PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A framework for chili fruits maturity estimation using deep convolutional neural network

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
PL
Szacowanie dojrzałości owoców chili przy użyciu głębokiej konwolucyjnej sieci neuronowej
Języki publikacji
EN
Abstrakty
EN
An agriculture robot has been demanded in recent years. Inaccurate in estimating the maturity of the chili always happens since the human eyes are tend to prone to errors. Serving an effective, innovative, feasible chili recognition system would help farmers as economical alternative by reducing the workloads while increasing fruit yield. Hence, a comprehensive framework of chili maturity estimation using deep learning is carried out.
PL
W ostatnich latach pojawił się popyt na robota rolniczego. Niedokładne oszacowanie dojrzałości chili zawsze się zdarza, ponieważ ludzkie oczy są podatne na błędy. Dostarczenie skutecznego, innowacyjnego i wykonalnego systemu rozpoznawania chili pomogłoby rolnikom jako ekonomiczna alternatywa, zmniejszając obciążenie pracą przy jednoczesnym zwiększeniu plonów owoców. W związku z tym przeprowadzane są kompleksowe ramy szacowania dojrzałości chili z wykorzystaniem głębokiego uczenia.
Rocznik
Strony
77--81
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
  • Centre for Telecommunication, Research and Innovation, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • Centre for Telecommunication, Research and Innovation, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • Centre for Telecommunication, Research and Innovation, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • Centre for Telecommunication, Research and Innovation, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
autor
  • Centre for Telecommunication, Research and Innovation, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • Centre for Telecommunication, Research and Innovation, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • 3MSJ Perwira Enterprise [202103095516 (SA0563088-W)], Duyung, 75460 Melaka, Malaysia
Bibliografia
  • [1] M. L. Praburaj, Role of Agriculture in the Economic Development of a Country, Int. J. Commer. (2018) vol. 6, no. 3, p. 2, doi: 10.5281/zenodo.1323056.
  • [2] Spatial features. http://www.biomedware.com/files/documentation/boundaryseer/ Projects/Spatial_feature_files.htm.
  • [3] M. Mraz, P. Findura, O. Urbanovicoba, I. rigo, P. Bajus, T. Drozdz and P. Keilbasa, Development of the web application by the information system for data processing and documentation on selected farm in agricultural production, Przeglad Elektrotechniczny, (2020), vol. 1, no. 218, pp. 218-221.
  • [4] C. Law, (PDF) Artificial Intelligence Definition, Ethics and Standards (2019), [Online]. Available: https://www.researchgate.net/publication/332548325_Artificial_ Intelligence_Definition_Ethics_and_Standards.
  • [5] R. Ashri, What Is AI? AI-Powered Work.(2020), pp. 15–29, doi: 10.1007/978-1-4842-5476-9_2.
  • [6] B. W. Wirtz, J. C. Weyerer, and C. Geyer, Artificial Intelligence and the Public Sector—Applications and Challenges, Int. J. Public Adm. (2019) vol. 42, no. 7, pp. 596–615, doi: 10.1080/01900692.2018.1498103.
  • [7] D. Alfer’ev, Artificial intelligence in agriculture, Agric. Lifestock Technol. / АгроЗооТехника (2018) vol. 7, no. 4 (4), doi: 10.15838/alt.2018.1.4.5.
  • [8] P. Dönmez, Introduction to Machine Learning, 2nd ed., by Ethem Alpaydın. Cambridge, MA: The MIT Press. ISBN: 978-0- 262-01243-0. $54/£ 39.95 + 584 pages., Nat. Lang. Eng. (2013) vol. 19, no. 2, pp. 285–288, doi: 10.1017/s1351324912000290.
  • [9] M. Mohammed, M. B. Khan, and E. B. M. Bashie, Machine learning: Algorithms and applications, (2016) no. July.
  • [10] IBM, Supervised Learning, Cloud Education. https://www.ibm.com/cloud/learn/supervised-learning (accessed May 29, 2021).
  • [11] J. Fidler Dennis and L. Arnroth, Supervised Learning Techniques:A comparison of the Random Forest and the Support Vector Machine, (2015).
  • [12] Q. Liu and Y. Wu, Encyclopedia of the Sciences of Learning, Encycl. Sci. Learn. (2012), no. January 2012, doi: 10.1007/978- 1-4419-1428-6.
  • [13] P. Dayan, Unsupervised Learning, Encycl. Neurosci. (2008), pp. 4154–4154, doi: 10.1007/978-3-540-29678-2_6202.
  • [14] Divyansh Dwivedi, Machine Learning for Beginners, data science. https://towardsdatascience.com/machine-learning-forbeginners- d247a9420dab (accessed May 30, 2021).
  • [15] IBM Education, What is Unsupervised Learning? | IBM, Ibm, (2020), no. August, pp. 1–8, doi: 10.13140/RG.2.2.33325.10720.
  • [16] Reinforcement Learning: What is, Algorithms, Applications, Example. https://www.guru99.com/reinforcement-learningtutorial. html.
  • [17] S. R. Hinton GE, Reducing the dimensionality of data with neural networks, Science, (2006). .
