Efficient Deep Learning model for de-husked Areca nut classification
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Abstract
Areca nut is a widely used agricultural product in India and even over the globe. Areca nut, a fruit of areca palm (Areca catechu) is grown widely in the Asia-Pacific region.. Areca nut segregation is of prime importance in the areca nut industry. The quality segregation of peeled/de-husked nuts requires skilled workers. This process of manual segregation is time-consuming and can lead to erroneous classification. Recent deep learning (DL) advances have improved the performance in multi-class problems. The present work presents the classification of de-husked areca nut among five classes using an efficient deep learning customized Convolutional Neural Network (CNN) and the results of this model were compared with the standard AlexNet architecture. The new CNN model was customized to obtain classification accuracy higher than the existing ones. A dataset of 300 nuts (60 per class) was created using a specially designed instrumentation setup. The areca nut images were then pre-processed and fed to these models to learn the features of the areca nut from different classes. The confusion matrix and Area Under the Curve - Receiver Operating Characteristics (AUC- ROC) were employed to assess the results of these models and cross-validated with 5 and 10-fold. The experimental results show that the CNN outperformed the AlexNet model with an average accuracy of 97.33% and 98.34%, F1 score of 97.48%, and 98.45% for 5 and 10 folds, respectively.
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Keywords
AlexNet, Area Under Curve (AUC), Areca nut, Convolutional Neural Network (CNN), Deep Learning
References
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Danti, A.& Suresha (2012). Segmentation and Classification of Raw Arecanuts Based on Three Sigma Control Limits. Procedia Technology 4, 215–219. https://doi.org/10.1016/j.protcy.2012.05.032
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Madani, A., Arnaout, Ramy, Mofrad, M.& Arnaout, Rima (2018). Fast and accurate view classification of echocardiograms using deep learning. npj Digital Med 1, 6. https://doi.org/10.1038/s41746-017-0013-1
Mai, X., Zhang, H., Jia, X.& Meng, M.Q.-H. (2020). Faster R-CNN With Classifier Fusion for Automatic Detection of Small Fruits. IEEE Trans. Automat. Sci. Eng. 1–15. https://doi.org/10.1109/TASE.2020.2964289
Mallaiah, S., Danti, A.& Narasimhamurthy, S.K. (2014). Classification of Diseased Arecanut based on Texture Features. IJCA, Recent Advances in Information Technology 1, 1–6.
Mallikarjuna, S.B., Shivakumara, P., Khare, V., Kumar N, V., Basavanna, M.,., Pal, U.& Poornima, B.. (2021). CNN BASED METHOD FOR MULTI-TYPE DISEASED ARECANUT IMAGE CLASSIFICATION. MJCS 34, 255–265. https://doi.org/10.22452/mjcs.vol34no3.3
Meghana D R& Prabhudeva S (2022). Image Processing based Arecanut Diseases Detection Using CNN Model. IJARSCT 747–752. https://doi.org/10.48175/IJARSCT-5154
Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A.& Doulamis, N. (2021). Multi-class Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. Technologies 9, 81. https://doi.org/10.3390/technologies9040081
Moss, W.J. (2022). The Seeds of Ignorance — Consequences of a Booming Betel-Nut Economy. N Engl J Med 387, 1059–1061. https://doi.org/10.1056/NEJMp2203571
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R.J., Fredes, C.& Valenzuela, A. (2020). A Review of Convolutional Neural Network Applied to Fruit Image Processing. Applied Sciences 10, 3443. https://doi.org/10.3390/app10103443
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Park, S.B., Lee, J.W.& Kim, S.K. (2004). Content-based image classification using a neural network. Pattern Recognition Letters 25, 287–300. https://doi.org/10.1016/j.patrec.2003.10.015
Patil, S., Naik, A., Sequeira, M., Naik, G.& Parab, J. (2022). An Algorithm for Pre-processing of Areca Nut for Quality Classification, in: Chen, J.I.-Z., Tavares, JMRS, Iliyasu, A.M. Du, K.-L. (Eds.), Second International Conference on Image Processing and Capsule Networks. Springer International Publishing, Cham, pp. 79–93. https://doi.org/10.1007/978-3-030-84760-9_8
Patil, S., Naik, A., Sequeira, M.& Parab, J. (2023). A De-husked Areca Nut Classification Algorithm Based on 10-Fold Cross-Validation of Convolutional Neural Network, in: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (Eds.), Advanced Network Technologies and Intelligent Computing. Springer Nature Switzerland, Cham, pp. 35–45. https://doi.org/10.1007/978-3-031-28183-9_3
Shedthi B, S., Siddappa, M., Shetty, S.& Shetty, V. (2023). Classification of areca nut using machine learning techniques. IJECE 13, 1914. https://doi.org/10.11591/ijece.v13i2.pp1914-1921
Siddesha, S., Niranjan, S.K.& Aradhya, V.N.M. (2015). Texture based classification of areca nut. International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) 688–692.
