Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network
DOI:
https://doi.org/10.32890/jict2022.21.3.4Keywords:
AlexNet, convolutional neural network, leaf disease, oilpalm, support vector machineAbstract
Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the manual extraction of different discriminative features before the classification models can be developed. In this study, deep learning models, specifically, the AlexNet convolutional neural network (CNN) and the combination of AlexNet and support vector machine (AlexNet-SVM), which overcome the limitation of handcrafting of feature representation were implemented for oil palm leaf disease identification. The images of healthy and infected leaf samples were collected, resized, and renamed before the model training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in models, without the need for segmentation and feature extraction as in the conventional machine learning methods. The optimal architecture of AlexNet CNN and AlexNet-SVM models were then determined and subsequently applied for the oil palm leaf disease identification.Comparative studies showed that the overall performance of the AlexNet CNN model outperformed AlexNet-SVM-based classifier.
References
Abdu, A. M., Mokji, M. M., & Sheikh, U. U. (2020). Automatic vegetable disease identification approach using individual lesion features. Computers and Electronics in Agriculture, 176, 105660. https://doi.org/https://doi.org/10.1016/j. compag.2020.105660
Abdulridha, J., Ampatzidis, Y., Kakarla, S. C., & Roberts, P. (2020). Detection of target spot and bacterial spot diseases in tomato using UAV-based and benchtop-based hyperspectral imaging techniques [Article]. Precision Agriculture, 21(5), 955–978. https://doi.org/10.1007/s11119-019-09703-4
Aji, A. F., Munajat, Q., Pratama, A. P., Kalamullah, H., Setiyawan, J., & Arymurthy, A. M. (2013). Detection of palm oil leaf disease with image processing and neural network classification on mobile device. International Journal of Computer Theory and Engineering, 5(3), 528.
Alaa, H., Waleed, K., Samir, M., Tarek, M., Sobeah, H., & Salam, M. A. (2020). An intelligent approach for detecting palm trees diseases using image processing and machine learning. Int. J. Adv. Comput. Sci. Appl., 11(7), 434–441.
Almabdy, S., & Elrefaei, L. (2019). Deep convolutional neural network-based approaches for face recognition. Applied Sciences, 9(20), 4397.
Ampatzidis, Y., Cruz, A., Pierro, R., Materazzi, A., Panattoni, A., De Bellis, L., & Luvisi, A. (2018). Vision-based system for Journal of ICT, 21, No. 3 (July) 2022, pp: 383– detecting grapevine yellow diseases using artificial intelligence. In XXX International Horticultural Congress IHC2018: VII Conference on Landscape and Urban Horticulture, IV Conference on 1279 (pp. 225–230).
Arjmand, A., Angelis, C. T., Christou, V., Tzallas, A. T., Tsipouras, M. G., Glavas, E.,...
Giannakeas, N. (2020). Training of deep convolutional neural networks to identify critical liver alterations in histopathology image samples. Applied Sciences, 10(1), 42.
Ashourloo, D., Aghighi, H., Matkan, A. A., Mobasheri, M. R., & Rad, A. M. (2016). An investigation into machine learning regression techniques for the leaf rust disease detection using hyperspectral measurement. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(9), 4344–4351, Article 7533506. https://doi.org/10.1109/ JSTARS.2016.2575360
Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393. https://doi.org/https://doi.org/10.1016/j. compag.2020.105393
Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, D. (2019). Deep neural networks with transfer learning in millet crop images. Computers in Industry, 108, 115–120.
Darwish, A., Ezzat, D., & Hassanien, A. E. (2020). An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm and Evolutionary Computation, 52, Article 100616. https://doi.org/10.1016/j.swevo.2019.100616
Elaraby, A., Hamdy, W., & Alanazi, S. (2022). Classification of citrus diseases using optimization deep learning approach. Computational Intelligence and Neuroscience, 2022, 9153207. https://doi.org/10.1155/2022/9153207
Elaraby, A., Hamdy, W., & Alruwaili, M. (2022). Optimization of deep learning model for plant disease detection using particle swarm optimizer. Computers, Materials & Continua, 71(2), 4019–4031. http://www.techscience.com/cmc/v71n2/45808
Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318.
Ghanei Ghooshkhaneh, N., Golzarian, M. R., & Mamarabadi, M. (2018). Detection and classification of citrus green mold caused Journal of ICT, 21, No. 3 (July) 2022, pp: 383– by Penicillium digitatum using multispectral imaging. Journal of the Science of Food and Agriculture, 98(9), 3542–3550. https://doi.org/10.1002/jsfa.8865
Guo, A., Huang, W., Ye, H., Dong, Y., Ma, H., Ren, Y., & Ruan, C. (2020). Identification of wheat yellow rust using spectral and texture features of hyperspectral images. Remote Sensing, 12(9), Article 1419. https://doi.org/10.3390/RS12091419
Habib, M. T., Majumder, A., Jakaria, A., Akter, M., Uddin, M. S., & Ahmed, F. (2020). Machine vision based papaya disease recognition. Journal of King Saud University-Computer and Information Sciences, 32(3), 300–309.
Hamdani, H., Septiarini, A., Sunyoto, A., Suyanto, S., & Utaminingrum, F. (2021). Detection of oil palm leaf disease based on color histogram and supervised classifier. Optik, 245, 167753. https://doi.org/https://doi.org/10.1016/j.ijleo.2021.167753
Han, Z., & Gao, J. (2019). Pixel-level aflatoxin detecting based on deep learning and hyperspectral imaging. Computers and Electronics in Agriculture, 164, 104888.
