Image processing Based Detection of Fungal Diseases in Plants

Procedia Computer Science

Volumn 46, Issue , 2015, Pages 1802-1808

  Pujari, Jagadeesh D ^a   Yakkundimath, Rajesh ^b   Byadgi, Abdulmunaf S ^c  

^a Department of Mechanical Engineering   (India)

^b KLEIT   (India)

^c UNIVERSITY OF AGRICULTURAL SCIENCES   (India)

Author keywords

Agriculture crops; Fungal disease; Horticulture crops; Pattern recognition

Indexed keywords

AGRICULTURAL ROBOTS; CLIMATE CHANGE; CROPS; DECISION SUPPORT SYSTEMS; DIAGNOSIS; FUNGI; MACHINERY; PATTERN RECOGNITION;

AGRICULTURAL PRODUCTIONS; AGRICULTURE CROPS; COMPUTER VISION TECHNIQUES; FUNGAL DISEASE; IMAGE PROCESSING TECHNIQUE; PLANT DISEASE DIAGNOSIS; PLANT MONITORING; STRATEGIC DECISIONS;

IMAGE PROCESSING;

EID: 84931464590     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2015.02.137     Document Type: Conference Paper

References (21)

1. Abu-Naser S.S., Kashkash KA, Fayyad M. Developing an Expert System for Plant Disease Diagnosis. Journal of Artificial Intelligence; 2010, Vol. 3, No. 4, pp. 269-276.
2. Al-Hiary H., Bani-Ahmad S, Reyalat M., Braik M, and ALRahamneh. Fast and Accurate Detection and Classification of Plant Diseases. Internationaljournal of Computer Applications; 2013, Vol. 17, No. 1, pp. 31-38.
3. Barbedo JCA. Digital Image Processing Techniques for Detecting, Quantifying and Classifying Plant Diseases. Springer Plus; 2013, Vol.2, No.660, pp. l-12.
4. Bauer SD, Korc F, Forstner W. The Potential of Automatic Methods of Classification to Identify Leaf Diseases from Multispectral Images. Precision Agriculture; 2011, Vol. 12, pp. 361-377.
5. Camargo A and Smith JS, Image Pattern Classification for the Identification of Disease Causing Agents in Plants. Computers and Electronics in Agriculture; 2009, Vol. 66, No. 2, pp. 121-125.
6. Chen YR, Chao K, Moon SK. Machine Vision Technology for Agricultural Applications. Computers and Electronics in Agriculture; 2002, Vol. 36, no.2-3, pp. 173-191.
7. Di Cui, Zhang Q, Li M., Hartman GL, Zhao Y. Image Processing Methods for Quantatively Detecting Soybean Rust from Multispectral Images; Biosystems Engineering; 2010, Vol. 107, No. 3, pp. 186-193.
8. Guru DS, Mallikarjuna PB, Manjunath S. Segmentation and Classification of Tobacco Seedling Diseases. COMPUTE '11 Proc. Fourth Annual Int. conf. ACM; 2011, Bangalore, India.
9. Huang KY. Application of Artificial Neural Network for Detecting Phalaenopsis Seedling Diseases using Color and Texture Features, Computers and Electronics in Agriculture, 2007, Vol.57, no.7, pp. 3-11.
10. Patil JK., Raj Kumar. Advances in Image Processing for Detection of Plant Diseases. Journal of Advanced Bioinformatics Applications and Research; 2011, Vol. 2, No. 2, pp. 135-141.
11. Pujari JD, Yakkundimath R, Byadgi AS Grading and Classification of anthracnose fungal disease in fruits. International Journal of Advanced Science and Technology; 2013, Vol. 52, No. 3, pp. 121-132.
12. Pujari JD, Yakkundimath R, Byadgi AS Statistical Methods for Quantitatively Detecting Fungal Disease from Fruits' Images. International Journal of Intelligent systems and Applications in Engineering; 2013, Vol. 1, No. 4, pp. 60-67.
13. Pujari JD, Yakkundimath R, Byadgi AS Detection and Classification with Radon Transform and Support Vector Machine affected on Cereals. International Journal of Computer Vision and Robotics; 2014, Vol. 4, No. 4, pp. 261-280.
14. Pujari JD, Yakkundimath R, Byadgi AS Automatic Fungal Disease Detection based on Wavelet Feature Extraction and PCA Analysis in Commercial Crops; International Journal of Image, Graphics and Signal Processing, 2014, vol. 1, no. 12, pp. 24-31.
15. Pujari JD, Yakkundimath R, Byadgi AS Classification of Fungal Disease Symptoms affected on Cereals using Color Texture Features. International Journal of Signal Processing, Image Processing and Pattern Recognition; 2013, Vol. 6, No. 6, pp. 321-330.
16. Pujari JD, Yakkundimath R, Byadgi AS Neuro-kNN classification system for detecting fungal disease affected on vegetables using local binary patterns. Agricultural Engineering International; CIGR, in press.
17. Pujari JD, Yakkundimath R, Byadgi AS Recognition and Classification of Normal and Affected Agriculture Produce Using Reduced Color and Texture Features. International Journal of Computer Applications, 2014, Vol.93, n.11, 2014, pp: 17-24.
18. Pydipati R, Burks TF, Lee WS Identification of Citrus Disease Using Color Texture Features and Discriminate Analysis. Computers and Electronics in Agriculture; 2006, Vol. 52, No.1-2, pp. 49-59.
19. Rumpf T, Mahlein AK, Steiner U, Oerke E.C., Dehne HW, Plumer L. Early Detection and Classification of Plant Diseases with Support Vector Machines Based on Hyperspectral Reflectance. Computers and Electronics in Agriculture; 2010, Vol. 74, No. 1, pp. 91-99.
20. Sankaran S, Mishra A, Ehsani R., Davis C. A Review of Advanced Techniques for Detecting Plant Diseases. Computers and Electronics in Agriculture; 2010, Vol. 72, pp. 1-13.
21. Tuker CC, Chakraborty K. Quantitative Assessment of Lesion Characteristics and Disease Severity Using Digital Image Processing. Journal of Phypathology; 2008, vol.145, no.7, pp. 273-278.