AUTOMATED SYSTEM FOR RECOGNITION OF CORN SEEDS INFECTED WITH FUSARIO BLOOD

Shukhrat Mannapovich Gulyamov; Ulugbek Turgudovich Mukhamedkhanov; Malika Yuldashovna Doshanova; Bekhruz Iskandar ogli Suvonov

Authors

Shukhrat Mannapovich Gulyamov Tashkent State Technical University named after Islam Karimov
Ulugbek Turgudovich Mukhamedkhanov Tashkent State Technical University named after Islam Karimov
Malika Yuldashovna Doshanova Muhammad al-Khwarizmi Tashkent University of Information Technologies
Bekhruz Iskandar ogli Suvonov University of Economics and Pedagogy

Keywords:

Recognition system, maize seeds, digital image analysis, recognition of Fusarium-infected seeds

Abstract

This paper proposes a scheme of an automated system for recognizing maize seeds infected with Fusarium and separating them from healthy ones. According to preliminary studies, it has been established that infected maize seeds can be recognized with an accuracy of up to 98% through the analysis of their color visual images as well as color and texture features. The proposed scheme is intended for the processing and analysis of visual images. It is assumed that this solution can be applied in practice under industrial conditions, which implies the creation of conditions for the simultaneous processing of a large number of seeds.

References

3. Delwiche S.R. Classification of scab- and other mold-damaged wheat kernels by Near-Infrared reflectance spectroscopy, 2003, Transactions of the ASAE Vol. 46(3): 731–738

4. Delwiche S.R., Gaines C.S., 2005, Wavelength selection for monochromatic and bichromatic sorting of Fusarium-damaged wheat, Applied Engineering in Agriculture Vol. 21(4): 681–688

5. Demeke Tigst, Randy M. Clear, Susan K. Patrik, Don Gaba, 2004, Species-specific PCR-based assays for the detection of Fusarium species and a comparison with the whole seed agar plate method and trichothecene analysis, International Journal of Food Microbiology 103 (2005) 271–284

6. Kos Gregor, Hans Lohninger, Rudolf Krska, 2002, Fourier transform mid-infrared spectroscopy with total reflection (FT-IR/ATR) as a tool for detection of Fusarium fungi on maize, Vibrational Spectroscopy, 29 (2002) 115–119

7. Ng H.F., W.F. Wilcke, R.V. Morvey, J.P. Lang, 1998, Machine vision color calibration in assessing corn kernel damage, Transactions of the ASAE Vol. 41(3): 727–732

8. Ruan R., S. Ning, L. Luo, X. Chen, P. Chen, R. Jones, W. Wilcke, V. Morey, 2001, Estimation of weight percentage of scabby wheat kernels using an automatic machine vision and neural network based system, Transactions of the ASAE Vol. 44(4): 983–988