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to databases like handwritten or printed documents and characters, faces, there is a need of<br />

agricultural databases that will ease in the testing and verification of newly developed image<br />

processing methods.<br />

REFERENCES<br />

1. Lalit P. Saxena1 and Leisa J.(2014) Armstrong2, A survey of image processing techniques for<br />

agriculture.<br />

2. A.B. Ehsanirad And Y. H. S. Kumar (2010), Leaf Recognition For Plant Classification Using<br />

Glcm And Pca Methods, Oriental Journal Of Computer Science & Technology, 3 (1), Pp. 31–36,<br />

2010.<br />

3. Image Analysis Applications In Plant Growth And Health Assessment, Journal Of Agricultural<br />

Faculty Of Mustafa Kemal University.(2017).<br />

4. C. C. Yang, S. O. Prasher, J. A. Landry, J. Perret And H. S. Ramaswamy (2000), Recognition Of<br />

Weeds With Image Processing And Their Use With Fuzzy Logic For Precision Farming,<br />

Canadian Agricultural Engineering, 42 (4), Pp. 195-200, 2000.<br />

5. D. S. Jayas, J. Paliwal and N. S. Visen (2000), Multi-layer neural networks for image analysis of<br />

agricultural products, J. Agric. Engng Res., 77 (2), pp. 119–128, 2000.<br />

6. D. J. Mulla (2013), Twenty five years of remote sensing in precision agriculture: Key advances<br />

and remaining knowledge gaps, Biosystems Engineering, 114, pp. 358–371, 2013.<br />

7. E. E. Kelman and R. Linker (2014), Vision-based localisation of mature apples in tree images<br />

using convexity, Biosystems Engineering, 114, pp. 174–185, 2014.<br />

8. C. J. Du and D. W. Sun (2004), Recent developments in the applications of image processing<br />

techniques for food quality evaluation, Trends in Food Science & Technology, 15, pp. 230–249,<br />

2004.<br />

9. C. Puchalski, J. Gorzelany, G. Zagula and G. Brusewitz (2008), Image analysis for apple defect<br />

detection, detection, Biosystems and Agricultura Engineering, 8, pp. 197–205, 2008.

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