, H., Xingguo, M., Interpreting spatial heterogeneity of crop yield with a process model and remote sensing. Ecological Modelling 222:14 (2011), 2530–2541.
27. Stajnko, D., Čmelik, Z., Modelling of apple fruit growth by application of image analysis. Agriculturae Conspectus Scientificus (ACS) 70:2 (2005), 59–64.
28. Stajnko, D., Lakota, M., Hočevar, M., Estimation of number and diameter of apple fruits in an orchard during the growing season by thermal imaging. Computers and Electronics in Agriculture 42:1 (2004), 31–42.
29. Sugiura, R., Noguchi, N., Ishii, K., Remote-sensing technology for vegetation monitoring using an unmanned helicopter. Biosystems Engineering 90:4 (2005), 369–379.
30. Tarabalka, Y., Benediktsson, J.A., Chanussot, J., Spectral–spatial classification of hyperspectral imagery based on partitional clustering techniques. Geoscience and Remote Sensing, IEEE Transactions on 47:8 (2009), 2973–2987.
31. Xiang, H., Tian, L., Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosystems Engineering 108:2 (2011), 174–190.
32. Yuping, M., Shili, W., Li, Z., Yingyu, H., Liwei, Z., Yanbo, H., et al. Monitoring winter wheat growth in North China by combining a crop model and remote sensing data. International Journal of Applied Earth Observation and Geoinformation 10:4 (2008), 426–437.
33. Zhou, R., Damerow, L., Sun, Y., Blanke, M.M., Using colour features of cv. ‘Gala’ apple fruits in an orchard in image processing to predict yield. Precision Agriculture 13:5 (2012), 568–580.