Hannan, M. A., How, D. N., Lipu, M. H., Ker, P. J., Dong, Z. Y., Mansur, M., & Blaabjerg, F. (2020). SOC estimation of li-ion batteries with learning rate-optimized deep fully convolutional network. IEEE Transactions on Power Electronics, 36(7), 7349–7353.
Hoffer, E., Hubara, I., & Soudry, D. (2017). Train longer, generalize better: Closing the generalization gap in large batch training of neural networks. Advances in Neural Information Processing Systems, 30.
Jacobs, R. A. (1988). Increased rates of convergence through learning rate adaptation. Neural Networks, 1(4), 295–307.
Jadhav, S. B., Udupi, V. R., & Patil, S. B. (2021). Identification of plant diseases using convolutional neural networks. International Journal of Information Technology, 13(6), 2461–2470.
Kawatra, M., Agarwal, S., & Kapur, R. (2020). Leaf disease detection using neural network hybrid models. In 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA) (pp. 225–230).
Khan, M. A., Akram, T., Sharif, M., & Saba, T. (2020). Fruits diseases classification: Exploiting a hierarchical framework for deep features fusion and selection. Multimedia Tools and Applications, 79(35), 25763–25783. https://doi.org/10.1007/ s11042-020-09244-3
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84–90. Journal of ICT, 21, No. 3 (July) 2022, pp: 383–
Li, X., Tang, Z., Chen, W., & Wang, L. (2019). Multimodal and multi-model deep fusion for fine classification of regional complex landscape areas using ZiYuan-3 imagery. Remote Sensing, 11(22), 2716.
Liu, J., & Wang, X. (2020). Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network [Original Research]. Frontiers in Plant Science, 11(898). https://doi.org/10.3389/fpls.2020.00898
Masazhar, A. N. I., & Kamal, M. M. (2017). Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier. In 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) (pp. 1–6).
Muhammad, N., Rubab, Bibi, N., Song, O.-Y., Khan, M. A., & Khan, S. A. (2021). Severity recognition of aloe vera diseases using AI in tensor flow domain. CMC-Computers Materials & Continua, 66(2), 2199–2216.
Pantazi, X. E., Moshou, D., & Tamouridou, A. A. (2019). Automated leaf disease detection in different crop species through image features analysis and one class classifiers. Computers and Electronics in Agriculture, 156, 96–104.
Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290. https://doi.org/https://doi.org/10.1016/j.compag.2018.04.002
Rashwan, S. A., & Elteir, M. K. (2022). Plant leaf disease detection using deep learning on mobile devices. International Journal of Computational Vision and Robotics, 12(2), 156–176.
Rath, A. K., & Meher, J. K. (2019). Disease detection in infected plant leaf by computational method. Archives of Phytopathology and Plant Protection, 52(19–20), 1348–1358. https://doi.org/10.10 80/03235408.2019.1708546
Roslan, A., & Idris, A. (2012). Economic impact of ganoderma incidence on Malaysian oil palm plantation–a case study in Johor. Oil Palm Industry Economic Journal, 12(1), 24–30.
Roy, S. S., Ahmed, M., & Akhand, M. A. H. (2018). Noisy image classification using hybrid deep learning methods. Journal of Information and Communication Technology, 17(2), 233–269.
Septiarini, A., Hamdani, H., Hardianti, T., Winarno, E., Suyanto, S., & Irwansyah, E. (2021). Pixel quantification and color feature extraction on leaf images for oil palm disease identification. In Journal of ICT, 21, No. 3 (July) 2022, pp: 383–2021 7th International Conference on Electrical, Electronics and Information Engineering (ICEEIE) (pp. 1–5).
Singh, R. K., Tiwari, A., & Gupta, R. K. (2022). Deep transfer modeling for classification of Maize Plant Leaf Disease. Multimedia Tools and Applications, 81(5), 6051–6067. https://doi.org/10.1007/s11042-021-11763-6
Skowron, M., Orlowska-Kowalska, T., Wolkiewicz, M., & Kowalski, C. T. (2020). Convolutional neural network-based stator current data-driven incipient stator fault diagnosis of inverter-fed induction motor. Energies, 13(6), 1475.
Sunpapao, A., Chairin, T., & Ito, S.-i. (2018). The biocontrol by streptomyces and trichoderma of leaf spot disease caused by Curvularia oryzae in oil palm seedlings. Biological Control, 123, 36–42.
Tang, M., Huang, Z., Yuan, Y., Wang, C., & Peng, Y. (2019). A bounded scheduling method for adaptive gradient methods. Applied Sciences, 9(17), 3569.
Türkoğlu, M., & Hanbay, D. (2019). Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering & Computer Sciences, 27(3), 1636–1651.
Veni, S., Vishnu Priya, P. M., Aishwarya Mala, G. M., Kayartaya, A., & Anusha, R. (2017). Computer aided system for detection and classification of brinjal leaf diseases using thermal and visible light images. Journal of Theoretical and Applied Information Technology, 95(19), 5224–5236. https://www.scopus.com/ inward/record.uri?eid=2-s2.0-85031745861&partnerID=40& md5=e3825478384efd8ea10593d779bf0b75
Yang, N., Qian, Y., El-Mesery, H. S., Zhang, R., Wang, A., & Tang, J. (2019). Rapid detection of rice disease using microscopy image identification based on the synergistic judgment of texture and shape features and decision tree–confusion matrix method. Journal of the Science of Food and Agriculture, 99(14), 6589–6600. https://doi.org/10.1002/jsfa.9943
Yuan, L., Yan, P., Han, W., Huang, Y., Wang, B., Zhang, J.,...
Bao, Z. (2019). Detection of anthracnose in tea plants based on hyperspectral imaging. Computers and Electronics in Agriculture, 167, 105039. https://doi.org/https://doi.org/10.1016/j.compag.2019.105039
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