Machine vision for automated maturity grading of oil palm fruits: A systematic review

Main Article Content

Afsar Kamal
Nur Diyana Kamarudin
Khairol Amali Bin Ahmad
Syarifah Bahiyah Rahayu
Mohd Rizal Mohd Isa
Siti Noormiza Makhtar
Zulkifli Yaakub


Keywords : machine Learning, deep Learning, CNN, feature extraction, Computer Vision, maturity grading
Abstract

The maturity of oil palm fruits is a very crucial factor for oil extraction industry in Indonesia, Malaysia, Thailand, and other countries to ensure the oil quality and increase productivity. This literature review examines the various machine learning techniques, especially the deep learning techniques used to automate the maturity grading process of oil palm fresh fruit bunches. The crucial advantages of using machine learning approaches were highlighted, and the limitations and prospects of each research article were discussed. This review describes the various image pre-processing techniques utilized to prepare images for model training. CNN is identified as the dominant over all classification techniques of machine learning to classify the oil palm fruits images based on maturity level, due to its ability of learning complex features.

Article Details

How to Cite
Kamal, A., Kamarudin, N. D., Bin Ahmad, K. A., Rahayu, S. B., Mohd Isa, M. R., Makhtar, S. N., & Yaakub, Z. (2024). Machine vision for automated maturity grading of oil palm fruits: A systematic review. Machine Graphics and Vision, 33(2), 47–75. https://doi.org/10.22630/MGV.2024.33.2.3
References

S. Abdul Saleem and T. Abdul Razak. Survey on color image enhancement techniques using spatial filtering. International Journal of Computer Applications, 94(9):39-45, 2014. https://doi.org/10.5120/16374-5837. (Crossref)

M. S. M. Alfatni, S. Khairunniza-Bejo, M. H. B. Marhaban, O. M. B. Saaed, A. Mustapha, et al. Towards a real-time oil palm fruit maturity system using supervised classifiers based on feature analysis. Agriculture, 12(9):1461, 2022. https://doi.org/10.3390/agriculture12091461. (Crossref)

M. S. M. Alfatni, A. R. M. Shariff, M. Z. Abdullah, M. H. Marhaban, S. B. Shafie, et al. Oil palm fresh fruit bunch ripeness classification based on rule-based expert system of roi image processing technique results. In: Proc. 7th IGRSM Int. Conf. and Exhibition on Geospatial & Remote Sensing, vol. 20 of IOP Conference Series: Earth and Environmental Science, p. 012018. Institute of Physics Publishing, Kuala Lumpur, Malaysia, 22-23 Apr 2014. https://doi.org/10.1088/1755-1315/20/1/012018. (Crossref)

M. S. M. Alfatni, A. R. M. Shariff, S. K. Bejo, O. M. B. Saaed, and A. Mustapha. Real-time oil palm FFB ripeness grading system based on ANN, KNN and SVM classifiers. In: Proc. 9th IGRSM Int. Conf. and Exhibition on Geospatial & Remote Sensing, vol. 169 of IOP Conference Series: Earth and Environmental Science, p. 012067. Institute of Physics Publishing, Kuala Lumpur, Malaysia, 24-25 Apr 2018. https://doi.org/10.1088/1755-1315/169/1/012067. (Crossref)

M. S. M. Alfatni, A. R. M. Shariff, O. M. Ben Saaed, A. M. Albhbah, and A. Mustapha. Colour feature extraction techniques for real time system of oil palm fresh fruit bunch maturity grading. In: Proc. 10th IGRSM Int. Conf. and Exhibition on Geospatial & Remote Sensing, vol. 540 of IOP Conference Series: Earth and Environmental Science, p. 012092. Institute of Physics Publishing, Kuala Lumpur, Malaysia, 20-21 Oct 2020. https://doi.org/10.1088/1755-1315/540/1/012092. (Crossref)

Z. Alom, M. Hasan, C. Yakopcic, T. M. Taha, and V. K. Asari. Recurrent residual convolutional neural network based on U-Net (R2U-Net) for medical image segmentation. arXiv, 2018. ArXiv:1802.06955. https://doi.org/10.48550/arXiv.1802.06955. (Crossref)

Y. Ang, H. Z. M. Shafri, Y. P. Lee, S. A. Bakar, H. Abidin, et al. Oil palm yield prediction across blocks from multi-source data using machine learning and deep learning. Earth Science Informatics, 15(4):2349-2367, 2022. https://doi.org/10.1007/s12145-022-00882-9. (Crossref)

S. Anwar and N. Barnes. Real image denoising with feature attention. In: Proc. 2019 IEEE/CVF Int. Conf. Computer Vision (ICCV), pp. 3155-3164. Seoul, Korea (South), 27 Oct - 02 Nov 2019. https://doi.org/10.1109/ICCV.2019.00325. (Crossref)