Swets, J.A. (1979). ROC analysis applied to the evaluation of medical imaging techniques. Investigative radiology 14, 109--121.
Veerakumar, T., Esakkirajan, S.& Vennila, I. (2014). Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images. AEU - International Journal of Electronics and Communications 68, 442–452. https://doi.org/10.1016/j.aeue.2013.11.008
Visa, S., Ramsay, Brian, Ralescu, A.L.& Esther, van der K. (2011). Confusion Matrix-based Feature Selection, in: MAICS, CEUR Workshop Proceedings. CEUR-WS.org, Cincinnati, Ohio, USA, pp. 120–127.
Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross-validation. Pattern Recognition 48, 2839–2846. https://doi.org/10.1016/j.patcog.2015.03.009
Yakno, M., Mohamad-Saleh, J.& Ibrahim, M.Z. (2021). Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma. Sensors 21, 6445. https://doi.org/10.3390/s21196445
Yamashita, R., Nishio, M., Do, R.K.G. & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629. https://doi.org/10.1007/s13244-018-0639-9
Yu-Dong Zhang& Sangaiah, A. (2022). Cognitive systems and signal processing in image processing, Cognitive data science in sustainable computing. Elsevier, Academic Press, London, United Kingdom.
Zeiler, M.D.& Fergus, R. (2014). Visualizing and Understanding Convolutional Networks, in: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (Eds.), Computer Vision – ECCV 2014. Springer International Publishing, Cham, pp. 818–833. https://doi.org/10.1007/978-3-319-10590-1_53
Ayoub Shaikh, T., Rasool, T. & Rasheed Lone, F. (2022). Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming. Computers and Electronics in Agriculture, 198, 107119. https://doi.org/10.1016/j.compag.2022.107119
Ramachandran M (2014), Heart and Toxins. Academic Press, Boston
Bharadwaj, N.K., Dinesh, R., & N, Vinay Kumar (2021). Classification and Grading of Arecanut Using Texture-Based Block-Wise Local Binary Patterns. TURCOMAT Vol.12, 575–586. https://doi.org/10.17762/turcomat.v12i11.5931
Boutaba, R., Salahuddin, M.A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F. & Caicedo, O.M. (2018). A comprehensive survey on machine learning for networking: evolution, applications, and research opportunities. J Internet Serv Appl 9, 16. https://doi.org/10.1186/s13174-018-0087-2
Cai, H.& Liu, S. (2019). Betel Nut Classification Method Based on Transfer Learning, in: 2019 IEEE 8th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, Dali, China, pp. 1039–1043. https://doi.org/10.1109/DDCLS.2019.8909008
Carbonero-Ruz, M., Martínez-Estudillo, F.J., Fernández-Navarro, F., Becerra-Alonso, D., Martínez-Estudillo, Alfonso Carlos. (2017). A two dimensional accuracy-based measure for classification performance. Information Sciences 382–383, 60–80. https://doi.org/10.1016/j.ins.2016.12.005
Chandrashekhara, H.& Suresha, M. (2019). Classification of Healthy and Diseased Arecanuts using SVM Classifier. ijcse 7, 544–548. https://doi.org/10.26438/ijcse/v7i2.544548
Danti, A.& Suresha (2012). Segmentation and Classification of Raw Arecanuts Based on Three Sigma Control Limits. Procedia Technology 4, 215–219. https://doi.org/10.1016/j.protcy.2012.05.032
Edan, Y., Han, S.& Kondo, N. (2009). Automation in Agriculture, in: Nof, S.Y. (Ed.), Springer Handbook of Automation. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1095–1128. https://doi.org/10.1007/978-3-540-78831-7_63
Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861–874. https://doi.org/10.1016/j.patrec.2005.10.010
Ferri, C., Hernández-Orallo, J.& Modroiu, R. (2009). An experimental comparison of performance measures for classification. Pattern Recognition Letters 30, 27–38. https://doi.org/10.1016/j.patrec.2008.08.010
Goodfellow, I., Bengio, Y.& Courville, A. (2016). Deep Learning, Adaptive Computation and Machine Learning series. The MIT Press.
