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Patatel, Deven J.
- Analytical Review of Major Nocturnal Pests’ Detection Technique using Computer Vision
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1 Information Technology Cell, Junagadh Agricultural University, Junagadh, Gujarat, IN
2 Department of Computer Engineering, RK University, Kasturbadham, Rajkot, Gujarat, IN
1 Information Technology Cell, Junagadh Agricultural University, Junagadh, Gujarat, IN
2 Department of Computer Engineering, RK University, Kasturbadham, Rajkot, Gujarat, IN
Source
Oriental Journal of Computer Science and Technology, Vol 11, No 3 (2018), Pagination: 179-182Abstract
1* and Nirav rav Bhattatt 2Research in agriculture is increasing quality and quantity, but pest reduces it. To prevent the effect of these pests, insecticides are used. But excessive use of pesticides is very harmful to production and environment. So initially pest detection is necessary. We work on nocturnal pests because that can be easily attracting using night trapping tools. The purpose of this review article is to analyse the popular techniques and find the right technique for the initial diagnosis and early detection of major nocturnal flying pests like Pink Bollworm, White Grub, Helicoverpa and Spodoptera. The importance of early detection can be in identifying and classifying the pests in a digital view. We have concluded our results with the various methods and the prospects of future research.Keywords
Computer Vision, Pest Detection Techniques, Convolution Neural Networks, Deep Learning.Full Text
References
- S. Narayana, “Ontology-based Advisory System for Cotton Crop Farmers,” International Journal of Computer Applications. 167(7):1–7.
- A. Rastogi, R. Arora, and S. Sharma, “Leaf disease detection and grading using computer vision technology & fuzzy logic,” in 2nd International Conference on Signal Processing and Integrated Networks, SPIN. 2015;500–505.
- O. S. Android, “Detection of Plant Leaf Disease Using Image Processing Approach,” International Journal of Scientific and Research Publications. 2016;6(2):73–76.
- Brian Patrick, “Invertebrates: light trapping,” Department of Conservation,Government of NewZeland. 2016;1–17.
- B. Debalina and M. Manik, “Object Identification For Computer Vision using Image Segmentation,” in 2nd International Conforence on Education Technology and Computer. 2010;170–172.
- C. Caramuta, G. Collodel, C. Giacomini, C. Gruden, G. Longo, and P. Piccolotto, “Survey of detection techniques, mathematical models and simulation software in pedestrian dynamics,” Transportation Research Procedia. 2017;25:551–567.
- G. T. Shrivakshan, “Enhanced Edge Detection Techniques for Identification of fish through its Morphological Features,” International Journal of Computer Science and Mobile Computing. 2017;6(11):48–53.
- R. Maini and A. Himanshu, “Study and Comparison of Various Image Edge Detection Techniques,” International Journal of Image Processing (IJIP). 2009;3(1):1–12.
- D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision. 2004;60(2):91–110.
- H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding. 2008;110(3):346–359.
- E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proceedings of the IEEE International Conference on Computer Vision. 2011;2564–2571.
- E. Karami, S. Prasad, and M. Shehata, “Image Matching Using SIFT , SURF , BRIEF and ORB: Performance Comparison for Distorted Images Image Matching Using SIFT , SURF , BRIEF and ORB : Performance Comparison for Distorted Images,” in Newfoundland Electrical and Computer Engineering Conference. 2015;4.
- A. Sharma, P. K. Singh, and P. Khurana, “Analytical Review on Object Segmentation and Recognition,” in IEEE 6th International Conference – Cloud System and Big Data Engineering (Confluence). 2016;524–530.
- Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE. 1998;86(11):2278–2323.
- N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research. 2014;15:1929–1958.
- N. Sharma, V. Jain, and M. Anju, “An Analysis Of Convolutional Neural Networks For Image Classification,” in Procedia Computer Science. 2018;132:377–384.
- A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture. 2017;147:70–90.