S. Ashari, G. J. Yanris, and I. Purnama. Oil palm fruit ripeness detection using deep learning. SinkrOn, 6(2):649-656, 2022. https://doi.org/10.33395/sinkron.v7i2.11420. (Crossref)

S. Avidan and A. Shamir. Seam carving for content-aware image resizing. ACM Transactions on Graphics, 26(3):10-1̶-10-9, July 2007. https://doi.org/10.1145/1239451.1239461. (Crossref)

M. Bagheri and M. H. Sedaaghi. A new method for detecting jittered PRI in histogram-based methods. Turkish Journal of Electrical Engineering and Computer Sciences, 26(3):1214-1224, 2018. https://doi.org/10.3906/elk-1710-169. (Crossref)

A. Bansal and N. Singh. Image enhancement techniques: A review. Asian Journal For Convergence In Technology (AJCT), 6(2):07-11, 2020. https://doi.org/10.33130/AJCT.2020v06i02.002. (Crossref)

O. M. Bensaeed, A. M. Shariff, A. B. Mahmud, H. Shafri, and M. Alfatni. Oil palm fruit grading using a hyperspectral device and machine learning algorithm. In: Proc. 7th IGRSM Int. Conf. and Exhibition on Geospatial & Remote Sensing, vol. 20 of IOP Conference Series: Earth and Environmental Science, p. 012017. Institute of Physics Publishing, Kuala Lumpur, Malaysia, 22023 Apr 2014. https://doi.org/10.1088/1755-1315/20/1/012017. (Crossref)

C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3):1937-1967, 2021. https://doi.org/10.1007/s10462-020-09896-5. (Crossref)

J. C. Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, New York, USA, 1981. https://doi.org/10.1007/978-1-4757-0450-1. (Crossref)

G. Biau and E. Scornet. A random forest guided tour. Test, 25(2):197-227, 2016. https://doi.org/10.1007/s11749-016-0481-7. (Crossref)

N. Bibi and H. Dawood. SEBR: Scharr edge-based regularization method for blind image deblurring. Arabian Journal for Science and Engineering, 49(3):3435-3451, 2024. https://doi.org/10.1007/s13369-023-07986-4. (Crossref)

J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679-698, 1986. https://doi.org/10.1109/ASICON.2011.6157287. (Crossref)

X. H. Cao, I. Stojkovic, and Z. Obradovic. A robust data scaling algorithm to improve classification accuracies in biomedical data. BMC Bioinformatics, 17:359, 2016. https://doi.org/10.1186/s12859-016-1236-x. (Crossref)

N. Cauli and D. Reforgiato Recupero. Survey on videos data augmentation for deep learning models. Future Internet, 14(3):93, 2022. https://doi.org/10.3390/FI14030093. (Crossref)

G. N. Chaple, R. D. Daruwala, and M. S. Gofane. Comparisions of Robert, Prewitt, Sobel operator based edge detection methods for real time uses on FPGA. In: Proc. Int. Conf. Technologies for Sustainable Development (ICTSD 2015), pp. 1-4. Mumbai, India, 04-06 Feb 2015. https://doi.org/10.1109/ICTSD.2015.7095920. (Crossref)

C. Cheadle, M. P. Vawter, W. J. Freed, and K. G. Becker. Analysis of microarray data using Z Score transformation. The Journal of Molecular Diagnostics, 5(2):73-81, 2003. https://doi.org/10.1016/S1525-1578(10)60455-2.

C. Chen, Q. Chen, J. Xu, and V. Koltun. Learning to see in the dark. In: Proc. 2018 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 3291-3300. Salt Lake City, UT, USA, 18-23 Jun 2018. https://doi.org/10.1109/CVPR.2018.00347. (Crossref)

Y. Chen, H. Jiang, C. Li, X. Jia, and P. Ghamisi. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10):6232-6251, 2016. https://doi.org/10.1109/TGRS.2016.2584107. (Crossref)

N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In: Proc. 2005 IEEE Computer Society Conf. Computer Vision and Pattern Recognition (CVPR'05), pp. 886-893. San Diego, CA, USA, 20-25 Jun 2005. https://doi.org/10.1109/CVPR.2005.177. (Crossref)

L. M. Dale, A. Thewis, C. Boudry, I. Rotar, P. Dardenne, et al. Hyperspectral imaging applications in agriculture and agro-food product quality and safety control: A review. Applied Spectroscopy Reviews, 48(2):142-159, 2013. https://doi.org/10.1080/05704928.2012.705800. (Crossref)

D. Danon, M. Arar, D. Cohen-Or, and A. Shamir. Image resizing by reconstruction from deep features. Computational Visual Media, 7(4):453-466, 2021. https://doi.org/10.1007/s41095-021-0216-x. (Crossref)

