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Articles

Novel Network for Medicinal Leaves Identification

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References

  • M. A. Khan, “Introduction and Importance of Medicinal plants and herbs,” National Health Portal India, 2016.
  • T. Beghin, J. S. Cope, P. Remagnino, and S. Barma, “Shape and texture based plant leaf classification,” in International Conference on Advanced Concepts for Intelligent Vision Systems, J. Blanc-Talon, D. Bone, W. Philips, D. Popescu, and P. Scheunders, Eds. Berlin, Heidelberg: Springer, 2010, pp. 345–53.
  • H. Laga, S. Kurtek, A. Srivastava, M. Golzarian, and S. J. Miklavcic, “A Riemannian elastic metric for shape-based plant leaf classification,” in Proceedings of IEEE Conference on Digital Image Computing Techniques and Applications, 2012, pp. 1–7.
  • A. Dobrescu, M. V. Giuffrida, and S. A. Tsaftaris, “Doing more with less: A multitask deep learning approach in plant phenotyping,” Front. Plant Sci., Vol. 11, p. 141, 2020.
  • H. K. Suh, J. Ijsselmuiden, J. W. Hofstee, and E. J. V. Henten, “Transfer learning for the classification of sugar beet and volunteer potato under field conditions,” Biosystems Eng., Vol. 174, pp. 50–65, 2018.
  • Z. Liu, F. Cheng, and H. Hong, “Identification of impurities in fresh shrimp using improved majority scheme-based classifier,” Food Anal. Methods, Vol. 9, no. 11, pp. 3133–42, 2016.
  • S. Sharma, S. N. Shivhare, N. Singh, and K. Kumar, “Computationally efficient ann model for small-scale problems,” in Machine Intelligence and Signal Analysis, M.Tanveer, R. Bilas Pachori, Eds., Vol. 748. Singapore: Springer, 2019, pp. 423–35.
  • S. Sharma, K. Kumar, and N. Singh, “Deep eigen space based asl recognition system,” IETE. J. Res., 1–11, 2020.
  • K. Kumar, “Text query based summarized event searching interface system using deep learning over cloud,” Multimed. Tools. Appl., Vol. 80, no. 7, pp. 11079–94, 2021.
  • I. Dabral, M. Singh, and K. Kumar, “Cancer detection using convolutional Neural network,” in International Conference on Deep Learning, Artificial Intelligence and Robotics, M. Tripathi, and S. Upadhyaya, Eds. Cham: Springer, 2019, pp. 290–8.
  • A. Negi, K. Kumar, and P. Chauhan, “Deep learning-based image classifier for malaria cell detection,” in Machine Learning for Healthcare Applications, S. Nandan Mohanty, G. Nalinipriya, O. Prakash jena, and A.Sarkar, eds. Hoboken (NJ): Wiley, 2021, pp. 187–97.
  • A. Negi, K. Kumar, and P. Chauhan, “Deep neural network-based multi-class image classification for plant diseases,” in Agricultural Informatics: Automation Using the IoT and Machine Learning, A. Choudhury, A. Biswas, M. Prateek, and A. Chakrabarti, Eds. Hoboken (NJ): Wiley, 2021, pp. 117–29.
  • A. Begue, V. Kowlessur, F. Mahomoodally, U. Singh, and S. Pudaruth, “Automatic recognition of medicinal plants using machine learning techniques,” Int. J. Adv. Comput. Sci. Appl., Vol. 8, no. 4, pp. 166–75, 2017.
  • K. Pankaja, and J. Thippeswamy, “Survey on leaf recognition and classification,” in Proceedings of IEEE Conference on Innovative Mechanisms for Industry Applications, 2017, pp. 442–9.
  • P. Chhabra, N. K. Garg, and M. Kumar, “Content-based image retrieval system using ORB and SIFT features,” Neural Comput. Appl., Vol. 32, no. 7, pp. 2725–33, 2020.