Hajian-Tilaki, K., (2013). Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. Caspian J Intern Med 4, 627–635.
Hameed, K., Chai, D.& Rassau, A. (2018). A comprehensive review of fruit and vegetable classification techniques. Image and Vision Computing 80, 24–44. https://doi.org/10.1016/j.imavis.2018.09.016
Hanley, J.A.& McNeil, B.J. (1982). The meaning and use of the Area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36. https://doi.org/10.1148/radiology.143.1.7063747
Huang, K.-Y. (2012). Detection and classification of areca nuts with machine vision. Computers & Mathematics with Applications 64, 739–746. https://doi.org/10.1016/j.camwa.2011.11.041
Jia, W., Tian, Y., Luo, R., Zhang, Z., Lian, J.& Zheng, Y., (2020). Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot. Computers and Electronics in Agriculture 172, 105380. https://doi.org/10.1016/j.compag.2020.105380
Jyothi K, Sindhu M Hegde, Sumedha K S, Sushma C R& Thanushree DC (2022). Grading of Arecanut Using Machine Learning Techniques. IJSRCSEIT 33–39. https://doi.org/10.32628/CSEIT2283119
Kamble, V.H.& Dale, M.P. (2022). Machine learning approach for longitudinal face recognition of children, in: Machine Learning for Biometrics. Elsevier, pp. 1–27. https://doi.org/10.1016/B978-0-323-85209-8.00011-0
Kamilaris, A.& Prenafeta-Boldú, F.X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture 147, 70–90. https://doi.org/10.1016/j.compag.2018.02.016
Khoshdeli, M., Cong, R.& Parvin, B. (2017). Detection of nuclei in H&E stained sections using convolutional neural networks, in: 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, Orland, FL, USA, pp. 105–108. https://doi.org/10.1109/BHI.2017.7897216
Krizhevsky, A., Sutskever, I.& Hinton, G.E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Commun. ACM 60, 84–90. https://doi.org/10.1145/3065386
Lanjewar, M.G., Parab, J.S.& Shaikh, A.Y. (20232). Development of framework by combining CNN with KNN to detect Alzheimer’s disease using MRI images. Multimed Tools Appl 82, 12699–12717. https://doi.org/10.1007/s11042-022-13935-4
LeCun, Y., Bengio, Y.& Hinton, G. (2015). Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539
Madani, A., Arnaout, Ramy, Mofrad, M.& Arnaout, Rima (2018). Fast and accurate view classification of echocardiograms using deep learning. npj Digital Med 1, 6. https://doi.org/10.1038/s41746-017-0013-1
Mai, X., Zhang, H., Jia, X.& Meng, M.Q.-H. (2020). Faster R-CNN With Classifier Fusion for Automatic Detection of Small Fruits. IEEE Trans. Automat. Sci. Eng. 1–15. https://doi.org/10.1109/TASE.2020.2964289
Mallaiah, S., Danti, A.& Narasimhamurthy, S.K. (2014). Classification of Diseased Arecanut based on Texture Features. IJCA, Recent Advances in Information Technology 1, 1–6.