S. Ding, W. Jia, C. Su, F. Jin, and Z. Shi. A survey on statistical pattern feature extraction. In: Proc. Conf. Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2008), vol. 5227 of Lecture Notes in Computer Science, pp. 701-708. Shanghai, China, 15-18 Sep 2008. https://doi.org/10.1007/978-3-540-85984-0_84. (Crossref)

C. Dong, C. C. Loy, K. He, and X. Tang. Learning a deep convolutional network for image super-resolution. In: Proc. European Conf. Computer Vision (ECCV 2014), vol. 8692 of Lecture Notes in Computer Science, pp. 184-199. Zurich, Switzerland, 6-12 Sep 2014. https://doi.org/10.1007/978-3-319-10593-2_13. (Crossref)

R. Dong, W. Li, H. Fu, L. Gan, L. Yu, et al. Oil palm plantation mapping from high-resolution remote sensing images using deep learning. International Journal of Remote Sensing, 41(5):2022-2046, 2020. https://doi.org/10.1080/01431161.2019.1681604. (Crossref)

S. R. Dubey and A. S. Jalal. Application of image processing in fruit and vegetable analysis: A review. Journal of Intelligent Systems, 24(4):405-424, 2015. https://doi.org/10.1515/jisys-2014-0079. (Crossref)

P. Dutta. Palm oil classification using deep learning. International Journal of Advance Research, Ideas and Innovations in Technology, 6(5):V6I5-1379, 2020. https://www.ijariit.com/manuscript/palm-oil-classification-using-deep-learning/.

N. Fadilah, J. Mohamad-Saleh, Z. A. Halim, H. Ibrahim, and S. S. S. Ali. Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch. Sensors, 12(10):14179-14195, 2012. https://doi.org/10.3390/s121014179. (Crossref)

J. Fang, C. Tang, Q. Cui, F. Zhu, L. Li, et al. Semi-supervised learning with data augmentation for tabular data. In: Proc. 31st ACM Int. Conf. Information & Knowledge Management (CIKM '22, p. 3928–3932. Atlanta, GA, USA, 2022. https://doi.org/10.1145/3511808.3557699. (Crossref)

G. D. Finlayson, M. S. Drew, and B. V. Funt. Color constancy: generalized diagonal transforms suffice. Journal of the Optical Society of America A, 11(11):3011-3019, 1994. https://doi.org/10.1364/josaa.11.003011. (Crossref)

G. D. Finlayson, S. D. Hordley, and P. M. Hubel. Color by correlation: A simple, unifying framework for color constancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1209-1221, 2001. https://doi.org/10.1109/34.969113. (Crossref)

D. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall, 2003.

P. Garg and T. Jain. A comparative study on histogram equalization and cumulative histogram equalization. International Journal of New Technology and Research, 3(9):73-81, 2003. https://doi.org/10.1016/S1525-1578(10)60455-2. (Crossref)

S. Garg and G. Ramakrishnan. Advances in quantum deep learning: An overview. arXiv, 2020. ArXiv:2005.04316. https://doi.org/10.48550/arXiv.2005.04316.

S. A. Ghazalli, H. Selamat, Z. Omar, and R. Yusof. Image analysis techniques for ripeness detection of palm oil fresh fruit bunches. ELEKTRIKA-Journal of Electrical Engineering, 18(3):57-62, 2019. https://doi.org/10.11113/elektrika.v18n3.192. (Crossref)

R. C. Gonzalez, R. E. Woods, and S. L. Eddins. Digital Image Processing Using MATLAB. Gatesmark, 2020. https://www.imageprocessingplace.com.

S. Guo, Z. Yan, K. Zhang, W. Zuo, and L. Zhang. Toward convolutional blind denoising of real photographs. In: Proc. 2019 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1712-1722. Long Beach, CA, USA, 15-20 Jun 2019. https://doi.org/10.1109/CVPR.2019.00181. (Crossref)

Harsawardana, R. Rahutomo, B. Mahesworo, T. W. Cenggoro, A. Budiarto, et al. AI-based ripeness grading for oil palm fresh fruit bunch in smart crane grabber. In: Proc. 3rd Int. Conf. Eco Engineering Development, vol. 426 of IOP Conference Series: Earth and Environmental Science, p. 012147. Institute of Physics Publishing, 13-14 Nov 2020. https://doi.org/10.1088/1755-1315/426/1/012147. (Crossref)

J. A. Hartigan and M. A. Wong. Algorithm as 136: A k-means clustering algorithm. Journal of the Royal Statistical Cociety. Series C (Applied Statistics), 28(1):100-108, 1979. https://doi.org/10.2307/2346830. (Crossref)

N. H. Harun, N. Misron, R. M. Sidek, I. Aris, D. Ahmad, et al. Investigations on a novel inductive concept frequency technique for the grading of oil palm fresh fruit bunches. Sensors (Switzerland), 13(2):2254-2266, 2013. https://doi.org/10.3390/s130202254. (Crossref)

