Research Article
BibTex RIS Cite
Year 2020, , 81 - 87, 31.01.2020
https://doi.org/10.17694/bajece.651286

Abstract

References

  • Kalyoncu, C., Önsen, T.: ‘Geometric leaf classification’, Computer Vision and Image Understanding, 2015, 133, (0), pp. 102–109
  • Shao, M., Du, J., Wang, J., Zhai, C.: ‘Recognition of leaf image set based on manifold-manifold distance’, In: Intelligent Computing Theory - 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014, Proceedings, pp. 332–337
  • Ruberto, C.D., Putzu, L.: ‘A fast leaf recognition algorithm based on SVM classifier and high dimensional feature vector’, In: VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications, Volume 1, Lisbon, Portugal, 5-8 January, 2014, pp. 601–609
  • Du, J., Zhai, C., Wang, Q.: ‘Recognition of plant leaf image based on fractal dimension features’, Neurocomputing ,2013,116, pp. 150–156
  • Miao, Z., Gandelin, M.-, Yuan, B.: ‘An oopr-based rose variety recognition system’, Eng. Appl. of AI , 2006, 19, (1), pp 79–101
  • Gu, X., Du, J.-X., Wang, X.-F.: ‘Leaf recognition based on the combination of wavelet transform and gaussian interpolation’, In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) Advances in Intelligent Computing. Lecture Notes in Computer Science, Springer, 2005, vol. 3644, pp 253–262
  • Wang, X., Du, J., Zhang, G.: ‘Recognition of leaf images based on shape features using a hypersphere classifier’, In: Advances in Intelligent Computing, International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I, pp 87–96
  • S., D.S., H., S., C., A., C., B.H.: ‘\: effects on development and environment’, AMBIO 31, 2002, pp 491–493
  • Tabuti, J.R.S., Dhillion, S.S., Lye, K.A.: ‘Traditional medicine in bulamogi county, uganda: its practitioners, users and viability’, Journal of Ethnopharmacology, 85, (1), 2003, pp 119–129
  • Sadraei, H., Ghannadi, A., Malekshahi, K.: ‘Relaxant effect of essential oil of melissa officinalis and citral on rat ileum contractions’, Fitoterapia, 2003, 74, (5), pp 445–452
  • Mucciarelli, M., Camusso, W., Bertea, C.M., Bossi, S., Maffei, M.: ‘Effect of (+)-pulegone and other oil components of menthapiperita on cucumber respiration’, Phytochemistry, 2001, 57, (1), pp 91–98
  • Baydar, H., Sağdiç, O., Özkan, G., Karadoğan, T.: ‘Antibacterial activity and composition of essential oils fromoriganum, thymbra and satureja species with commercial importance in Turkey’, Food Control, 2004, 15, (3), pp 169–172
  • Centritto, M., Loreto, F., Massacci, A., Pietrini, F., Villani, M.C., Zacchini, M.: ‘Improved growth and water use efficiency of cherry saplings under reduced light intensity’, Ecological Research, 2000, 15, (4), pp. 385–392
  • ‘The CLEF 2011 plant images classification task’, http://ceur-ws.org/Vol-1177/CLEF2011wn-ImageCLEF-GoeauEt2011a.pdf , accessed 25 June 2015
  • ‘The imageclef 2012 plant identification task’, http://ceur-ws.org/Vol-1178/CLEF2012wn-ImageCLEF-GoeauEt2012.pdf , 25 June 2015ww
  • Yanikoglu B., Aptoula, E., Tirkaz C.: Automatic plant identification from photographs Machine Vision and Applications, 2014, 25, pp.1369–1383
  • Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y., Chang, Y., Xiang, Q.: ‘A leaf recognition algorithm for plant classification using probabilistic neural network’, CoRR abs/0707.4289, 2007
  • Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y., Chang, Y., Xiang, Q.: ‘A leaf recognition algorithm for plant classification using probabilistic neural network’, CoRR abs/0707.4289, 2007
  • Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.B.: ‘Leafsnap: A computer vision system for automatic plant species identification’, In: Computer Vision - ECCV 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II, pp. 502–516 Söderkvist, O.J.O.: ‘Computer vision classification of leaves from swedish trees’. Master’s thesis, Linköping University, SE-581 83 Linköping, Sweden, LiTH-ISY-EX-3132, September 2011
  • Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: ‘Neural network application on foliage plant identification’, CoRR abs/1311.5829 ,2013
  • Ghazi, M.M., Yanikoglu, B., Aptoula, E.:’Plant identification using deep neural networks via optimization of transfer learning parameters’, Neurocomputing, 2017, 235, pp. 228-235
  • Odabas, M.S., Senyer, N., Kayhan, G., Ergun, E.: ‘Estimation of chlorophyll concentration index at leaves using artificial neural networks’, Journal of Circuits, Systems, and Computers , 2015
  • Manning, C.D., Raghavan, P., Schu¨tze, H.: ‘Introduction to Information Retrieval’, Cambridge University Press, New York, NY, USA , 2008
  • Breiman, L., Friedman, J.H., Olshen, R., Stone, A.C.G., 1984. Classification and regression trees. Wadsworth International Group, Belmont, California, USA.
  • Loh, W.-Y.: ‘Classification and regression trees’, Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery, 2011, 1, (1), pp. 14–23
  • Altman, N.S.: ‘An introduction to kernel and nearest-neighbour nonparametric regression’,1992, 46, (3), pp. 175–185
  • Specht, D.F.: ‘Probabilistic neural networks’, Neural Netw. 1990, 3, (1), pp. 109–118
  • Stehman, S.V.: ‘Selecting and Interpreting Measures of Thematic Classification Accuracy’, Remote Sensing of Environment ,1997, 62, (1), pp. 77–89
  • BS ISO 5725-1: ‘Accuracy (trueness and precision) of measurement methods and results - part 1: General principles and definitions’, 1994
  • Metz, C.E.: ‘Basic principles of {ROC} analysis’, Seminars in Nuclear Medicine,1978, 8, (4), pp 283–298
  • Du, J.-X., Wang, X.-F., Zhang, G.-J.: ‘Leaf shape based plant species recognition’, Appl. Math. Comput, 2007,185, (2), pp 883–893

