A Survey of Deep Learning Techniques for Maize Leaf Disease Detection: Trends from 2016 to 2021 and Future Perspectives

Nunoo-Mensah, H.; Kuseh, S. Wewoliamo; Yankey, J.; Acheampong, F. A.

doi:10.22061/jecei.2022.8602.531

فهرست نشریات

دانشگاه تربیت دبیر شهید رجائی

انتشارات دانشگاه تربیت دبیر شهید رجائی

نشریات مستقل دانشگاه در سامانه ارزیابی نشریات علمی وزارت علوم

نشریه معماری وشهرسازی پایدار موفق به اخذ رتبه علمی-پژوهشی شد

تعداد نشریات	11
تعداد شماره‌ها	210
تعداد مقالات	2,098
تعداد مشاهده مقاله	2,877,477
تعداد دریافت فایل اصل مقاله	2,085,252

	A Survey of Deep Learning Techniques for Maize Leaf Disease Detection: Trends from 2016 to 2021 and Future Perspectives
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقاله 11، دوره 10، شماره 2، مهر 2022، صفحه 381-392 اصل مقاله (823.07 K)
نوع مقاله: Review paper
شناسه دیجیتال (DOI): 10.22061/jecei.2022.8602.531
نویسندگان
H. Nunoo-Mensah^* ¹؛ S. Wewoliamo Kuseh¹؛ J. Yankey¹؛ F. A. Acheampong²
¹Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
²Department of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
تاریخ دریافت: 23 آبان 1400، تاریخ بازنگری: 15 بهمن 1400، تاریخ پذیرش: 19 بهمن 1400
چکیده
Background and Objectives: To a large extent, low production of maize can be attributed to diseases and pests. Accurate, fast, and early detection of maize plant disease is critical for efficient maize production. Early detection of a disease enables growers, breeders and researchers to effectively apply the appropriate controlled measures to mitigate the disease’s effects. Unfortunately, the lack of expertise in this area and the cost involved often result in an incorrect diagnosis of maize plant diseases which can cause significant economic loss. Over the years, there have been many techniques that have been developed for the detection of plant diseases. In recent years, computer-aided methods, especially Machine learning (ML) techniques combined with crop images (image-based phenotyping), have become dominant for plant disease detection. Deep learning techniques (DL) have demonstrated high accuracies of performing complex cognitive tasks like humans among machine learning approaches. This paper aims at presenting a comprehensive review of state-of-the-art DL techniques used for detecting disease in the leaves of maize. Methods: In achieving the aims of this paper, we divided the methodology into two main sections; Article Selection and Detailed review of selected articles. An algorithm was used in selecting the state-of-the-art DL techniques for maize disease detection spanning from 2016 to 2021. Each selected article is then reviewed in detail taking into considerations the DL technique, dataset used, strengths and limitations of each technique. Results: DL techniques have demonstrated high accuracies in maize disease detection. It was revealed that transfer learning reduces training time and improves the accuracies of models. Models trained with images taking from a controlled environment (single leaves) perform poorly when deployed in the field where there are several leaves. Two-stage object detection models show superior performance when deployed in the field. Conclusion: From the results, lack of experts to annotate accurately, Model architecture, hyperparameter tuning, and training resources are some of the challenges facing maize leaf disease detection. DL techniques based on two-stage object detection algorithms are best suited for several plant leaves and complex backgrounds images.
کلیدواژه‌ها
Convolutional Neural Network؛ Deep Learning؛ Maize Disease؛ Transfer Learning

