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Avocado fruit disease detection and classification using modified SCA–PSO algorithm-based MobileNetV2 convolutional neural network

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Abstract

Classification of diseases in fruits is a comparatively multifaceted problem due to varieties, which include irregular shapes, colors, and textures. Deep learning plays a vital role in the classification of diseases from fruits. The agricultural productivity of fruit highly depends on the economic growth of Ethiopia. The production of avocado fruit enriches the economy of farmers in Ethiopia. Improper care causes severe possessions on avocado fruit productivity. The automatic classification of avocado diseases from the healthy fruit will reduce the extensive work of monitoring the big farms. This research presents a Modified Sine Cosine Algorithm–Particle Swarm Optimization (MSCA–PSO)-based MobileNetV2 Convolutional Neural Network (CNN) model for detecting and classifying avocado fruit diseases into healthy and non-healthy diseases from the images. This research considers the Avocado fruit disease image database as input to the proposed model. It is proposed to identify and detect the diseases by taking the high-resolution image database “Fruits 360 dataset” and real images collected from different agricultural farms in Ethiopia. Further, to show the robustness of the proposed hybrid MSCA–PSO algorithm, three benchmark functions, such as the Rastrigin function, Griewanks’s function, and Sphere function, are considered, and the results are presented. The proposed Modified SCA–PSO-based MobileNetV2 convolutional neural network model obtained an accuracy of 98.42% compared to the conventional CNN models, and the comparison results are presented.

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Data availability

The real avocado disease-related image data are collected from the various agricultural farms of weredas (districts) of Jima and Illubabor Zone of Southwestern Ethiopia: Mana, Goma, Seka, Metu, and research organizations of Ethiopia. The data have also been collected from the Avocado “Fruits 360 dataset”.

References

  1. De, C.A.I., Ehsani, R., Ploetz, R.C., Crane, J.H., Buchanon, S.: Detection of laurel wilt disease in avocado using low altitude aerial imaging. PLoS One 10(4), 1–13 (2015). https://doi.org/10.1371/journal.pone.0124642

    Article  Google Scholar 

  2. Abdulridha, J., Ehsani, R., de Castro, A.: Detection and differentiation between Laurel wilt disease, phytophthora disease, and salinity damage using a hyper-spectral sensing teschnique. Agriculture 6(4), 1–13 (2016). https://doi.org/10.3390/agriculture6040056

    Article  Google Scholar 

  3. Jaafar, A., Yiannis, A., Reza, E., de Castro, I.: Evaluating the performance of spectral features and multivariate analysis tools to detect laurel wilt disease and nutritional deficiency in avocado. Comput. Electron. Agric. 155, 203–211 (2018). https://doi.org/10.1016/j.compag.2018.10.016

    Article  Google Scholar 

  4. Abdulridha, J., Ehsani, R., Abd-Elrahman, A.: A remote sensing technique for detecting laurel wilt disease in avocado in presence of other biotic and abiotic stresses. J. Electron. Comput. Electron. Agric. Elsevier 156, 549–557 (2019). https://doi.org/10.1016/j.compag.2018.12.018

    Article  Google Scholar 

  5. Jeanette, H., John, F., Yiannis, A., Jaafar, A., Andrew, L.: Finite difference analysis and bivariate correlation of hyperspectral data for detecting laurel wilt disease and nutritional deficiency in avocado. Remote Sens. 11, 1748 (2019). https://doi.org/10.3390/rs11151748

    Article  Google Scholar 

  6. Bhargava A, Bansal A. Food Anal. Methods (2019). https://doi.org/10.1007/s12161-019-01690-6

  7. Jaramillo-Acevedo, C.A., Choque-Valderrama, W.E., Guerrero-Álvarez, G.E., Meneses-Escobar, C.A.: Hass avocado ripeness classification by mobile devices using digital image processing and ANN methods. Int. J. Food Eng. (2020). https://doi.org/10.1515/ijfe-2019-0161

    Article  Google Scholar 

  8. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L. C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proc. IEEE conference on computer vision and pattern recognition, 4510–4520 (2018)

  9. Mai, X., Zhang, H., Jia, X., Meng, M.Q.: Faster R-CNN with classifier fusion for automatic detection of small fruits. IEEE Trans. Autom. Sci. Eng. Autom. Sci. Eng. 17(3), 1555–1569 (2020). https://doi.org/10.1109/TASE.2020.2964289

    Article  Google Scholar 

  10. Oppenheim D., Shani G.: Potato disease classification using convolution neural networks”, volume 8, special issue 2. In: 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20, July 2017 , 244–249 (2017)

  11. Yang, L., Yi, S., Nianyin, Z., Yurong, L., Yong, Z.: Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267, 378–384 (2017)

    Article  Google Scholar 

  12. Bhardwaj, A., Hasteer, N., Kumar, Y., Yogesh: Deep learning based fruit defect detection system. In: Mekhilef, S., Shaw, R.N., Siano, P. (eds) Innovations in electrical and electronic engineering. ICEEE 2022. Lecture Notes in Electrical Engineering, vol 894. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-1677-9_28

  13. Thangarasu, R., Dinesh, D., Hariharan, S., Rajendar, S., Gokul, D., Hariskarthi, T.: Automatic recognition of avocado fruit diseases using modified deep convolutional neural network. Int. J. Grid Distrib. Comput. 13(1), 1550–1559 (2020)

    Google Scholar 

  14. Ramirez, O. J. V., Cruz, J. E., de la Cruz Machaca, W. A. M.: Agroindustrial plant for the classification of hass avocados in real-time with ResNet-18 architecture. In: 2021 5th international conference on robotics and automation sciences (ICRAS), 206–210 (2021). https://doi.org/10.1109/ICRAS52289.2021.9476659