Medicinal and Aromatic Plants Identification Using Machine Learning Methods

Year 2020, , 81 - 87, 31.01.2020
https://doi.org/10.17694/bajece.651286

Abstract

In this study,
different machine learning (ML) methods were used to classify medicinal and
aromatic plants (MAP) namely St. John’s wort (Hypericum perforatum L.), Melissa (Melissa officinalis L.), Echinacea (Echinacea purpurea L.), Thyme (Thymus
sp.) and Mint (Mentha angustifolia
L.)  based on leaf shape, gray and
fractal features. Naive Bayes Classifier (NBC), Classification and Regression
Tree (CART), K-Nearest Neighbor (KNN), and Probabilistic Neural Network (PNN)
classification were used as methods. The results indicated that plant species
were successfully recognized the average of correct classification rate. The
best classification rate on the NBC was taken: training data for classification
rate 98.39% and test data classification rate for 98.00% are obtained. ML could
be accurate tools for MAP classification tasks.

References

  • Kalyoncu, C., Önsen, T.: ‘Geometric leaf classification’, Computer Vision and Image Understanding, 2015, 133, (0), pp. 102–109
  • Shao, M., Du, J., Wang, J., Zhai, C.: ‘Recognition of leaf image set based on manifold-manifold distance’, In: Intelligent Computing Theory - 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014, Proceedings, pp. 332–337
  • Ruberto, C.D., Putzu, L.: ‘A fast leaf recognition algorithm based on SVM classifier and high dimensional feature vector’, In: VISAPP 2014 - Proceedings of the 9th International Conference on Computer Vision Theory and Applications, Volume 1, Lisbon, Portugal, 5-8 January, 2014, pp. 601–609
  • Du, J., Zhai, C., Wang, Q.: ‘Recognition of plant leaf image based on fractal dimension features’, Neurocomputing ,2013,116, pp. 150–156
  • Miao, Z., Gandelin, M.-, Yuan, B.: ‘An oopr-based rose variety recognition system’, Eng. Appl. of AI , 2006, 19, (1), pp 79–101
  • Gu, X., Du, J.-X., Wang, X.-F.: ‘Leaf recognition based on the combination of wavelet transform and gaussian interpolation’, In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) Advances in Intelligent Computing. Lecture Notes in Computer Science, Springer, 2005, vol. 3644, pp 253–262
  • Wang, X., Du, J., Zhang, G.: ‘Recognition of leaf images based on shape features using a hypersphere classifier’, In: Advances in Intelligent Computing, International Conference on Intelligent Computing, ICIC 2005, Hefei, China, August 23-26, 2005, Proceedings, Part I, pp 87–96
  • S., D.S., H., S., C., A., C., B.H.: ‘\: effects on development and environment’, AMBIO 31, 2002, pp 491–493
  • Tabuti, J.R.S., Dhillion, S.S., Lye, K.A.: ‘Traditional medicine in bulamogi county, uganda: its practitioners, users and viability’, Journal of Ethnopharmacology, 85, (1), 2003, pp 119–129
  • Sadraei, H., Ghannadi, A., Malekshahi, K.: ‘Relaxant effect of essential oil of melissa officinalis and citral on rat ileum contractions’, Fitoterapia, 2003, 74, (5), pp 445–452
  • Mucciarelli, M., Camusso, W., Bertea, C.M., Bossi, S., Maffei, M.: ‘Effect of (+)-pulegone and other oil components of menthapiperita on cucumber respiration’, Phytochemistry, 2001, 57, (1), pp 91–98
  • Baydar, H., Sağdiç, O., Özkan, G., Karadoğan, T.: ‘Antibacterial activity and composition of essential oils fromoriganum, thymbra and satureja species with commercial importance in Turkey’, Food Control, 2004, 15, (3), pp 169–172
  • Centritto, M., Loreto, F., Massacci, A., Pietrini, F., Villani, M.C., Zacchini, M.: ‘Improved growth and water use efficiency of cherry saplings under reduced light intensity’, Ecological Research, 2000, 15, (4), pp. 385–392