  • [18] H. Mureşan and M. Oltean, Fruit recognition from images using deep learning, arXiv, (2018) no. June 2018, doi: 10.2478/ausi- 2018-0002.
  • [19] S. Osowski and K. Siwek, CNN application in face recognition, Przeglad Elektrotechniczny, (2020), vol. 3, no. 142, pp. 142- 145.
  • [20] Artem Oppermann, What is Deep Learning and How does it work? Towards Data Science, (2019). https://towardsdatascience.com/what-is-deep-learning-andhow- does-it-work-2ce44bb692ac (accessed May 24, 2021).
  • [21] J. Ahmad, H. Farman, and Z. Jan, Deep Learning Methods and Applications BT - Deep Learning: Convergence to Big Data Analytics. Springer Singapore, (2019).
  • [22] JiTU7, Introduction to Supervised Deep Learning Algorithms!, Analytics Vidhya, (2021). https://www.analyticsvidhya.com/blog/2021/05/introduction-tosupervised- deep-learning-algorithms/ (accessed Jun. 16, 2021).
  • [23] IBM Education, Recurrent Neural Networks, IBM, (2020). https://www.ibm.com/cloud/learn/recurrent-neural-networks.
  • [24] L. Morissette and S. Chartier, The k-means clustering technique: General considerations and implementation in Mathematica, Tutor. Quant. Methods Psychol. (2013), vol. 9, no. 1, pp. 15–24, doi: 10.20982/tqmp.09.1.p015.
  • [25] A. Ghosh, A. Sufian, F. Sultana, A. Chakrabarti, and D. De, Fundamental concepts of convolutional neural network, (2019), vol. 172, no. January.
  • [26] Y. D. Zhang et al., Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation, Multimed. Tools Appl., (2019), vol. 78, no. 3, pp. 3613–3632, doi: 10.1007/s11042-017-5243-3.
  • [27] A. Mathew, P. Amudha, and S. Sivakumari, Deep learning techniques: an overview, Adv. Intell. Syst. Comput., (2021), vol. 1141, no. January, pp. 599–608, doi: 10.1007/978-981-15- 3383-9_54.
  • [28] Z. M. Khaing, Y. Naung, and P. H. Htut, Development of control system for fruit classification based on convolutional neural network, Proc. 2018 IEEE Conf. Russ. Young Res. Electr. Electron. Eng. ElConRus (2018), vol. 2018-Janua, pp. 1805– 1807, doi: 10.1109/EIConRus.2018.8317456.
  • [29] S. Albelwi and A. Mahmood, A framework for designing the architectures of deep Convolutional Neural Networks, Entropy, (2017), vol. 19, no. 6, doi: 10.3390/e19060242.
  • [30] S. R. Dubey and A. S. Jalal, Fruit and vegetable recognition by fusing colour and texture features of the image using machine learning, Int. J. Appl. Pattern Recognit.,(2015), vol. 2, no. 2, p. 160, doi: 10.1504/ijapr.2015.069538.
  • [31] T. Ishikawa et al., Classification of strawberry fruit shape by machine learning, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., (2018), vol. 42, no. 2, pp. 463–470, doi: 10.5194/isprs-archives-XLII-2-463-2018.
  • [32] M. Ataş, Y. Yardimci, and A. Temizel, A new approach to aflatoxin detection in chili pepper by machine vision, Comput. Electron. Agric., (2012), vol. 87, pp. 129–141, doi: 10.1016/j.compag.2012.06.001.
  • [33] O. Cruz-Domínguez et al., A novel method for dried chili pepper classification using artificial intelligence, J. Agric. Food Res., (2021), vol. 3, no. October 2020, p. 100099, doi: 10.1016/j.jafr.2021.100099.
  • [34] M. Kaur and R. Sharma, Quality Detection of Fruits by Using ANN Technique, IOSR J. Electron. Commun. Eng. Ver. II, (2015) vol. 10, no. 4, pp. 2278–2834, doi: 10.9790/2834- 10423541.
  • [35] A. Taofik, N. Ismail, Y. A. Gerhana, K. Komarujaman, and M. A. Ramdhani, Design of Smart System to Detect Ripeness of Tomato and Chili with New Approach in Data Acquisition, IOP Conf. Ser. Mater. Sci. Eng., (2018), vol. 288, no. 1, pp. 0–6, doi: 10.1088/1757-899X/288/1/012018.
  • [36] S. Deepika, FRUIT MATURITY AND DISEASE DETECTION USING ARTIFICIAL NEURAL NETWORK, no. 09, pp. 144– 151, (2020).
  • [37] N. Fadilah, J. M. Saleh, H. Ibrahim, and Z. A. Halim, Oil palm fresh fruit bunch ripeness classification using artificial neural network, ICIAS 2012 - 2012 4th Int. Conf. Intell. Adv. Syst. A Conf. World Eng. Sci. Technol. Congr. - Conf. Proc., vol. 1, pp. 18–21, (2012), doi: 10.1109/ICIAS.2012.6306151.
  • [38] P. Nandhini and J. Jaya, Image Segmentation for Food Quality Evaluation Using Computer Vision System, (2014), vol. 4, no. 2, pp. 1–3.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-de7f3d33-f5c1-4aa9-bb08-9f0b680b9610
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.