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Nandini, S.
- Detection of Pink Bollworm, Pectinophora gossypiella, Saunders Infestation Using Soft X-Ray Machine
Abstract Views :257 |
PDF Views:0
Authors
S. Nandini
1,
S. Mohan
2
Affiliations
1 Fertilizer Control Laboratory, TIRUVARUR (T.N.), IN
2 School of Post Graduate Studies, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
1 Fertilizer Control Laboratory, TIRUVARUR (T.N.), IN
2 School of Post Graduate Studies, Tamil Nadu Agricultural University, Coimbatore (T.N.), IN
Source
International Journal of Plant Protection, Vol 8, No 2 (2015), Pagination: 256-260Abstract
An experiment was carried on standardization of X-ray radiography methodology for the detection of pink bollworm infestation in cotton bolls during 2012-14 at Indian Institute of Crop Processing Technology, Thanjavur, Tamil Nadu, India. Studies revealed that the controllable input electrical parameters of the X-ray machine viz., voltage, current and exposure period required for the detection of internal infestation varied widely for cotton bolls compared to stored grains and fruits tested by other scientists. High voltage and current were required for dense cotton bolls to ensure adequate penetration of radiation. It was observed on visual analysis that the X-ray radiation generated at 80 kV and 10 mA for 30 seconds resulted in the best visual images to view internal content of cotton bolls and observed to be the best for cotton bolls imagery out of 96 combinations tested for best detection of hidden infestation. While other combinations, for example, 60Kv, 4mA for 10 seconds and 90 Kv, 10mA for 30 seconds manifested into lighter and darker images, respectively.Keywords
Pink Bollworm, Infestation, X-Ray, Non Destructing Sampling.
References
- Agarwal, R.A., Gupta, G.P. and Grag, D.O. (1984). Cotton pest management in India. Research Publication, Azad Nagar, Delhi, pp. 1-191.
- Agarwal, R.A. (2013). All India Co-ordinated Cotton Improvement Project – Annual Report (2012-13).
- Fenton, F.A. and Waite, W.W. (1932). Detecting pink bollworms in cottonseeds by the X-ray. J. Agric. Res., 45(6) : 347-348.
- Gutierrez, A.P., Sergine and Ponsard (2006). Physiologically based demographics of Bt cotton–pest interactions I. Pink bollworm resistance, refuge and risk. Ecological Modelling, 191 : 346–359.
- Haff, R.F. and Slaughter, D.C. (1999). X-ray inspection of wheat for granary weevils. ASAE Paper No. 99-3060. St. Joseph, MI: ASAE.
- Karunakaran, C., Jayas, D.S. and White, N.D.G. (2000). Detection of insect infestations in wheat kernels using soft X-rays. CSAE/SCGR Paper No. AFL122. Winnipeg, MB: CSAE/SCGR.
- Karunakaran, C., Jayas, D.S. and White, N.D.G. (2003a). Soft X-ray inspection of wheat kernels infested by Sitophilus oryzae. Trans. ASAE, 46(3):739:745.
- Karunakaran, C., Jayas, D.S. and White, N.D.G. (2003b). XRay image analysis to detect infestations caused by insects in grain. Cereal Chem., 80(5):553-557.
- Keagy, P.M. and Schatzki, T.F. (1991). Effect of image resolution on insect detection in wheat radiographs. Cereal Chem., 68(4): 339-343.
- Keagy, P.M. and Schatzki, T.F. (1993). Machine recognition of weevil damage in wheat radiographs. Cereal Chem., 70(6): 696-700.
- Milner, M., Lee, M.R. and Katz, R. (1950). Application of Xray technique to the detection of internal insect infestation of grain. J. Econ. Entomol., 43(6):933-935.
- Ramakrishnan, N., Sarath Babu, B. and Ramesh Babu, T. (2011). Standardization of X-Ray Radiography Methodology for the Detection of Hidden Infestation in Cereals. Indian J. Plant Protec., 39 (4) : 249-257.
- Sarath Babu, B. (1997). Detection of insect pest of quarantine significance in screening of germplasm under exchange programme during 1989-1995 in India. J. Entomol Res., 21: 295-297.
- Schatzki, T.F. and Fine, T.A. (1988). Analysis of radiograms of wheat kernels for quality control. Cereal Chem., 65(3):233-239.
- Shahin, M. A., Tollner, E.N., Mcclendon, R.W. and Arabnia, H.R. (2002). Apple classification based on surface bruises using image processing and neural networks. Trans. ASAE, 45 : 1619-1627.
- Thomas, P., Kannan, A., Degwekar, V.H. and Ramamurthy, M.S. (1995). Non-destructive detection of seed weevilinfested mango fruits by X-ray imaging. Postharvest Biol. & Technol., 5 (1-2) : 161-165.
- Protein Quality and Acceptability of Care's Kerala Indigenous Food
Abstract Views :184 |
Authors
Affiliations
1 Sri Avinashilingam Home Science College for Women, Coimbatore-641011, IN
1 Sri Avinashilingam Home Science College for Women, Coimbatore-641011, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 13, No 1 (1976), Pagination: 1-6Abstract
The major problem confronting preschool children in developing countries like India is a 'food - gap' rather than a 'protein gap'. This food gap can be filled through a judicious mixture of indigenous foods in quantities which could satisfy the calorie needs of children. It is encouraging to note that the Government of Kerala in collaboration with CARE has taken a right step towards self-sufficiency, through the production of Kerala Indigenous Food (KIP) based on tapioca.- Feature Match:A General ANNF Estimation Technique and Its Applications
Abstract Views :229 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, K. S. Institute of Technology, Bangalore, Karnataka, IN
1 Department of Computer Science and Engineering, K. S. Institute of Technology, Bangalore, Karnataka, IN
Source
International Journal of Innovative Research and Development, Vol 5, No 6 (2016), Pagination: 24-27Abstract
This paper proposes a summed up Approximate Nearest Neighbor Field(ANNF) calculation structure between a source and the objective picture. The calculation can appraise ANNF maps between any sets of pictures. The speculation is accomplished through the fitting spatial extent changes. The pair of pictures is approximated utilizing low-dimensional components. This ANNF guide can be further enhanced in view of the picture coherency and spatial changes. The ANNF outline work represents two applications, for example, i) Optic plate location and ii) Super determination. Optic circle recognition manages the restorative imaging where that finds the optic plates in retinal picture utilizing a sound optic circle picture as regular target picture and the second application manages super determination of engineered pictures utilizing a typical source picture as word reference.
Keywords
Approximate Nearest Neighbour Field, Patch Based Image Synthesis, Patch Match, Optic Disk Detection, Super Resolution.Full Text