  • ‘The CLEF 2011 plant images classification task’, http://ceur-ws.org/Vol-1177/CLEF2011wn-ImageCLEF-GoeauEt2011a.pdf , accessed 25 June 2015
  • ‘The imageclef 2012 plant identification task’, http://ceur-ws.org/Vol-1178/CLEF2012wn-ImageCLEF-GoeauEt2012.pdf , 25 June 2015ww
  • Yanikoglu B., Aptoula, E., Tirkaz C.: Automatic plant identification from photographs Machine Vision and Applications, 2014, 25, pp.1369–1383
  • Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y., Chang, Y., Xiang, Q.: ‘A leaf recognition algorithm for plant classification using probabilistic neural network’, CoRR abs/0707.4289, 2007
  • Wu, S.G., Bao, F.S., Xu, E.Y., Wang, Y., Chang, Y., Xiang, Q.: ‘A leaf recognition algorithm for plant classification using probabilistic neural network’, CoRR abs/0707.4289, 2007
  • Kumar, N., Belhumeur, P.N., Biswas, A., Jacobs, D.W., Kress, W.J., Lopez, I.C., Soares, J.V.B.: ‘Leafsnap: A computer vision system for automatic plant species identification’, In: Computer Vision - ECCV 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II, pp. 502–516 Söderkvist, O.J.O.: ‘Computer vision classification of leaves from swedish trees’. Master’s thesis, Linköping University, SE-581 83 Linköping, Sweden, LiTH-ISY-EX-3132, September 2011
  • Kadir, A., Nugroho, L.E., Susanto, A., Santosa, P.I.: ‘Neural network application on foliage plant identification’, CoRR abs/1311.5829 ,2013
  • Ghazi, M.M., Yanikoglu, B., Aptoula, E.:’Plant identification using deep neural networks via optimization of transfer learning parameters’, Neurocomputing, 2017, 235, pp. 228-235
  • Odabas, M.S., Senyer, N., Kayhan, G., Ergun, E.: ‘Estimation of chlorophyll concentration index at leaves using artificial neural networks’, Journal of Circuits, Systems, and Computers , 2015
  • Manning, C.D., Raghavan, P., Schu¨tze, H.: ‘Introduction to Information Retrieval’, Cambridge University Press, New York, NY, USA , 2008
  • Breiman, L., Friedman, J.H., Olshen, R., Stone, A.C.G., 1984. Classification and regression trees. Wadsworth International Group, Belmont, California, USA.
  • Loh, W.-Y.: ‘Classification and regression trees’, Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery, 2011, 1, (1), pp. 14–23
  • Altman, N.S.: ‘An introduction to kernel and nearest-neighbour nonparametric regression’,1992, 46, (3), pp. 175–185
  • Specht, D.F.: ‘Probabilistic neural networks’, Neural Netw. 1990, 3, (1), pp. 109–118
  • Stehman, S.V.: ‘Selecting and Interpreting Measures of Thematic Classification Accuracy’, Remote Sensing of Environment ,1997, 62, (1), pp. 77–89
  • BS ISO 5725-1: ‘Accuracy (trueness and precision) of measurement methods and results - part 1: General principles and definitions’, 1994
  • Metz, C.E.: ‘Basic principles of {ROC} analysis’, Seminars in Nuclear Medicine,1978, 8, (4), pp 283–298
  • Du, J.-X., Wang, X.-F., Zhang, G.-J.: ‘Leaf shape based plant species recognition’, Appl. Math. Comput, 2007,185, (2), pp 883–893
There are 31 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence, Software Testing, Verification and Validation
Journal Section Araştırma Articlessi
Authors

Gökhan Kayhan 0000-0003-3391-0097

Erhan Ergün 0000-0003-1446-2428

Publication Date January 31, 2020
Published in Issue Year 2020

Cite

APA Kayhan, G., & Ergün, E. (2020). Medicinal and Aromatic Plants Identification Using Machine Learning Methods. Balkan Journal of Electrical and Computer Engineering, 8(1), 81-87. https://doi.org/10.17694/bajece.651286

All articles published by BAJECE are licensed under the Creative Commons Attribution 4.0 International License. This permits anyone to copy, redistribute, remix, transmit and adapt the work provided the original work and source is appropriately cited.Creative Commons Lisansı