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In: Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 770-778. Las Vegas, NV, USA, 27-30 Jun 2016. https://doi.org/10.1109/CVPR.2016.90. (Crossref)

M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, and B. Scholkopf. Support vector machines. IEEE Intelligent Systems and their Applications, 13(4):18-28, 1998. https://doi.org/10.1109/5254.708428. (Crossref)

A. Humeau-Heurtier. Texture feature extraction methods: A survey. IEEE Access, 7:8975-9000, 2019. https://doi.org/10.1109/ACCESS.2018.2890743. (Crossref)

Z. Ibrahim, N. Sabri, and D. Isa. Palm oil fresh fruit bunch ripeness grading recognition using convolutional neural network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3-2):109-113, 2018. https://jtec.utem.edu.my/jtec/article/view/4720.

S. Ioffe. Batch renormalization: Towards reducing minibatch dependence in batch-normalized models. In: Advances in Neural Information Processing Systems 30 - Proc. 30th Conf. Neural Information Processing Systems (NIPS 2017), vol. 30, pp. 1945-1953. Long Beach, CA, USA, 4-9 Dec 2017. http://papers.nips.cc/paper/by-source-2017-1198/.

N. Iqbal, R. Mumtaz, U. Shafi, and S. M. H. Zaidi. Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms. PeerJ Computer Science, 7:e536, 2021. https://doi.org/10.7717/PEERJ-CS.536. (Crossref)

N. Ismail and O. A. Malik. Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Information Processing in Agriculture, 9(1):24-37, March 2022. https://doi.org/10.1016/j.inpa.2021.01.005. (Crossref)

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with Conditional Adversarial Networks. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 5967-5976. Honolulu, HI, USA, 2017. https://doi.org/10.1109/CVPR.2017.632. (Crossref)

A. Jaffar, R. Jaafar, N. Jamil, C. Y. Low, and B. Abdullah. Photogrammetric grading of oil palm fresh fruit bunches. International Journal of Mechanical & Mechatronics Engineering, 9(10):7-13, 2009.

A. K. Jain. Fundamentals of digital image processing. Prentice-Hall, Englewood Cliffs, NJ, USA, 1989.

A. N. Jarayee, H. Z. M. Shafri, Y. Ang, Y. P. Lee, S. A. Bakar, et al. Oil palm plantation land cover and age mapping using Sentinel-2 satellite imagery and machine learning algorithms. In: Proc. 8th Int. Conf. Geomatics and Geospatial Technology (GGT 2022), vol. 1051 of IOP Conference Series: Earth and Environmental Science, p. 012024. Institute of Physics Publishing, Online, 25-26 May 2022. https://doi.org/10.1088/1755-1315/1051/1/012024. (Crossref)

X. Jiang, Y. Wang, W. Liu, S. Li, and J. Liu. CapsNet, CNN, FCN: Comparative performance evaluation for image classification. International Journal of Machine Learning, 9(6):840-848, 2019. https://doi.org/10.18178/ijmlc.2019.9.6.881. (Crossref)

F. A. Junior and Suharjito. Video based oil palm ripeness detection model using deep learning. Heliyon, 9(1):e13036, 2023. https://doi.org/10.1016/j.heliyon.2023.e13036. (Crossref)

P. Junkwon, T. Takigawa, H. Okamoto, H. Hasegawa, M. Koike, et al. Potential application of color and hyperspectral images for estimation of weight and ripeness of oil palm (Elaeis guineensis Jacq. var. tenera). Agricultural Information Research, 18(2):72-81, 2009. https://doi.org/10.3173/air.18.72. (Crossref)

B. Khagi and G. R. Kwon. Pixel-label-based segmentation of cross-sectional brain MRI using simplified SegNet architecture-based CNN. Jornal of Healthcare Engineering, 2018:3640705, 2018. https://doi.org/10.1155/2018/3640705. (Crossref)

S. Khalid, T. Khalil, and S. Nasreen. A survey of feature selection and feature extraction techniques in machine learning. In: Proc. 2014 Science and Information Conference (SAI 2014), pp. 372-378. IEEE, London, UK, 27-29 Aug 2014. https://doi.org/10.1109/SAI.2014.6918213. (Crossref)

J. Kim, J. K. Lee, and K. M. Lee. Accurate image super-resolution using very deep convolutional networks. In: Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 1646-1654. Las Vegas, NV, USA, 27-30 Jun 2016. https://doi.org/10.1109/CVPR.2016.182. (Crossref)

S. B. Kotsiantis. Decision trees: A recent overview. Artificial Intelligence Review, 39(4):261-283, 2013. https://doi.org/10.1007/s10462-011-9272-4. (Crossref)

O. Kramer. K-nearest neighbors. In: Dimensionality Reduction with Unsupervised Nearest Neighbors, vol. 51 of Intelligent Systems Reference Library, pp. 13-23. Springer, Berlin, Heidelberg, 2013. https://doi.org/10.1007/978-3-642-38652-7_2. (Crossref)

A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In: Proc. 25th Int. Conf. Neural Information Processing Systems (NIPS), pp. 1097-1105. Curran Associates, Lake Tahoe, NV, USA, 3-8 Dec 2012. https://proceedings.neurips.cc/paper_files/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html.