مراجع
[1] C.A. Wongnaa, D. Awunyo-Vitor, A. Mensah, F. Adams, “Profit efficiency among maize farmers and implications for poverty alleviation and food security in ghana,” Sci. Afr., 6: e00206, 2019. [2] B. Shiferaw, B. M. Prasanna, J. Hellin, M. Banziger, “Crops that feed the world 6. past successes and future challenges to the role played by maize in global food security,” Food Secur., 3(3): 307, 2011. [3] S.P. Mohanty, D.P. Hughes, M. Salathe, “Using deep learning for image-based plant disease detection,” Front. plant scie., 7: 1419, 2016. [4] C. Manjunath, H. Karthik, N. Reddy, S. Sahana, et al., “A review on detection of plant diseases through various methodologies,” Int. J. Res. Appl. Scie. Eng. Tech., 6(5): 74–80, 2018. [5] A. Singh, B. Ganapathysubramanian, A.K. Singh, S. Sarkar, “Machine learning for high-throughput stress phenotyping in plants,” Trends plant scie., 21(2): 110–124, 2016. [6] F. Martinelli, R. Scalenghe, S. Davino, S. Panno, G. Scuderi, P. Ruisi, P. Villa, D. Stroppiana, M. Boschetti, L.R. Goulart, et al., “Advanced methods of plant disease detection. a review,” Agron. Sustainable Dev., 35(1): 1–25, 2015. [7] S. Ghosal, D. Blystone, A.K. Singh, B. Ganapathysubramanian, A. Singh, S. Sarkar, “An explainable deep machine vision framework for plant stress phenotyping,” PNAS, 115(18): 4613–4618, 2018. [8] C. DeChant, T. Wiesner-Hanks, S. Chen, E.L. Stewart, J. Yosinski, M.A. Gore, R.J. Nelson, H. Lipson, “Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning,” Phytopathology, 107(11): 1426 1432, 2017. [9] A.K. Mahlein, “Plant disease detection by imaging sensors– parallels and specific demands for precision agriculture and plant phenotyping,” Plant dis., 100(2): 241–251,2016. [10] W. J. Liang, H. Zhang, G.f. Zhang, H.x. Cao, “Rice blast disease recognition using a deep convolutional neural network,” Sci. rep., 9(1): 1–10, 2019. [11] A. Singh, B. Ganapathysubramanian, A.K. Singh, S. Sarkar, “Machine learning for high-throughput stress phenotyping in plants,” Trends plant sci., vol. 21, no. 2, pp. 110–124, 2016. [12] R.I. Hasan, S.M. Yusuf, L. Alzubaidi, “Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion,” Plants, 9(10): 1302, 2020. [13] A.K. Singh, B. Ganapathysubramanian, S. Sarkar, A. Singh “Deep learning for plant stress phenotyping: trends and future perspectives,” Trends plant sci., 23(10): 883–898, 2018. [14] A. Kamilaris, F.X. Prenafeta-Boldu, “Deep learning in agricul- ture: A survey,” Comput. Electron. Agric., 147: 70–90, 2018. [15] F. Chollet et al., Deep learning with Python, vol. 361. Manning New York, 2018 [16] J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, Y. Wei, “Deformable convolutional networks,” in Proc. IEEE International Conference On Computer Vision: 764–773, 2017. [17] N.K. Manaswi, N.K. Manaswi, S. John, Deep learning with applications using python. Springer, 2018. [18] 2020 [Online]. Available: https://missinglink.ai/guides/convolutional-neuralnetworks/convolutional-neural-network-architectureforging-pathways-future/.. [19] M.Z. Alom, T.M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M.S. Nasrin, M. Hasan, B.C. Van Essen, A.A. Awwal, V. K. Asari, “A state-of-the-art survey on deep learning theory and architectures,” Electronics, 8(3): 292, 2019. [20] I.Hadji, R.P. Wildes, “What do we understand about convo lutional networks?,” arXiv preprint arXiv:1803.08834, 2018. [21] K. Weiss, T.M. Khoshgoftaar, D. Wang, “A survey of transfer learning,” J. Big data, 3(1): 1–40, 2016. [22] B. Tan, Y. Zhang, S. Pan, Q. Yang, “Distant domain transfer learning,” in Proc. the AAAI Conference on Artificial Intelli gence, 2017. [23] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, “Going deeper with convolutions,” in Proc. IEEE conference on computer vision and pattern recognition: 1–9, 2015. [24] A. Krizhevsky, I. Sutskever, G.E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. neural inf. Process. Syst., 25(2): 1097–1105, 2012. [25] K. He, X. Zhang, S. Ren, J. Sun, “Deep residual learning for image recognition,” in Proc. the IEEE conference on computer vision and pattern recognition: 770–778, 2016. [26] K. Simonyan, A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014. [27] G. Huang, Z. Liu, L. Van Der Maaten, K.Q. Weinberger, “Densely connected convolutional networks,” in Proc. the IEEE conference on computer vision and pattern recognition: 4700–4708, 2017. [28] M. Tan, Q. Le, “Efficientnet: Rethinking model scaling for con volutional neural networks,” in Proc. International Conference on Machine Learning (PMLR): 6105–6114, 2019. [29] D. Hughes, M. Salathe, et al., “An open access repository of images on plant health to enable the development of mobile disease diagnostics,” arXiv preprint arXiv:1511.08060, 2015. [30] D. Singh, N. Jain, P. Jain, P. Kayal, S. Kumawat, N. Batra, “Plantdoc: a dataset for visual plant disease detection,” in Proc. the 7th ACM IKDD CoDS and 25th COMAD: 249–253, 2020. [31] B. Richey, M.V. Shirvaikar, “Deep learning based real-time detection of northern corn leaf blight crop disease using yolov4,” in Proc. Real-Time Image Processing and Deep Learning: 1173606, 2021. [32] S. Ghose, “Corn or maize leaf disease dataset,” Nov 2020. [33] B. Gokulnath, G. Usha Devi, “A survey on plant disease prediction using machine learning and deep learning techniques,” Inteligencia Artificial, 23(65): 136-154, 2020. [34] A.K. Singh, B. Ganapathysubramanian, S. Sarkar, and A. Singh, “Deep learning for plant stress phenotyping: trends and future perspectives,” Trends plant sci., 23(10): 883–898, 2018. [35] A. Singh, B. Ganapathysubramanian, A.K. Singh, S. Sarkar, “Machine learning for high-throughput stress phenotyping in plants,” Trends plant sci., 21(2): 110–124, 2016. [36] B. Richey, S. Majumder, M. Shirvaikar, N. Kehtarnavaz, “Real time detection of maize crop disease via a deep learning-based smartphone app,” in Proc. Real-Time Image Processing and Deep Learning: 114010A, 2020. [37] J.G. Esgario, R.A. Krohling, J.A. Ventura, “Deep learning for classification and severity estimation of coffee leaf biotic stress,” Comput. Electron. Agric., 169: 105162, 2020. [38] X. