Survey Report on Image Processing in Agriculture
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
India is an agricultural country; wherein about 70% of the population depends on agriculture. Agriculture has changed more in the past century than it has since farming began many millennia ago. Research in agriculture is aimed towards increase of productivity and food quality at reduced expenditure and with increased profit. Current crop production practices, often called precision agriculture (PA), benefited from all earlier revolutions in crop production. Many times expert advice may not be affordable, majority times the availability of expert and their services may consume time. Image processing along with availability of communication network can change the situation of getting the expert advice well within time and at affordable cost since image processing was the effective tool for analysis of parameters. Image processing techniques in agriculture are mainly used for image categorization based on shape, size, color and texture for decision-making. This leads to agricultural input rationalization and environmental damage reduction by adjusting the agricultural practices like fertilizer and pesticide application to the site that demands and profit maximization. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, weed detection and fruit grading. This paper presents a survey on some of the existing image processing techniques in agriculture.
Copyright © 2015 Praise Worthy Prize - All rights reserved.
Marian Wiwarta, Gabriel Fordon ́skib, Krystyna Zuk-Gołaszewskac, Elzabieta Suchowilskaa, (2009) , "Early diagnostics of macronutrient deficiencies in three legume species by color image analysis", Computers and Electronics in Agriculture, 125–132
Camargoa, J.S. Smith, (2009), "An image-processing based to automatically identify plant disease visual symptoms", biosystems engineering 9–21
Xu Liming, Zhao Yanchao, (2010), "Automated strawberry grading system based on image processing", Computers and Electronics in Agriculture 71S S32–S39
Fernando López-García, Gabriela Andreu-García, José Blasco, Nuria Aleixos, José-Miguel Valiente, (2010) , "Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach", Computers and Electronics in Agriculture 71 189–197
Anderson Rochaa, Daniel C. Hauaggeb, Jacques Wainera, Siome Goldensteina, (2010), "Automatic fruit and vegetable classification from images", Computers and Electronics in Agriculture 96–104
Bolle, R.M., Connell, J.H., Haas, N., Mohan, R., Taubin, G., (1996) "Veggievision: a produce recognition system" In: WACV, Sarasota, USA, pp. 1–8.
Jongman Cho, Junghyeon Choi, Mu Qiao, Chang-woo Ji, Hwang-young Kim, Ki-baik Uhm, and Tae-soo Chon, (2007), "Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis", International Journal of Mathematics and Computers In Simulation, Issue 1, Volume 1
M. Guijarroa, G. Pajaresb, I. Riomorosc, P.J. Herrerad, X.P. Burgos-Artizzue, A. Ribeiroe, (2011), "Automatic segmentation of relevant textures in agricultural images", Computers and Electronics in Agriculture.75–83
J. Blasco, N. Aleixos, J. Go ́ mez, E. Molto, (2007), "Citrus sorting by identification of the most common defects using multispectral computer vision", Journal of Food Engineering 384–393
A.J. Pérez, F. López, J.V. Benlloch, S. Christensen, (1997) "Colour and shape analysis techniques for weed detection in cereal fields", First European Conference for Information Technology in Agriculture, Copenhagen, 15-18 June.
Wiesnerova ́ Dana, Wiesner Ivo, (2008), "Computer image analysis of seed shape and seed color for flax cultivar description", computers and electronics in agriculture 126–135
Xavier P. Burgos-Artizzua, Angela Ribeiroa, Maria Guijarrob, Gonzalo Pajares, (2011) "Real-time image processing for crop/weed discrimination in maize fields", Computers and Electronics in Agriculture, 337–346
Ebrahim Ebrahimi, Kaveh Mollazade, Arman Arefi,( 2011),"Detection of Greening in Potatoes using Image Processing Techniques", Journal of American Science,7(3)
T. Rumpf, A.-K. Mahlein, U. Steiner E.-C. Oerke, H.-W. Dehne, L. Plümer, (2010) "Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance", Computers and Electronics in Agriculture , 91–99
Camargo, J.S. Smith, (2009) "Image pattern classification for identification of disease causing agents in plants", Computers and Electronics in Agriculture 121–125
Isabelle Philipp, Thomas Rath, (2002), "Improving plant discrimination in image processing by use of different colour space transformations", Computers and Electronics in Agriculture , 1–15
Juan Ignacio Arribas, Gonzalo V. Sánchez-Ferrero, Gonzalo Ruiz-Ruiz, Jaime Gómez-Gil, (2011), "Leaf classification in sunflower crops by computer vision and neural networks", Computers and Electronics in Agriculture 9–18
David Story, Murat Kacira, Chieri Kubota, Ali Akoglu, Lingling, (2010) "Lettuce calcium deficiency detection with machine vision computed plant features in controlled environments", Computers and Electronics in Agriculture 74 238–243
Kamarul Hawari Ghazali, Mohd. Marzuki Mustafa and Aini Hussain, (2008) "Machine Vision System for Automatic Weeding Strategy using Image Processing Technique", American-Eurasian J. Agric. & Environ. Sci., 3 (3): 451-458, ISSN 1818-6769
Adán Mercado-Luna, Enrique Rico-García, Alfredo Lara-Herrera, Genaro Soto-Zarazúa, Rosalía Ocampo-Velázquez, Ramón Guevara-González, Gilberto Herrera-Ruiz and Irineo Torres-Pacheco, (2010),"Nitrogen determination on tomato (Lycopersicon esculentum Mill.) seedlings by color image analysis (RGB) ", African Journal of Biotechnology Vol. 9(33), pp. 5326-5332, ISSN 1684–5315 ©2010 Academic Journals
Narendra V G, Hareesh K S, (2010), "Prospects and Computer Vision Automated Grading and Sorting Systems in Agricultural and Food Products for Quality Evaluation", International Journal of Computer Applications (0975 – 8887) Volume 1 – No. 4.
Victor Alchanatis, Leonid Ridel, Amots Hetzroni, Leonid Yaroslavsky, (2005) " Weed detection in multi-spectral images of cotton fields", Computers and Electronics in Agriculture 47 243–260
Guo-Quan Jiang, Cui-Jun Zhao, Yong-Sheng Si, (2010) "A Machine Vision Based Crop Rows Detection For Agricultural Robots", Proceedings of the 2010 International Conference on Wavelet Analysis and Pattern Recognition, Qingdao, 11-14 July
Cunha, J.B., "Application of image processing techniques in the characterization of plant leafs," in Industrial Electronics, 2003. ISIE '03. 2003 IEEE International Symposium on , vol.1, no., pp.612-616 vol. 1, 9-11 June 2003
Yong Wu and Yi Pan, 2010, "Cereal Grain Size measurement Based on Image Processing Technology" International Conference on Intelligent Control and Information Processing August 13-15, - Dalian, China
Jinghui Li, Lingwang Gao, Zuorui Shen,(2010), "Extraction and analysis of digital images feature of three kinds of wheat diseases",3rd International Congress on Image and Signal Processing (CISP2010)
Chakraborty, A.K.; Sanyal, P.; Mukherjee, S.; Chatterjee, S.; Hazra, P.; Bandyopadhyay, S.K., "Image Based Analysis of Tomato Leaves to Determine the Number of Petioles," in Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of , vol., no., pp.312-317, 4-7 Dec. 2009
Bin Ji; Weixing Zhu; Bo Liu; Changhua Ma; Xianfeng Li, "Review of Recent Machine-Vision Technologies in Agriculture," in Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on , vol.3, no., pp.330-334, Nov. 30 2009-Dec. 1 2009
WANG Li-shu, (2010) "Soybean Leaves Nitrogen elements information collected preprocessing based on Model identification", 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE).
Young K. Chang, Qamar Zaman, Aitazaz A. Farooque, Arnold W. Schumann, David C. Percival, (2012) , "An automated yield monitoring system II for commercial wild blueberry double-head harvester", Computers and Electronics in Agriculture 81 97–103
Mohammad Ei –Helly, Ahmed Rafea, Salwa Ei – Gamal And Reda Abd Ei Whab Integrating Diagnostic Expert System With Image Processing Via Loosely Coupled Technique, Central Laboratory for Agricultural Expert System(CLAES).
Panagiotis Tzionas, Stelios E. Papadakis and Dimitris Manolakis Plant leaves classification based on morphological features and fuzzy surface selection technique, 5th International Conference ON Technology and Automation ICTA’05, Thessaloniki, Greece, pp.365-370,15-16 .
Rakesh Kaundal, Amar S Kapoor and Gajendra PS Raghava Machine learning techniques in disease forecasting: a case study on rice blast prediction, BMC Bioinformatics.
M. S. Prasad Babu and B. Srinivasa Rao Leaves Recognition Using Back Propagation Neural Network-Advice For Pest and Disease Control On Crops, IndiaKisan.Net: Expert Advissory System.
Alexander A. Doudkin , Alexander V. Inyutin, Albert I. Petrovsky, Maxim E. Vatkin Three Level Neural Network for Data Clusterzation on Images of Infected Crop Field, Journal of Research and Applications in Agricultural Engineering, Vol.52(1).
Stephen Gang Wu, Forrest Sheng Bao, Eric You Xu, Yu – Xuan Wang Yi – Fan Chang A Leaf Recognition Algorithm for Plant Classification Using Probabilistic Neural Network , IEEE 7th International Symposium on Signal Processing and Information Technology.
M. T. Maliappis, K. P. Ferentinos, H. C. Passam And A. B. Sideridis  Gims: A Web based Greenhouse Intelligent Management System, World Journal of AGRICLTURAL Sciences 4(5):640-647.
Santanu Phadikar & Jaya Sil Rice Disease Identification Using Pattern Recognition Techniques, Proceedings Of 11th International Conference On Computer And Information Technology,25-27
Weizheng S.,Yachun W.,Zhanliang C.& Hongda W.Grading Method Of Leaf Spot Disease Based On Image Processing, Proceedings Of 2008 International Conference On Computer Science And Software Engineering, Volume 06.