O. Kupyn, V. Budzan, M. Mykhailych, D. Mishkin, and J. Matas. DeblurGAN: Blind motion deblurring using conditional adversarial networks. In: Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8183-8192. Salt Lake City, UT, USA, 18-23 Jun 2018. https://doi.org/10.1109/CVPR.2018.00854. (Crossref)

J. W. Lai, H. R. Ramli, L. I. Ismail, and W. Z. W. Hasan. Real-time detection of ripe oil palm fresh fruit bunch based on yolov4. IEEE Access, 10:95763-95770, 2022. https://doi.org/10.1109/ACCESS.2022.3204762. (Crossref)

E. Land and J. McCann. Lightness and retinex theory. Journal of the Optical Society of America, 61(1):1-11, 1971. https://doi.org/10.1364/JOSA.61.000001. (Crossref)

M. P. LaValley. Logistic regression. Circulation, 117(18):2395-2399, May 2008. https://doi.org/10.1161/CIRCULATIONAHA.106.682658. (Crossref)

Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436-444, 2015. https://doi.org/10.1038/nature14539. (Crossref)

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2323, 1998. https://doi.org/10.1109/5.726791. (Crossref)

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, et al. Photo-realistic single image super-resolution using a generative adversarial network. In: Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 105-114. Honolulu, HI, USA, 21-26 Jul 2017. https://doi.org/10.1109/CVPR.2017.19. (Crossref)

W. Li, K. Z. Mao, H. Zhang, and T. Chai. Selection of Gabor filters for improved texture feature extraction. In: Proc. Int. Conf. Image Processing (ICIP), pp. 361-364. Hong Kong, China, 26-29 Sep 2010. https://doi.org/10.1109/ICIP.2010.5653278. (Crossref)

Q. Ma, Y. Wang, and T. Zeng. Retinex-based variational framework for low-light image enhancement and denoising. IEEE Transactions on Multimedia, 25:5580-5588, 2023. https://doi.org/10.1109/TMM.2022.3194993. (Crossref)

S. G. Mallat. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7):674-693, 1989. https://doi.org/10.1109/34.192463. (Crossref)

D. Marr and E. Hildreth. Theory of edge detection. Proceedings of the Royal Society B Biological Sciences, 207(1167):187-217, 1980. https://doi.org/10.1098/RSPB.1980.0020. (Crossref)

N. Misron, N. A. Aliteh, N. H. Harun, K. Tashiro, T. Sato, et al. Relative estimation of water content for flat-type inductive-based oil palm fruit maturity sensor. Sensors (Switzerland), 17(1):52, 2017. https://doi.org/10.3390/s17010052. (Crossref)

N. Misron, N. S. K. Azhar, M. N. Hamidon, I. Aris, K. Tashiro, et al. Fruit battery with charging concept for oil palm maturity sensor. Sensors, 20(1):52, 2020. https://doi.org/10.3390/s20010226. (Crossref)

N. A. Mubin, E. Nadarajoo, H. Z. M. Shafri, and A. Hamedianfar. Young and mature oil palm tree detection and counting using convolutional neural network deep learning method. International Journal of Remote Sensing, 40(19):7500-7515, 2019. https://doi.org/10.1080/01431161.2019.1569282. (Crossref)

A. Mustaffa, F. Arith, N. I. F. Peong, N. R. Jaffar, E. L. Linggie, et al. Segregation of oil palm fruit ripeness using color sensor. Indonesian Journal of Electrical Engineering and Computer Science, 25(1), 2022. https://doi.org/10.11591/ijeecs.v25.i1.pp130-137. (Crossref)

S. Nah, T. H. Kim, and K. M. Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In: Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 257-265. Honolulu, HI, USA, 21-26 Jul 2017. https://doi.org/10.1109/CVPR.2017.35. (Crossref)

B. R. Naidu, P. L. Rao, P. Babu, and K. V. L. Bhavani. Efficient case study for image edge gradient based detectors - Sobel, Robert Cross, Prewitt and Canny. International Journal of Electronics Communication and Computer Engineering, 3(3):561-570, 2012. https://www.ijecce.org/index.php/issues?view=publication&task=show&id=162.

I. Nurhabib, K. B. Seminar, and Sudradjat. Recognition and counting of oil palm tree with deep learning using satellite image. In: Proc. 2nd Int. Conf. Sustainable Plantation, vol. 974 of IOP Conference Series: Earth and Environmental Science, p. 012058. Institute of Physics Publishing, Bogor, Indonesia, 2-3 Sep 2022. https://doi.org/10.1088/1755-1315/974/1/012058. (Crossref)

T. Ojala, M. Pietikainen, and T. Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623. (Crossref)

N. Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1):62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076. (Crossref)

K. O’Shea and R. Nash. An introduction to convolutional neural networks. arXiv, 2015. ArXiv.08458v2. https://doi.org/10.48550/arXiv.1511.08458.