  15. Valiente, L. D., Parco, K. M. R., Sangalang, G. C. P.: Non-destructive image processing analysis for defect identification and maturity detection on avocado fruit. In: 2021 5th International Conference on Communication and Information Systems (ICCIS), 175–179 (2021). https://doi.org/10.1109/ICCIS53528.2021.9645970

  16. Shahi, T.B., Sitaula, C., Neupane, A., Guo, W.: Fruit classification using attention-based MobileNetV2 for industrial applications. PLoS One 17(2), e0264586 (2022). https://doi.org/10.1371/journal.pone.0264586. (PMID: 35213643; PMCID: PMC8880666)

    Article  Google Scholar 

  17. Guan, Q., Wan, X., Lu, H., Ping, B., Li, D., Wang, L., Zhu, Y., Wang, Y., Xiang: Deep convolutional neural network Inceptionv3 model for differential diagnosing of lymph node in cytological images: a pilot study. J. Ann. Transl. Med. 7(14):307 (2019). https://doi.org/10.21037/atm.2019.06.29

  18. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. IEEE Conf. Comput. Vis. Pattern Recogn. (CVPR) 2016, 2818–2826 (2016)

    Google Scholar 

  19. Joshi, K., Tripathi, V., Bose, C., Bhardwaj, C.: Robust sports image classification using inceptionV3 and neural networks. Proc. Comput. Sci. 167, 2374–2381 (2020). https://doi.org/10.1016/j.procs.2020.03.290. (ISSN 1877–0509)

    Article  Google Scholar 

  20. Chugh, G., Sharma, A., Choudhary, P., Khanna, R.: Potato leaf disease detection using inception V3. Int. Res. J. Eng. Technol (IRJET) 7(11), 1363–1366 (2020)

    Google Scholar 

  21. Aseffa, D., Kalla, H., Mishra, S.: Ethiopian banknote recognition using convolutional neural network and its prototype development using embedded platform. J. Sens. (2022). https://doi.org/10.1155/2022/4505089

    Article  Google Scholar 

  22. Pranali Kosamkar K., Kulkarni V.Y., Mantri K., Rudrawar, S., Salmpuria, S., Gadekar N.: Leaf disease detection and recommendation of pesticides using convolution neural network. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) (2018). https://doi.org/10.1109/ICCUBEA.2018.8697504

  23. Rudnik, K., Michalski, P.: A vision-based method utilizing deep convolutional neural networks for fruit variety classification in uncertainty conditions of retail sales. Appl. Sci. 9, 3971 (2019). https://doi.org/10.3390/app9193971

    Article  Google Scholar 

  24. Xiang Q., Wang X., Li R., Zhang G., Lai J., Hu Q.: Fruit Image Classification Based on MobileNetV2 with Transfer Learning Technique. In: CSAE 2019: Proceedings of the 3rd International Conference on Computer Science and Application Engineering October 2019 Article No.: 121, Pages 1–7 (2019).https://doi.org/10.1145/3331453.3361658

  25. Mishra, S., Sahu, P., Senapati, M.R.: MASCA-PSO based LLRBFNN model and improved fast and robust FCM algorithm for detection and classification of brain tumor from MR image. Evol. Intel. 12, 647–663 (2019). https://doi.org/10.1007/s12065-019-00266-x

    Article  Google Scholar 

  26. Mishra, S., Gelmecha, D.J., Singh, R.S., Rathee, D.S., Gopikrishna, T.: Hybrid WCA–SCA and modified FRFCM technique for enhancement and segmentation of brain tumor from magnetic resonance images. Biomed. Eng. Appl. Basis Commun. (2021). https://doi.org/10.4015/S1016237221500174. (World Scientific)

    Article  Google Scholar 

  27. Barzegar, A., Sadollah, A., Su, R.: A novel fully informed water cycle algorithm for solving optimal power flow problems in electric grids. Optim. Control Syst. Control (2019). https://arxiv.org/abs/1909.08800.

  28. Xiang Q., Wang X., Li R., Zhang G., Lai J., Hu Q.: Fruit Image classification based on mobilenetv2 with transfer learning technique. In: CSAE 2019: Proceedings of the 3rd International Conference on Computer Science and Application Engineering October 2019, 121, 1–7 (2019). https://doi.org/10.1145/3331453.3361658

  29. Oltean, M.: Fruits 360 dataset. Mendeley Data (2018). https://doi.org/10.17632/rp73yg93n8.1

    Article  Google Scholar 

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Funding

This research is self-funded by Adama and Science and Technology University, Adama, Ethiopia.

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Authors and Affiliations

  1. Department of ECE, Adama Science and Technology University, Adama, Ethiopia

    Satyasis Mishra, Tadesse Hailu Ayane, V. Ellappan, Davinder Singh Rathee & Harish Kalla

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  1. Satyasis Mishra
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  2. Tadesse Hailu Ayane
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  3. V. Ellappan
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  4. Davinder Singh Rathee
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Contributions

SM prepared the documentation and methodology part of the research. THA prepared the document as per the journal format and also helped in collecting and preprocessing the data. VE complied research diagram, and python programs are compiled. DSR collected data from different parts of Ethiopia. HK prepared all simulation work and all figures with the GPU system. All authors reviewed the manuscript.

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Correspondence to Satyasis Mishra.

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Authors of the paper Satyasis Mishra, Tadesse Hailu Ayane, Ellappan V., and Harish Kalla declare no conflict of interest or financial conflicts.

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Mishra, S., Ayane, T.H., Ellappan, V. et al. Avocado fruit disease detection and classification using modified SCA–PSO algorithm-based MobileNetV2 convolutional neural network. Iran J Comput Sci 5, 345–358 (2022). https://doi.org/10.1007/s42044-022-00116-7

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