A.Meunkaewjinda, P.Kumsawat, K.Attakitmongcol & A.Srikaew Grape leaf disease detection from color imagery system using hybrid intelligent system, Proceedings of ECTICON, 2008,IEEE,PP-513-516.
Geng Ying, Li Miao, Yuan Yuan &Hu Zelin A Study on the Method of Image Pre-Processing for Recognition of Crop Diseases, International Conference on Advanced Computer Control, 2008 IEEE.
S.S. Abu-Naser, K. A. Kashkash and M. Fayyad Developing an Expert System for Plant Disease Diagnosis, Journal of Artificial Intelligence 1(2):78-85.
Xinhong Zhang & Fan Zhang Images Features Extraction of Tobacco Leaves, 2008 Congress on Image and Signal Processing, IEEE computer society, pp-773-776.
Xu Pengyun& Li Jigang  Computer assistance image processing spores counting, 2009 International Asia Conference on Informatics in Control, Automation and Robotics, IEEE computer society, pp-203-206.
Qing Yao, Zexin Guan, Yingfeng Zhou, Jian Tang, Yang Hu, Baojun Yang Application of support vector machine for detecting rice diseases using shape and color texture features, 2009 International Conference on Engineering Computation, IEEE computer society, pp-79-83.
D. Cui, Q. Zhang, M. Li, Y. Zhao, G. Hartman, Detection of soybean rust using a multispectral image sensor, Journal Sensing and Instrumentation for Food Quality and Safety, 3 (1) (2009), pp. 49–56.
Helmi Zulhaidi Mohd Shafri and Nasrulhapiza Hamdan Hyperspectral Imagery for Mapping Disease Infection in Oil Palm Plantation Using Vegetation Indices and Red Edge Techniques, American Journal of Applied Sciences 6(6):1031- 1035.
H.Al-Hiary,S.Bani-Ahmad, M.Reyalat,M.Braik &Z.AlRahamneh  Fast & accurate detection & classification of plant diseases, international journal of computer applications(0975-8887), volume 17- no.1, pp-31-38.
Lino, Antonio Carlos Loureiro; Sanches, Juliana; Fabbro, Inacio Maria Dal. Image processing techniques for lemons and tomatoes classification. Bragantia, Campinas, v. 67, n. 3, p. 785-789, 2008.
F. Lpez-Garca, G. Andreu-Garca, J. Blasco, N. Aleixos and J. M. Valiente, (2010), Automatic detection of skin defects in citrus fruits using a multivariate image, Computers and electronics in Agriculture, vol. 71, pp. 189-197.
S.Mika, G.Rtsch, J.Weston, B.Schkopf, K.R.Miller (1999)Fisher discriminant analysis with kernels,Neural Networks for Signal Processing IX. ―.In: IEEE Signal Processing Society Workshop, pp. 41-48, 1999.
H. Wang, G. Li, Z. Ma and X. Li,( 2012)Image recognition of plant diseases based on backpropagation networks,In: 5th International Congress on Image and Signal Processing (CISP 2012), pp. 894-900
B. K. Cho, M. S. Kim, I. S. Baek, D. Y. Kim, W. H. Lee, J.Kim,H. Bae and Y.S Kim (2013,) Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery. Postharvest Biology and Technology,vol. 76, pp. 40-49, 2013.
O.O. Arjenaki, P. A.Moghaddam and A.M. Motlagh (,2013), Online tomato sorting based on shape, maturity, size, and surface defects using machine vision, Turkish Journal of Agriculture and Forestry, vol.37, pp. 62-68.
Revathi, P.; Hemalatha, M., "Classification of cotton leaf spot diseases using image processing edge detection techniques," in Emerging Trends in Science, Engineering and Technology (INCOSET), 2012 International Conference on , vol., no., pp.169-173, 13-14 Dec. 2012.
Santanu Phadikar & Jaya Sil  Rice Disease Identification Using Pattern Recognition Techniques, Proceedings Of 11th International Conference On Computer And Information Technology, 25-27
4] Geng Ying, Li Miao, Yuan Yuan &Hu Zelin A Study on the Method of Image PreProcessing for Recognition of Crop Diseases, International Conference on Advanced Computer Control, 2008 IEEE.
Ajay A. Gurjar, Viraj A. Gulhane, (2009), Disease Detection on Cotton Leaves by Eigenfeature Regularization and Extraction Technique, International Journal of Electronics, Communication & Soft Computing Science and Engineering (IJECSCSE) Volume 1, Issue 1.
H. Al-Hiary, S. Bani-Ah Mad, M. Reyalat, M. Braik And Z. A Lrahamneh, Fast And Accurate Detection And Classification Of Plant Diseases, IJCA, 2011, 17(1), 31-38, IEEE-2010.
E.Elhariri, N. El-Bendary, M. M. M.Fouad, J.Plato, A. E., Hussein and A. M Hassanien (2014) Multi-class SVM Based Classification Approach for Tomato Ripeness, Innovations in Bio-inspired Computing and Applications, Advances in Intelligent Systems and Computing, vol. 237, pp.175-186.
- » —
Please send any question about this web site to email@example.com
Copyright © 2005-2022 Praise Worthy Prize