Abstract
An automated grading system is important in assisting the farmers to perform quality inspection in a more effective manner as compared to manual approach. Besides that systematic fruit grading is a requirement for effective fruit and vegetable marketing. This is because delivering immature, and bruised fruits will lead to lower market price. Hence, this work proposed an automated Citrus suhuiensis fruit grading system based on image processing that can detect multi-index simultaneously such as maturity, quality and size of a local fruit. The fruits are classified according to the grading specification provided by Federal Agricultural Marketing Authority (FAMA). A convolutional neural network method is adopted to perform the classification process. A total of 303 training images and 75 test images were used in maturity dataset, whilst total of 283 training images and 68 test images were used in quality dataset. Experimental results showed that the proposed classification model able to classify the fruits into 6 classes of maturity and 3 classes of quality.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
H. Ghazali, R. Rosman, Z. Sharif, Soluble solid content determination of Limau Madu using microwave sensing technique at 2.0–2.6 GHz. IOP Conf. Ser. Mater. Sci. Eng. 341, 0–6 (2018). https://doi.org/10.1088/1757-899X/341/1/012006
N. Baiti, M. Kluang, P.O. Box, Preliminary growth performance of Limau Madu accessions 51–55 (2018)
P.R. Rokaya, D.R. Baral, D.M. Gautam, A.K. Shrestha, K.P. Paudyal, Effect of postharvest treatments on quality and shelf life of Mandarin (Citrus reticulata Blanco). Am. J. Plant Sci. 07, 1098–1105 (2016). https://doi.org/10.4236/ajps.2016.77105
F.M.A. Mazen, A.A. Nashat, Ripeness classification of bananas using an artificial neural network. Arab. J. Sci. Eng. 44, 6901–6910 (2019). https://doi.org/10.1007/s13369-018-03695-
M.R. Fiona, S. Thomas, I.J. Maria, B. Hannah, Identification of ripe and unripe citrus fruits using artificial neural network. J. Phys. Conf. Ser. 1362 (2019). https://doi.org/10.1088/1742-6596/1362/1/012033
H. Azarmdel, A. Jahanbakhshi, S.S. Mohtasebi, A.R. Muñoz, Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharvest Biol. Technol. 166, 111201 (2020). https://doi.org/10.1016/j.postharvbio.2020.111201
A. Magsi, J. Ahmed Mahar, S.H. Danwar, Date fruit recognition using feature extraction techniques and deep convolutional neural network. Indian J. Sci. Technol. 12, 1–12 (2019). https://doi.org/10.17485/ijst/2019/v12i32/146441
M.O. Al-Shawwa, Classification of apple fruits by deep learning. Int. J. Acad. Eng. Res. 3, 1–6 (2019)
L. Deng, D. Yu, Deep learning: methods and applications. Found. Trends Signal Process. 7, 197–387 (2013). https://doi.org/10.1561/2000000039
FAMA: Limaumanis.pdf (2001). http://www.fama.gov.my/documents/20143/0/Limaumanis.pdf/c33addc6-bd1c-12be-10f4-272fcbd6ecff
Acknowledgements
The research funding was provided by RU Grant—Faculty Programme by Faculty of Engineering, University of Malaya with project number GPF042A-2019.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mohmad, F.A., Mohd Khairuddin, A.S., Mohamed Shah, N. (2022). Automated Grading of Citrus suhuiensis Fruit Using Deep Learning Method. In: Kumar, A., Zurada, J.M., Gunjan, V.K., Balasubramanian, R. (eds) Computational Intelligence in Machine Learning. Lecture Notes in Electrical Engineering, vol 834. Springer, Singapore. https://doi.org/10.1007/978-981-16-8484-5_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-8484-5_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8483-8
Online ISBN: 978-981-16-8484-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)