Top

Published in:

2024 | OriginalPaper | Chapter

Classification of Rice Varieties Using a Deep Neural Network Model

Author : Nuran Peker

Published in: Advances in Intelligent Manufacturing and Service System Informatics

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The chapter delves into the application of deep neural networks for classifying rice varieties, a significant crop in global agriculture. Unlike previous studies that primarily use convolutional neural networks with rice images, this research employs a deep neural network model to classify numerical data derived from rice grain morphology. The study utilizes the Rice dataset, which contains morphological features extracted from rice grain images. The proposed model, named 3H2D, consists of three hidden layers and two dropout layers to mitigate overfitting. The model is trained and evaluated on the Rice dataset, achieving a high classification accuracy of 94.09%. The chapter also compares the performance of the 3H2D model with ten other machine learning algorithms, demonstrating its superiority in accuracy, precision, recall, and F1-score. This research contributes to the field of agricultural classification by showcasing the effectiveness of deep neural networks in handling complex datasets, offering a promising approach for future studies in precision agriculture.

AI Generated

This summary of the content was generated with the help of AI.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter EFQM Based Supplier Selection

next chapter Elevation Based Outdoor Navigation with Coordinated Heterogeneous Robot Team

Moazzam, S.I., et al.: A review of application of deep learning for weeds and crops classification in agriculture. In: 2019 International Conference on Robotics and Automation in Industry (ICRAI), pp. 1–6. IEEE (2019)

Pallagani, V., et al.: DCrop: a deep-learning-based framework for accurate prediction of diseases of crops in smart agriculture. In: 2019 IEEE International Symposium on Smart Electronic Systems (ISES), pp. 29–33 (2019)

Guillén-Navarro, M.A., et al.: A deep learning model to predict lower temperatures in agriculture. J. Ambient Intell. Smart Environ. 12(1), 21–34 (2020)CrossRef

Darwin, B., et al.: Recognition of bloom/yield in crop images using deep learning models for smart agriculture: a review. Agronomy 11(4), 646 (2021)CrossRef

Kelly, S., Tolvanen, J.P.: Domain-Specific Modeling: Enabling Full Code Generation. Wiley IEEE Computer Society Press, Hoboken (2008)

Jiang, T., Liu, X., Wu, L.: Method for mapping rice fields in complex landscape areas based on pre-trained convolutional neural network from HJ-1 A/B data. ISPRS Int. J. Geo Inf. 7(11), 418 (2018)CrossRef

Zhou, C., et al.: Automated counting of rice panicle by applying deep learning model to images from unmanned aerial vehicle platform. Sensors 19(14), 3106 (2019)CrossRef

Chu, Z., Yu, J.: An end-to-end model for rice yield prediction using deep learning fusion. Comput. Electron. Agric. 174, 105471 (2020)CrossRef

Park, S., et al.: i6mA DNC: prediction of DNA N6-Methyladenosine sites in rice genome based on dinucleotide representation using deep learning. Chemom. Intell. Lab. Syst. 204, 104102 (2020)CrossRef

10.

Emon, S.H., Mridha, M.A.H., Shovon, M.: Automated recognition of rice grain diseases using deep learning. In: 2020 11th International Conference on Electrical and Computer Engineering (ICECE), pp. 230–233. IEEE (2020)

11.

Yang, Q., et al.: A near real-time deep learning approach for detecting rice phenology based on UAV images. Agric. For. Meteorol. 287, 107938 (2020)CrossRef

12.

Shi, Y., et al.: Improving performance: a collaborative strategy for the multi-data fusion of electronic nose and hyperspectral to track the quality difference of rice. Sens. Actuators B Chem. 333, 129546 (2021)CrossRef

13.

Bari, B.S., et al.: A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework. PeerJ Comput. Sci. 7, e432 (2021)CrossRef

14.

Zhu, A., et al.: Mapping rice paddy distribution using remote sensing by coupling deep learning with phenological characteristics. Remote Sens. 13(7), 1360 (2021)CrossRef

15.

Son, N.H., Thai-Nghe, N.: Deep learning for rice quality classification. In: 2019 International Conference on Advanced Computing and Applications, pp. 92–96. IEEE (2019)

16.

Weng, S., et al.: Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 234, 118237 (2020)CrossRef

17.

Joshi, D., et al.: Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network. Opt. Laser Technol. 137, 106861 (2021)CrossRef

18.

Pradana-López, S., et al.: Low requirement imaging enables sensitive and robust rice adulteration quantification via transfer learning. Food Control 127, 108122 (2021). https://doi.org/10.1016/j.foodcont.2021.108122CrossRef

19.

Yang, M.D., et al.: A UAV open dataset of rice paddies for deep learning practice. Remote Sens. 13(7), 1358 (2021)CrossRef

20.

Gilanie, G., et al.: RiceNet: convolutional neural networks-based model to classify Pakistani grown rice seed types. Multimed. Syst. 27(5), 867–875 (2021). https://doi.org/10.1007/s00530-021-00760-2CrossRef

21.

Estrada-Pérez, L.V., et al.: Thermal imaging of rice grains and flours to design convolutional systems to ensure quality and safety. Food Control 121, 107572 (2021). https://doi.org/10.1016/j.foodcont.2020.107572CrossRef

22.

Cinar, I., Koklu, M.: Classification of rice varieties using artificial intelligence methods. Int. J. Intell. Syst. Appl. Eng. 7(3), 188–194 (2019)CrossRef

23.

Peker, N., Kubat, C.: Application of Chi-square discretization algorithms to ensemble classification methods. Expert Syst. Appl. 185, 115540 (2021). https://doi.org/10.1016/j.eswa.2021.115540CrossRef

24.

Berkson, J.: Application of the logistic function to bio assay. J. Am. Stat. Assoc. 39(227), 357–365 (1944)

25.

Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)CrossRefMATH

26.

Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 144–152 (1986)

27.

Breiman, L., et al.: Classification and Regression Trees. CRC Press (1986)

28.

Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefMATH

29.

Bayes, T.: LII. An essay towards solving a problem in the doctrine of chances. By the late Rev. Mr. Bayes, FRS communicated by Mr. Price, in a letter to John Canton, AMFR S. Philos. Trans. R. Soc. Lond. 53, 370–418 (1763)

30.

Narasimha Murty, M., Susheela Devi, V.: Pattern Recognition. Springer, London (2011)CrossRefMATH

31.

Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)CrossRefMATH

32.

Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the 13th International Conference on Machine Learning (ICML), vol. 96, pp. 148–156 (1996)

33.

Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)CrossRefMATH

34.

Raschka, S.: Python Machine Learning. Packt Publishing Ltd. (2015)

35.

Srivastava, N., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH

36.

Chollet, F.: Keras: The Python deep learning library. Astrophysics Source Code Library, ascl-1806 (2018)

Title: Classification of Rice Varieties Using a Deep Neural Network Model
Author: Nuran Peker
Publisher: Springer Nature Singapore
Book: Advances in Intelligent Manufacturing and Service System Informatics
Print ISBN: 978-981-9960-61-3

Electronic ISBN: 978-981-9960-62-0

Copyright Year: 2024
DOI: https://doi.org/10.1007/978-981-99-6062-0_47

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"