D. S. Park, W. Chan, Y. Zhang, C. C. Chiu, B. Zoph, et al. SpecAugment: A simple data augmentation method for automatic speech recognition, 15-19 Sep 2019. https://doi.org/10.21437/Interspeech.2019-2680. (Crossref)

S. Pizer, R. Johnston, J. Ericksen, B. Yankaskas, and K. Muller. Contrast-limited adaptive histogram equalization: speed and effectiveness. In: [1990] Proc. First Conference on Visualization in Biomedical Computing, pp. 337-345. Atlanta, GA, USA, 22-25 May 1990. https://doi.org/10.1109/VBC.1990.109340. (Crossref)

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, et al. Adaptive histogram equalization and its variations. Computer Vision, Graphics, and Image Processing, 39(3):355-368, 1987. https://doi.org/10.1016/S0734-189X(87)80186-X. (Crossref)

N. A. Prasetyo, Pranowo, and A. J. Santoso. Automatic detection and calculation of palm oil fresh fruit bunches using faster R-CNN. International Journal of Applied Science and Engineering, 17(2):121-134, 2020. https://doi.org/10.6703/IJASE.202005_17(2).121.

J. M. S. Prewitt. Object Enhancement and Extraction. In: B. S. Lipkin and A. Rozenfeld, eds., Picture Processing and Psychopictorics, pp. 75-150. Academic Press, New York, London, 1970.

G. J. Quan, A. R. B. M. Shariff, and N. M. Nawi. Grading of maturity of oil palm fruit based on visible and NIR band. In: Proc. Asian Conference on Remote Sensing (ACRS 2020), 2020. https://acrs-aars.org/proceeding/ACRS2020/35kl3w.pdf.

T. Raj, F. H. Hashim, A. B. Huddin, A. Hussain, M. F. Ibrahim, et al. Classification of oil palm fresh fruit maturity based on carotene content from raman spectra. Scientific Reports, 11(1):18315, 2021. https://doi.org/10.1038/s41598-021-97857-5. (Crossref)

P. R. Rajarapollu and V. R. Mankar. Bicubic interpolation algorithm implementation for image appearance enhancement. InternatIonal Journal of Computer Science and Technology, 8(2):23-26, 2017. https://www.ijcst.com/vol8/8.2/4-prachi-r-rajarapollu.pdf.

E. Reinhard, M. Ashikhmin, B. Gooch, and P. Shirley. Color transfer between images. IEEE Computer Graphics and Applications, 21(5):34-41, 2001. https://doi.org/10.1109/38.946629. (Crossref)

Y. D. Rivera-Mendes, J. C. Cuenca, and H. M. Romero. Physiological responses of oil palm (Elaeis guineensis Jacq.) seedlings under different water soil conditions. Agronomia Colombiana, 34(2):163-171, 2016. https://doi.org/10.15446/agron.colomb.v34n2.55568. (Crossref)

O. Ronneberger, P. Fischer, and T. Brox. U-Net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention - Proc. MICCAI 2015, vol. 9351 of Lecture Notes in Computer Science, p. 234–241. Springer Verlag, Munich, Germany, 5-9 Oct 2015. https://doi.org/10.1007/978-3-319-24574-4_28. (Crossref)

N. Sabri, Z. Ibrahim, S. Syahlan, N. Jamil, and N. N. A. Mangshor. Palm oil fresh fruit bunch ripeness grading identification using color features. Journal of Fundamental and Applied Sciences, 9(4S):563-579, 2018. https://doi.org/10.4314/JFAS.V9I4S.32. (Crossref)

O. M. B. Saeed, S. Sankaran, A. R. M. Shariff, H. Z. M. Shafri, R. Ehsani, et al. Classification of oil palm fresh fruit bunches based on their maturity using portable four-band sensor system. Computers and Electronics in Agriculture, 82:55-60, 2012. https://doi.org/10.1016/j.compag.2011.12.010. (Crossref)

A. Y. Saleh and E. Liansitim. Palm oil classification using deep learning. Science in Information Technology Letters, 1(1):1-8, 2020. https://doi.org/10.31763/sitech.v1i1.1. (Crossref)

H. Salehinejad, S. Sankar, J. Barfett, E. Colak, and S. Valaee. Recent advances in recurrent neural networks. arXiv, 2017. ArXiv:01078v3. https://doi.org/10.48550/arXiv.1801.01078.

