Image based identification of millets using fusion of Hu moments and LBP
*Swati ShilaskarCorresponding authorswati.shilaskar@vit.eduDepartment of Electronics & Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Shripad Bhatlawandeshripad.bhatlawande@vit.eduDepartment of Electronics & Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Prapti Duddalwarprapti.duddalwar21@vit.eduDepartment of Electronics & Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Sakshi Jaiswalsakshi.jaiswal21@vit.eduDepartment of Electronics & Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Dipali Kadamdipali.kadam21@vit.eduDepartment of Electronics & Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile → , Kaustubh Chavanmahendrakumar.kaustubh21@vit.eduDepartment of Electronics & Telecommunication EngineeringVishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 08 Jun 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1623
- Pages:
- 1005–1015
Abstract
Keywords
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References
[1] Wei, W. U., et al. ‘Detection and Enumeration of Wheat Grains Based on a Deep Learning Method under Various Scenarios and Scales’. Journal of Integrative Agriculture, vol. 19, no. 8, pp. 1998–2008 (2020).
[2] Nayak, S. R., Arora, V., Sinha, U., & Poonia, R. C. A statistical analysis of COVID-19 using Gaussian and probabilistic model. Journal of Interdisciplinary Mathematics, 24(1), 19–32 (2021).
[3] Rao, A. Shubha, and K. Mahantesh. ‘Impact of Computer Vision Based Secure Image Enrichment Techniques on Image Classification Model’. Journal of Discrete Mathematical Sciences & Cryptography, vol. 26, no. 3, Taru Publications, pp. 899–911 (2023).
[4] Wasson, Vikas, and Birampal Kaur. ‘Image Quality Assessment: Intelligent Feature Selection Using Soft Computing Techniques’. Journal of Information & Optimization Sciences, vol. 44, no. 6, Taru Publications, pp. 1009–1022 (2023).
[5] Jain, S., Kumar, S., Sharma, V. K., & Poonia, R. C. Peregrine preying pattern based differential evolution for robot path planning. Journal of Interdisciplinary Mathematics, 23(2), 555–562 (2020).
[6] Altuntas, Yahya, et al. ‘Identification of Haploid Maize Seeds Using Gray Level Co-Occurrence Matrix and Machine Learning Techniques’. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), IEEE (2018).
[7] Koklu, Murat, and Ilker Ali Ozkan. ‘Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques’. Computers and Electronics in Agriculture, vol. 174, no. 105507, Elsevier BV, p. 105507 July (2020).
[8] Koklu, Murat, Ilkay Cinar, et al. ‘Classification of Rice Varieties with Deep Learning Methods’. Computers and Electronics in Agriculture, vol. 187, no. 106285, Elsevier BV, p. 106285 Aug. (2021).
[9] Laabassi, Karim, et al. ‘Wheat Varieties Identification Based on a Deep Learning Approach’. Journal of the Saudi Society of Agricultural Sciences, vol. 20, no. 5, Elsevier BV, pp. 281–289 July (2021).
[10] Arora, Biren, et al. ‘Rice Grain Classification Using Image Processing & Machine Learning Techniques’. 2020 International Conference on Inventive Computation Technologies (ICICT), IEEE (2020).
[11] Koklu, Murat, Seyma Sarigil, et al. ‘The Use of Machine Learning Methods in Classification of Pumpkin Seeds (Cucurbita Pepo L.)’. Genetic Resources and Crop Evolution, vol. 68, no. 7, Springer Science and Business Media LLC, pp. 2713–2726, Oct. (2021).
[12] Lingwal, Surabhi, et al. ‘Image-Based Wheat Grain Classification Using Convolutional Neural Network’. Multimedia Tools and Applications, vol. 80, no. 28–29, Springer Science and Business Media LLC, pp. 35441–35465, Nov. (2021).
[13] Lin, Ping, et al. ‘Determination of the Varieties of Rice Kernels Based on Machine Vision and Deep Learning Technology’. 2017 10th International Symposium on Computational Intelligence and Design (ISCID), IEEE (2017).
[14] Poonia, Ramesh C., et al., eds. Smart Farming Technologies for Sustainable Agricultural Development. IGI Global (2018).
[15] Kozłowski, Michał, et al. ‘Varietal Classification of Barley by Convolutional Neural Networks’. Biosystems Engineering, vol. 184, Elsevier BV, pp. 155–165, Aug. (2019).
[16] Olgun, Murat, et al. ‘Wheat Grain Classification by Using Dense SIFT Features with SVM Classifier’. Computers and Electronics in Agriculture, vol. 122, Elsevier BV, pp. 185–190, Mar. (2016).
[17] Zapotoczny, Piotr. ‘Discrimination of Wheat Grain Varieties Using Image Analysis and Neural Networks. Part I. Single Kernel Texture’. Journal of Cereal Science, vol. 54, no. 1, Elsevier BV, pp. 60–68, July (2011).
[18] Kurtulmuş, F., and H. Ünal. ‘Discriminating Rapeseed Varieties Using Computer Vision and Machine Learning’. Expert Systems with Applications, vol. 42, no. 4, Elsevier BV, pp. 1880–1891, Mar. (2015).
[19] Wang, Aichen, Wen Zhang, and Xinhua Wei. “A review on weed detection using ground-based machine vision and image processing techniques.” Computers and electronics in agriculture 158 : 226-240 (2019).
[20] Zareiforoush, Hemad, et al. ‘Qualitative Classification of Milled Rice Grains Using Computer Vision and Metaheuristic Techniques’. Journal of Food Science and Technology, vol. 53, no. 1, Springer Science and Business Media LLC, pp. 118–131, Jan. (2016).
[21] Ropelewska, Ewa. ‘The Use of Seed Texture Features for Discriminating Different Cultivars of Stored Apples’. Journal of Stored Products Research, vol. 88, no. 101668, Elsevier BV, p. 101668, Sept. (2020).
