Image processing has been proved to be an effective tool for analysis in various fields and applications. Agriculture sector where the parameters like canopy, yield, quality of the product were the important measures from the farmers� point of view. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, yield mapping, robotic harvesting, fruit grading, weed detection, and leaves disease detection.

Color Features; Texture Features; Classifier; Machine Vision

[1] J. Blasco, N. Aleixos, J. Gomez, and E. Molto, �Citrus sorting by identification of the most common defects using multispectral computer vision,� Journal of Food Engineering, vol. 83(3), pp. 384�393, 2007.
[2] J. Blasco, N. Aleixos, J. Gomez-Sanchis, and E. Molto, �Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features,� Biosystems Engineering, vol. 103, pp. 137�145, 2009.
[3] K. Vijayarekha, and R. Govindaraj, �Citrus fruit external defect classification using wavelet packet transform textures and ANN,� IEEE international conference on industrial technology, pp. 2872-2877. Doi: 10.1109/ICIT.2006.372646.
[4] R. Renu, and D. V. Chidanand, �Internal Quality Classification of agricultural produceusing Non-destructive Image Processing Technologies (soft X-ray)� International Journal of Latest Trends in Engineering and Technology, vol. 2(4), pp. 535-543, 2013.
[5] M.M. Blanke, �Prediction of apple yields in Europe-present and new approaches in research,� In L. Smith (Ed.), Proceedings 106th annual meeting Washington state horticultural association, pp. 68-75. Yakima, WA: WSHA Publishing, 2011.
[6] D.M. Bulanon, T. Kataoka, Y. Ota, and T. Hiroma, �A segmentation algorithm for the automatic recognition of fuji apples,� Biosystems Engineering. Vol. 83, pp. 405�412, 2002.
[7] M.W. Hanan, T.F. Burks, and D.M. Bulanon, �A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection,� CIGR Ejournal Vol. XI, 2009.
[8] R. Zhou, L. Damerow, Y.Sun and M. M. Blanke,� Using colour features of cv. �Gala� apple fruits in an orchard in image processing to predict yield,� Precision Agric. DOI 10.1007/s11119-01209269-2, vol. 13, pp. 568-580, 2012.
[9] D. Stajnko, J. Rakun and M. Blanke, �Modelling Apple Fruit Yield Using Image Analysis for Fruit Colour,� Shape and Texture,Europ.J.Hort.Sci,ISSN 1611-4426. � Verlag Eugen Ulmer KG, Statgart, vol. 74(6), pp. 260-267, 2009.
[10] Y. Sarig, �Mechanized fruit harvesting-Site Specific Solutions,� Information and Technology for Sustainable Fruit and Vegetable Production, FRUTIC 05: pp. 237-247, 2005.
[11] M.W. Hanan, T.F. Burks, and D.M. Bulanon, �A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection,� CIGR Ejournal Vol. XI, 2009.
[12] Y. Wang, X. Zhu, and C. Ji, �Machine vision based cotton recognition for cotton harvesting robot,� Computer and Computing Technologies in Agriculture, vol. 2, pp. 1421-1425, 2008.
[13] A.S. Ogale, and Y. Aloimonos, �A roadmap to the integration of early visual modules,� International Journal of Computer Vision, vol. 72(1), pp. 9-25, 2007.
[14] D. Al-Bashish, M. Braik, and S. Bani-Ahmad, �Detection and classification of leaf diseases using Kmeans-based segmentation and neural-networks-based classification,� Inform. Technol. J., vol. 10, pp. 267-275. DOI:10.3923/itj.2011.267.275, 2011.
[15] T. Rumpf, A.K. Mahlein, U. Steiner, E.C. Oerke, H.W. Dehne, and L. Plumer, �Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance,� Computers and Electronics in Agriculture, Vol. 74(1), Pages 91-99, ISSN 0168-1699, DOI:10.1016/j.compag.2010.06.009, 2010.
[16] C. Hillnhuetter, and A.K. Mahlein, �Early detection and localisation of sugar beet diseases: new approaches,� Gesunde Pfianzen vol. 60 (4), pp. 143�149, 2008.
[17] M. S.Prasad Babu, and B. Srinivasa Rao, �Leaves recognition using back-propagation neural network - advice for pest and disease control on crops,� Technical report, Department of Computer Science & Systems Engineering, Andhra University, India. Downloaded from www.indiakisan.net on May 2010.
[18] A. Camargo, and J. S. Smith, �An imageprocessing based algorithm to automatically identify plant disease visual symptoms,� Biosystems Engineering, Vol.102 (1), pp. 9-21, ISSN15375110,DOI:10.1016/j.biosystemseng.2008.09.030, 2009.
[19] S. D. Bauer, F. Korc, and W.Forstner, �Investigation into the classification of diseases of sugar beet leaves using multispectral images,� In: E.J. van Henten, D. Goense and C. Lokhorst: Precision agriculture 09. Wageningen Academic Publishers, p. 229-238. URL:http://www.precision-crop-protection.unibonn.de/gk_research/project.php?project=3_09, 2009.
[20] S. Weizheng, W. Yachun, C. Zhanliang, and W. Hongda, �Grading Method of Leaf Spot Disease Based on Image Processing,� In Proceedings of the 2008 international Conference on Computer Science and Software Engineering, Vol. 06, IEEE Computer Society, Washington, DC, 491-494. DOI= http://dx.doi.org/10.1109/CSSE. 2008.1649, 2008.
[21] M. Sezgin, and B. Sankur, �Survey over image thresholding techniques and quantitative performance evaluation,� Journal of Electronic Imaging vol. 13(1), pp. 146�165, DOI:10.1117/1.1631315, 2003.
[22] N. Otsu, �A Threshold Selection Method from Gray- Level Histograms,� IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9(1), pp. 62-66, 1979.
[23] Imran Ahmed, Awais Adnan, Salim Gul, and Md Islam, �Edge based real time weed recognition system for selective herbicides,� Proceedings of IMECS, Vol-1, 2008.
[24] Imran Ahmed, Syed shah, Md Islam, and Awais Adnan,� A Real time specific weed recognition system using statistical methods,� World academy of science, engineering and technology, pp 143-145, 2007.
[25] H. Muhammed Siddiqi, Irshad Ahmed, Suziah Sulaiman, �Weed recognition based erosion and dilation segmentation algorithm,� IEEE International conference on education technology and computer, pp 224-228, 2009.
[26] V. K. Mishra, S. Kumar, and R. K. Gupta, �Design and implementation of image fusion system�, IJCSE, International Journal of Computer Sciences and Engineering, Volume 01, Issue03, Page No (182-186), 2014.