Abstract
A digital-based automated method for identifying plants is presented in this research. The leaf is chosen to gain the characteristics of the plant out of all the available plant parts. Digital image processing methods are used to determine five geometrical characteristics. Six fundamental morphological traits are derived based on these geometrical factors. Leaf structure is used to extract the vein feature, which is a derived feature. Digital scanners are used to capture leaf photos at the first stage. The retrieved morphological traits are then used as input in the classification stage, which follows. The suggested algorithm's recognition accuracy is evaluated. This algorithm's accuracy has been evaluated against two separate databases and compared. For both databases, the false acceptance ratio and false rejection ratio are computed. This method is used to classify 12 different types of plants. Dataset has 92 photos of 12 different plants. Because it is independent of leaf age, this technique employs an efficient algorithm utilized for plant identification and categorization. The suggested approach is quick and simple to use.
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References
Mzoughi O, Yahiaoui I, Boujemaa N, Zagrouba E (2013) Advanced tree species identification using multiple leaf parts image queries. IEEE ICIP
Chollet, Franc¸ois (2015). https://blog.keras.io/building-powerful-image-classification- models-using-very-little-data. html
Bhardwaj A, Kaur M (2013) A review on plant recognition and classification techniques using leaf images. Int J Eng Trends Technol 4(2)
Wang Z, Chi Z, Feng D, Wang Q (2000) Leaf image retrieval with shape features. In: 4th international conference on advances in visual information systems, vol 3, pp 477–487
Beghin T, Cope JS, Remagnino P, Barman S (2010) Shape and texture based plant leaf classification. In: International conference on advanced concepts for intelligent vision systems (ACVIS), vol 3, pp 345–353
Wang B, Brown D, Gao Y, Salle JL (2013) Mobile plant leaf identification using smart- phones. IEEE ICIP
Wu SG, Bao FS, Xu EY, Wang YX, Chang YF, Xiang QL (2007) A leaf recognition algorithm for plant classification using probabilistic neural network. IEEE ISSPIT, pp 11– 16
Chaki J, Parekh R (2012) Plant leaf recognition using Gabor filter. Int J Comput Appl 56(10)
Timmermans AJM, Hulzebosch AA (1996) Computer vison system for on-line sorting of pot plants using an artificial neural network classifier. Comput Electron Agric 1:41–55
Fu H, Chi Z, Feng D, Song J (2004) Machine learning techniques for ontology-based leaf classification. In: IEEE 2004 8th international conference on control, automation, robotics and vision, Kunming, China, vol 2, pp 201–348
Smith TS (2006) MATLAB: advanced GUI development. Dog Ear Publishing 4010 W
Higham DJ, Higham NJ (2005) MATLAB guide, 2nd ed. Library of Congress Cataloging in Publication Data, Datta, Biswanath
Kattan P (2009) MATLAB for beginners: a gentle approach: Revised edition. Cre ate Space Independent Publishing Platform
Singh S (2005) Pattern recognition and image analysis: third international conference on advances in pattern recognition. Scientific Publishing Services, Chennai, India
Guzman JD, Peralta EK (2008) Classification of Philippine rice grains using machine vision and artificial neural networks. In: IAALD AFITA WCCA 2008 World conference on agricultural information and IT, vol 2, pp 241–481
Marques O (2011) Practical image and video processing using MATLAB. Kluwer Academic Publishers. (John W. & Sons)
Miranda JL, Gerardo BD, Tanguilig BT III (2014) Pest detection and extraction using image processing techniques. Int J Comput Commun Eng 3(3):189–192
Lukas R, Capungay KN (2006) Color image processing: methods and applications. Narsosa Publishing House, Bombay, India
Poon TC, Banerjee PP (2001) Contemporary optical image processing with MATLAB. Elsevier`s Optical, Electronic and Control Publishing
Karpathy A et al. http://cs231n.github.io/transfer-learning/
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. 1512.03385
Parekh JC (2012) Plant leaf recognition using Gabor filter. Int J Comput Appl 56(10):89–96
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556
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Manimaran, M., Shalini, M.A., Rajkumar, R., Santhiya, K.T., Ranjitha, G., Saran, C. (2024). Leaf Image-Based Plant Identification Using Morphological Feature Extraction. In: Agrawal, J., Shukla, R.K., Sharma, S., Shieh, CS. (eds) Data Engineering and Applications. IDEA 2022. Lecture Notes in Electrical Engineering, vol 1189. Springer, Singapore. https://doi.org/10.1007/978-981-97-2451-2_13
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DOI: https://doi.org/10.1007/978-981-97-2451-2_13
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