  • M. Kumar, M. K. Jindal, R. K. Sharma, and S. R. Jindal, “Performance evaluation of classifiers for the recognition of offline handwritten Gurmukhi characters and numerals: a study,” Artif. Intell. Rev., Vol. 53, no. 3, pp. 2075–97, 2020.
  • M. Bansal, M. Kumar, M. Kumar, and K. Kumar, “An efficient technique for object recognition using Shi-Tomasi corner detection algorithm,” Soft. Comput., Vol. 25, no. 6, pp. 4423–32, 2021.
  • M. Kumar, S. Gupta, X. Z. Gao, and A. Singh, “Plant species recognition using morphological features and adaptive boosting methodology,” IEEE. Access, Vol. 7, pp. 163912–18, 2019.
  • Y. Lai, “A comparison of traditional machine learning and deep learning in image recognition,” J. Phys: Conf. Ser., Vol. 1314, no. 1, p. 012148, 2019.
  • A. K. Rangarajan, R. Purushothaman, and A. Ramesh, “Tomato crop disease classification using pre-trained deep learning algorithm,” Procedia. Comput. Sci., Vol. 133, pp. 1040–7, 2018.
  • W. S. Jeon, and S. Y. Rhee, “Plant leaf recognition using a convolution neural network,” Int. J. Fuzzy Log. Intell. Syst., Vol. 17, no. 1, pp. 26–34, 2017.
  • B. P. G. Toth, M. Osváth, D. Papp, and G. Szűcs, “Deep learning for plant classification and content-based image retrieval,” Cybern. Inf. Technol., Vol. 19, no. 1, pp. 88–100, 2019.
  • J. W. Tan, S. W. Chang, S. A. Kareem, H. J. Yap, and K. T. Yong, “Deep learning for plant species classification using leaf vein morphometric,” IEEE/ACM Trans. Comput. Biol. Bioinf., Vol. 17, no. 1, pp. 82–90, 2018.
  • C. S. Pereira, R. Morais, and M. J. C. S. Reis, “Deep learning techniques for grape plant species identification in natural images,” Sensors, Vol. 19, no. 22, p. 4850, 2019.
  • M. R. Dileep, and P. N. Pournami, “Ayurleaf: A deep learning approach for classification of medicinal plants,” in Proceedings of IEEE Region 10 Conference (TENCON), 2019, pp. 321–5.
  • J. Huixian, “The analysis of plants image recognition based on deep learning and artificial neural network,” IEEE. Access, Vol. 8, pp. 68828–41, 2020.
  • S. Roopashree, and J. Anitha, “Medicinal leaf dataset,” Mendeley Data, V1, 2020. DOI: 10.17632/nnytj2v3n5.1.
  • S. Kumari, M. Singh, and K. Kumar, “Prediction of liver disease using grouping of machine learning classifiers,” in International Conference on Deep Learning, Artificial Intelligence and Robotics, M. Tripathi, and S. Upadhyaya, Eds. Cham: Springer, 2019, pp. 339–49.
  • Monika, M. Kumar, and M. Kumar, “XGBoost: 2D-object recognition using shape descriptors and extreme Gradient boosting classifier,” in Computational Methods and Data Engineering, Singapore: Springer, 2021, pp. 207–22.
  • B. Dudi, and V. Rajesh, “Medicinal plant recognition based on CNN and machine learning,” Int. J. Adv. Trends Comput. Sci. Eng., Vol. 8, no. 4, pp. 999–1003, 2019.
  • M. Kumar, P. Chhabra, and N. K. Garg, “An efficient content based image retrieval system using BayesNet and K-NN,” Multimed. Tools. Appl., Vol. 77, no. 16, pp. 21557–70, 2018.
  • D. Garg, N. K. Garg, and M. Kumar, “Underwater image enhancement using blending of CLAHE and percentile methodologies,” Multimed. Tools. Appl., Vol. 77, no. 20, pp. 26545–61, 2018.

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