Mallikarjuna, S.B., Shivakumara, P., Khare, V., Kumar N, V., Basavanna, M.,., Pal, U.& Poornima, B.. (2021). CNN BASED METHOD FOR MULTI-TYPE DISEASED ARECANUT IMAGE CLASSIFICATION. MJCS 34, 255–265. https://doi.org/10.22452/mjcs.vol34no3.3
Meghana D R& Prabhudeva S (2022). Image Processing based Arecanut Diseases Detection Using CNN Model. IJARSCT 747–752. https://doi.org/10.48175/IJARSCT-5154
Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A.& Doulamis, N. (2021). Multi-class Confusion Matrix Reduction Method and Its Application on Net Promoter Score Classification Problem. Technologies 9, 81. https://doi.org/10.3390/technologies9040081
Moss, W.J. (2022). The Seeds of Ignorance — Consequences of a Booming Betel-Nut Economy. N Engl J Med 387, 1059–1061. https://doi.org/10.1056/NEJMp2203571
Naranjo-Torres, J., Mora, M., Hernández-García, R., Barrientos, R.J., Fredes, C.& Valenzuela, A. (2020). A Review of Convolutional Neural Network Applied to Fruit Image Processing. Applied Sciences 10, 3443. https://doi.org/10.3390/app10103443
Nti, I.K., Nyarko-Boateng, O. &Aning, J. (2021)., Performance of Machine Learning Algorithms with Different K Values in K-fold Cross Validation. IJITCS 13, 61–71. https://doi.org/10.5815/ijitcs.2021.06.05
Park, S.B., Lee, J.W.& Kim, S.K. (2004). Content-based image classification using a neural network. Pattern Recognition Letters 25, 287–300. https://doi.org/10.1016/j.patrec.2003.10.015
Patil, S., Naik, A., Sequeira, M., Naik, G.& Parab, J. (2022). An Algorithm for Pre-processing of Areca Nut for Quality Classification, in: Chen, J.I.-Z., Tavares, JMRS, Iliyasu, A.M. Du, K.-L. (Eds.), Second International Conference on Image Processing and Capsule Networks. Springer International Publishing, Cham, pp. 79–93. https://doi.org/10.1007/978-3-030-84760-9_8
Patil, S., Naik, A., Sequeira, M.& Parab, J. (2023). A De-husked Areca Nut Classification Algorithm Based on 10-Fold Cross-Validation of Convolutional Neural Network, in: Woungang, I., Dhurandher, S.K., Pattanaik, K.K., Verma, A., Verma, P. (Eds.), Advanced Network Technologies and Intelligent Computing. Springer Nature Switzerland, Cham, pp. 35–45. https://doi.org/10.1007/978-3-031-28183-9_3
Shedthi B, S., Siddappa, M., Shetty, S.& Shetty, V. (2023). Classification of areca nut using machine learning techniques. IJECE 13, 1914. https://doi.org/10.11591/ijece.v13i2.pp1914-1921
Siddesha, S., Niranjan, S.K.& Aradhya, V.N.M. (2015). Texture based classification of areca nut. International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) 688–692.
Swets, J.A. (1979). ROC analysis applied to the evaluation of medical imaging techniques. Investigative radiology 14, 109--121.
Veerakumar, T., Esakkirajan, S.& Vennila, I. (2014). Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images. AEU - International Journal of Electronics and Communications 68, 442–452. https://doi.org/10.1016/j.aeue.2013.11.008
Visa, S., Ramsay, Brian, Ralescu, A.L.& Esther, van der K. (2011). Confusion Matrix-based Feature Selection, in: MAICS, CEUR Workshop Proceedings. CEUR-WS.org, Cincinnati, Ohio, USA, pp. 120–127.
Wong, T.-T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross-validation. Pattern Recognition 48, 2839–2846. https://doi.org/10.1016/j.patcog.2015.03.009
Yakno, M., Mohamad-Saleh, J.& Ibrahim, M.Z. (2021). Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma. Sensors 21, 6445. https://doi.org/10.3390/s21196445
Yamashita, R., Nishio, M., Do, R.K.G. & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629. https://doi.org/10.1007/s13244-018-0639-9
Yu-Dong Zhang& Sangaiah, A. (2022). Cognitive systems and signal processing in image processing, Cognitive data science in sustainable computing. Elsevier, Academic Press, London, United Kingdom.
Zeiler, M.D.& Fergus, R. (2014). Visualizing and Understanding Convolutional Networks, in: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (Eds.), Computer Vision – ECCV 2014. Springer International Publishing, Cham, pp. 818–833. https://doi.org/10.1007/978-3-319-10590-1_53
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How to Cite
Efficient Deep Learning model for de-husked Areca nut classification. (2023). Journal of Applied and Natural Science, 15(4), 1529-1540. https://doi.org/10.31018/jans.v15i4.5067