M. I. Sameen and A. R. b. Mohamed Shariff. The use of genetic algorithm for palm oil fruit maturity detection. In: Proc. The 36th Asian Conf. Remote Sensing (ACRS 2015), pp. 3552-3556. Curran Associates, Inc., 2016, Quezón City, Metro Manila, Philippines, 24-28 Oct 2015.

Y. D. Sean, D. D. Smith, V. S. P. Bitra, V. Bera, and S. N. Umar. Development of computer vision system for fruits. Current Journal of Applied Science and Technology, 40(36):1-11, 2021. https://doi.org/10.9734/cjast/2021/v40i3631576. (Crossref)

M. K. Shabdin, A. R. M. Shariff, M. N. A. Johari, N. K. Saat, and Z. Abbas. A study on the oil palm fresh fruit bunch (FFB) ripeness detection by using hue, saturation and intensity (HSI) approach. In: Proc. 8th IGRSM Int. Conf. and Exhibition on Geospatial & Remote Sensing, vol. 37 of IOP Conference Series: Earth and Environmental Science, p. 012039. Institute of Physics Publishing, Kuala Lumpur, Malaysia, 13-14 Apr 2014. https://doi.org/10.1088/1755-1315/37/1/012039. (Crossref)

J. H. Shah, M. Sharif, M. Raza, and A. Azeem. A survey: Linear and nonlinear PCA based face recognition techniques. The International Arab Journal of Information Technology, 10(6):536-545, 2013. https://iajit.org/paper/3370/A-Survey-Linear-and-Nonlinear-PCA-Based.

C. Shorten and T. M. Khoshgoftaar. A survey on image data augmentation for deep learning. J Big Data, 6(1):60, 2019. https://doi.org/10.1186/s40537-019-0197-0. (Crossref)

S. Sinsomboonthong. Performance comparison of new adjusted min-max with decimal scaling and statistical column normalization methods for artificial neural network classification. International Journal of Mathematics and Mathematical Sciences, 2022:3584406, 2022. https://doi.org/10.1155/2022/3584406. (Crossref)

I. E. Sobel. Camera Models and Machine Perception. Ph.D. thesis, Stanford University, Stanford, CA, 1970.

Suharjito, F. A. Junior, Y. P. Koeswandy, Debi, P. W. Nurhayati, et al. Annotated datasets of oil palm fruit bunch piles for ripeness grading using deep learning. Scientific Data, 10(1):72, 2023. https://doi.org/10.1038/s41597-023-01958-x. (Crossref)

K. Sunilkumar and D. Babu. Surface color based prediction of oil content in oil palm (Elaeis guineensis Jacq.) fresh fruit bunch. African Journal of Agricultural Research, 8(6):564-569, 2013. https://doi.org/10.5897/AJAR12.1789.

A. Susanto, T. W. Cenggoro, and B. Pardamean. Oil palm fruit image ripeness classification with computer vision using deep learning and visual attention. Journal of Telecommunication, Electronic and Computer Engineering, 12(2):21–27, 2020. https://jtec.utem.edu.my/jtec/article/view/5543.

A. Syaifuddin, L. N. A. Mualifah, L. Hidayat, and A. M. Abadi. Detection of palm fruit maturity level in the grading process through image recognition and fuzzy inference system to improve quality and productivity of crude palm oil (CPO). In: Proc. 3rd Int. Seminar on Innovation in Mathematics and Mathematics Education (ISIMMED 2019), vol. 1581 of Journal of Physics: Conference Series, p. 012003. Institute of Physics Publishing, Yogyakarta, Indonesia, 3-4 Oct 2020. https://doi.org/10.1088/1742-6596/1581/1/012003. (Crossref)

R. Szeliski. Computer Vision: Algorithms and Applications. Springer Nature, 2022. https://doi.org/10.1007/978-3-030-34372-9. (Crossref)

E. Teye, C. L. Y. Amuah, T. S. Yeh, and R. Nyorkeh. Nondestructive detection of moisture content in palm oil by using portable vibrational spectroscopy and optimal prediction algorithms. Journal of Analytical Methods in Chemistry, 2023:3364720, 2023. https://doi.org/10.1155/2023/3364720. (Crossref)

Y. Triyanto, R. Watrianthos, Y. Sepriani, and K. Rizal. Palm oil prediction production using extreme learning machine. International Journal of Scientific & Technology Research, 8:8, 2019. https://www.ijstr.org/final-print/aug2019/Palm-Oil-Prediction-Production-Using-Extreme-Learning-Machine.pdf. (Crossref)

A. Tuerxun, A. R. M. Shariff, R. Janius, Z. Abbas, and G. A. Mahdiraji. Oil palm fresh fruit bunches maturity prediction by using optical spectrometer. In: Proc. 10th IGRSM Int. Conf. and Exhibition on Geospatial & Remote Sensing, vol. 540 of IOP Conference Series: Earth and Environmental Science, p. 012085. Institute of Physics Publishing, Kuala Lumpur, Malaysia, 20-21 Oct 2020. https://doi.org/10.1088/1755-1315/540/1/012085. (Crossref)

