EAGROBOT - A robot for early crop disease detection using image processing

2nd International Conference on Electronics and Communication Systems, ICECS 2015

Volumn , Issue , 2015, Pages 1684-1689

  Pilli, Sai Kirthi ^a   Nallathambi, Bharathiraja ^a   George, Smith Jessy ^a   Diwanji, Vivek ^a  

^a Life Sciences Digital Business Operations   (India)

Author keywords

Agricultural robot; Image processing; pest identification

Indexed keywords

AGRICULTURAL ROBOTS; CROPS; DECISION SUPPORT SYSTEMS; PESTICIDES; REMOTE SENSING; ROBOTS;

CONTROLLED IRRIGATIONS; CRITICAL GROWTH; CROP DISEASE; DECISION SUPPORTS; GROUND BASED; NUTRIENT DEFICIENCY; PLANT DISEASE; REAL-TIME TESTING;

IMAGE PROCESSING;

EID: 84942626391     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ECS.2015.7124873     Document Type: Conference Paper

References (20)

1. Moran, M. Susan: "Image-Based Remote Sensing for Agricultural Management-Perspectives of Image Providers, Research Scientists, and Users. " Proceedings of the 2nd International Conference on Geospatial Information in Agriculture and Forestry; Jan. 10-12, 2000
2. Fritz Brugger, "Mobile Applications in Agriculture", Syngenta Foundation, Basel, Switzerland, 2011
3. Laudien, R., Bareth, G. & Doluschitz, R., 2004b: "Comparison of remote sensing based analysis of crop diseases by using high resolution multispectral and hyperspectral data-case study: Rhizoctonia solani in sugar beet" in Proceedings of the 12th International Conference on Geoinformatics, June 7th-9th, Gävle, p. 670-676
4. Pierre Sibiry Traoré, "The view from above" in ICT Update, a remote sensing scientist and GIS head at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 23 February 2010
5. Lilienthal H, Ponomarev M, Schnug E 2004 Application of LASSIE to improve agricultural field experimentation. Landbauforsch Völkenrode 54(1):21-26 Online. Available: http://literatur. vti. bund. de/digbib-extern/bitv/zi032847. pdf
6. Indira Devi P, "Pesticides in agriculture A boon or a curse; A case study of Kerala", in Economic and Political Weekly, Vol: 45, Issue: 26, pp: 199, June 2010
7. Sayantan Bera, "Toxic punch: heavy use of pesticides depleting local fish and bird populations", Nov 3, 2012. Online. Available: http://www. downtoearth. org. in/content/toxic-punch-heavy-usepesticides-depleting-local-fish-and-bird-populations
8. Weizheng, S., Yachun, W., Zhanliang, C., and Hongda, W. (2008). "Grading Method of Leaf Spot Disease Based on Image Processing" Proceedings of the 2008 international Conference on Computer Science and Software Engineering-Volume 06 (December 12-14, 2008). CSSE. IEEE Computer Society, Washington, DC, 491-494.
9. Prasad Babu, M. S. And Srinivasa Rao, B. "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.
10. Camargo, A. And Smith, J. S., "An imageprocessing based algorithm to automatically identify plant disease visual symptoms, Biosystems Engineering", Volume 102, Issue 1, January 2009, Pages 9-21, ISSN 1537-5110.
11. Hillnhuetter, C. And A.-K. Mahlein, "Early detection and localisation of sugar beet diseases: new approaches", Gesunde Pfianzen 60 (4) (2008), pp. 143-149.
12. Rumpf, T., A.-K. Mahlein, U. Steiner, E.-C. Oerke, H.-W. Dehne, L. Plumer, "Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance", Computers and Electronics in Agriculture, Volume 74, Issue 1, October 2010, Pages 91-99, ISSN 0168-1699.
13. Al-Bashish, D., M. Braik and S. Bani-Ahmad. "Detection and classification of leaf diseases using Kmeans-based segmentation and neural-networks-based classification". Inform. Technol. J., 10: 267-275.
14. Bauer, S. D.; Korc, F., Förstner, W. "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 2009. Wageningen Academic Publishers, p. 229-238.
15. Camargo, A. And Smith, J. S. "An image processing based algorithm to automatically identify plant disease visual symptoms", Biosystems Engineering, Volume 102, Issue 1, January 2009, Pages 9-21, ISSN 1537-5110.
16. Wang, X., M. Zhang, J. Zhu and S. Geng, "Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN)", International Journal of Remote Sensing 29 (6) (2008), pp. 1693-1706.
17. H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik and Z. ALRahamneh. "Fast and Accurate Detection and Classification of Plant Diseases". In International Journal of Computer Applications (0975-8887) Volume 17-No. 1, March 2011
18. J. K. Sainis, R. Rastogi, V. K. Chadda, "Applications of image processing in biology and agriculture" barc newsletter online. Available:http://www. barc. gov. in/publications/nl/1999/199905-01. pdf
19. Precision agriculture '09. edited by: E. J. van Henten, D. Goense and C. Lokhorst, "BoniRob: An autonomous field robot platform for individual plant phenotyping". By A. Ruckelshausen, P. Biber, M. Dorna, H. Gremmes, R. Klose, A. Linz, R. Rahe, R. Resch, M. Thiel, D. Trautz and U. Weiss Page 841-847, SBN: 978-90-8686-113-2.
20. Chaitra Bahuman, Siddharth Nair, Anil Bahuman, "Building A Digital Library of Agricultural Documents Using Open Source Software", National Seminar on Empowering the Masses through Technology Application and Skill Training, Chandigarh, December 2005