Fungus/Disease Analysis in Tomato Crop using Image Processing Techniques
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2014 by IJCTT Journal|
|Year of Publication : 2014|
|Authors : Shruti , Nidhi Seth|
|DOI : 10.14445/22312803/IJCTT-V13P113|
Shruti , Nidhi Seth. "Fungus/Disease Analysis in Tomato Crop using Image Processing Techniques". International Journal of Computer Trends and Technology (IJCTT) V13(2):61-63, July 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
The crop of tomato is very often infected by a disease that leaves spots of brown, gray or off-white colors on the plant’s leafs in winter. Scientifically, this disease is known as cercospora leaf spot or cercospora cruciferarum. It’s a kind of fungus that often kills young seedlings. The fungus spreads by air and can also infect tomato plants. Therefore, it is important to monitor the leaf at regular intervals so as to keep track on quality of growing tomato crop. In the presented paper, a novel machine vision system has been proposed that visual inspects the leafs coming out of the soil and based on spots on leaves, it determines the nature of fungus and its depth into the tomato steam. The size of the fungus, color depth and location and locus of the fungus on leaves give an accurate determination of crop quality under the soil. In the presented thesis work, the image of the crop leaves are taken by a good quality color camera and processed for getting a gray colored and segmented image depending upon the nature and size of the fungus. A criterion is set for acceptable and rejects crop quality based on the fungus level.
1. R.Kalaivani1, Dr.S. Muruganand2, Dr.Azha.Periasamy, “IDENTIFYING THE QUALITY OF TOMATOES IN IMAGE PROCESSING USING MATLAB”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 8, August 2013
2. Mahdi M. Ali, Ahmed 1 1 Al-Ani, 2Derek Eamus and 3Daniel K.Y. Tan, “A New Image Processing Based Technique to Determine Chlorophyll in Plants”, American-Eurasian J. Agric. & Environ. Sci., 12 (10): 1323-1328, 2012
3. Chawaroj Jaisin 1, Siwalak Pathaveerat 2, and Anupun Terdwongworakul 3,” Determining the size and location of longans in bunches by image processing technique “,Maejo Int. J. Sci. Technol. 2013, 7(03), 444-455.
4. Hadi Izadi, Saadat Kamgar, Mohamad Hossien Raoufat,” Mass Modeeling of Tomato Based on Phisical Characteristics”, International Journal of Agronomy and Plant Production. Vol., 4 (10), 2631-2636, 2013.
5. Meenu Dadwal, V.K.Banga,” Estimate Ripeness Level of fruits Using RGB Color Space and Fuzzy Logic Technique”, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-2, Issue-1, October 2012.
6. Anup Vibhute,S K Bodhe, “ Applications of Image Processing in Agriculture”, International Journal of Computer Applications (0975 – 8887) Volume 52– No.2, August 2012.
7. Aristoteles,Ossy Dwi Endah,Dwi Susanto” Image Processing for Save Life Predictions of Tomato Fruit using RGB Method”, International Journal of Computer Applications (0975 8887)Volume 81 - No. 6, November 2013.
8. Kenneth VA Richardson,” Evaluation of Five Staked Tomato Varieties For Quality And Yield”, Gladstone Road Agricultural Centre Department of Agriculture Nassau, Bahamas May 2013
Segmentation, K-Means, Pixel Neighborhood