V. Tyagi. Understanding Digital Image Processing. CRC Press, Boca Raton, 2018. https://doi.org/10.1201/9781315123905. (Crossref)

G. T. H. Tzuan, F. H. Hashim, T. Raj, A. B. Huddin, and M. S. Sajab. Oil palm fruits ripeness classification based on the characteristics of protein, lipid, carotene, and guanine/cytosine from the Raman spectra. Plants, 11(15):1936, 2022. https://doi.org/10.3390/plants11151936. (Crossref)

D. Ulyanov, A. Vedaldi, and V. Lempitsky. Instance normalization: The missing ingredient for fast stylization. arXiv, 2016. ArXiv:1607.08022. https://doi.org/10.48550/arXiv.1607.08022.

L. Vincent and P. Soille. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(06):583-598, 1991. https://doi.org/10.1109/34.87344. (Crossref)

X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, et al. ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. In: Proc. European Conf. Computer Vision (ECCV) Workshops, pp. 63-79. Springer, 2018, Munich, Germany, 8-14 Sep 2018. https://doi.org/10.1007/978-3-030-11021-5_5. (Crossref)

J. Wei and K. Zou. EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks. In: Proc. ICLR 2019 Workshop on Learning from Limited Labeled Data. New Orleans, LA, United States, 6-9 May 2019. Accessible on OpenReview. https://openreview.net/forum?id=BJelsDvo84. (Crossref)

H. Wibowo, I. S. Sitanggang, M. Mushthofa, and H. A. Adrianto. Large-scale oil palm trees detection from high-resolution remote sensing images using deep learning. Big Data and Cognitive Computing, 6(3):89, 2022. https://doi.org/10.3390/bdcc6030089. (Crossref)

Z. Y. Wong, W. J. Chew, and S. K. Phang. Computer vision algorithm development for classification of palm fruit ripeness. In: Proc. 13th. Int. Engineering Research Conference (13TH EURECA 2019), vol. 2233 of AIP Conference Proceedings, p. 030012, 2020. https://doi.org/10.1063/5.0002188. (Crossref)

B. Xu, Y. Zhuang, H. Tang, and L. Zhang. Object-based multilevel contrast stretching method for image enhancement. IEEE Transactions on Consumer Electronics, 56(3):1746-1754, 2010. https://doi.org/10.1109/TCE.2010.5606321. (Crossref)

W. Yang, X. Zhang, Y. Tian, W. Wang, J. H. Xue, et al. Deep learning for single image super-resolution: A brief review. IEEE Transactions on Multimedia, 21(12):3106-3121, 2019. https://doi.org/10.1109/TMM.2019.2919431. (Crossref)

K. Yarak, A. Witayangkurn, K. Kritiyutanont, C. Arunplod, and R. Shibasaki. Oil palm tree detection and health classification on high‐resolution imagery using deep learning. Agriculture, 11(2):183, 2021. https://doi.org/10.3390/agriculture11020183. (Crossref)

Y. Zhan, D. Hu, Y. Wang, and X. Yu. Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geoscience and Remote Sensing Letters, 15(2):212-216, 2018. https://doi.org/10.1109/LGRS.2017.2780890. (Crossref)

F. Zhang, Y. Shao, Y. Sun, K. Zhu, C. Gao, et al. Unsupervised low-light image enhancement via histogram equalization prior. arXiv, 2021. ArXiv:2112.01766. https://doi.org/10.48550/arXiv.2112.01766.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26(7):3142-3155, 2017. https://doi.org/10.1109/TIP.2017.2662206. (Crossref)

R. Zhang, P. Isola, and A. A. Efros. Colorful image colorization. In: Proc. European Conf. Computer Vision (ECCV 2016), vol. 9907 of Lecture Notes in Computer Science, pp. 649-666. Springer International Publishing, Amsterdam, The Netherlands, 11-14 Oct 2016. https://doi.org/10.1007/978-3-319-46487-9_40. (Crossref)

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proc. 2017 IEEE Int. Conf. Computer Vision (ICCV), pp. 2242-2251. Venice, Italy, 22-29 Oct 2017. https://doi.org/10.1109/ICCV.2017.244. (Crossref)

X. Zhu, D. Shen, R. Wang, Y. Zheng, S. Su, et al. Maturity grading and identification of Camellia oleifera fruit based on unsupervised image clustering. Foods, 11(23):3800, 2022. https://doi.org/10.3390/FOODS11233800. (Crossref)

S. Zolfagharnassab, A. R. B. M. Shariff, R. Ehsani, H. Z. Jaafar, and I. B. Aris. Classification of oil palm fresh fruit bunches based on their maturity using thermal imaging technique. Agriculture, 12(11):1779, 2022. https://doi.org/10.3390/agriculture12111779. (Crossref)

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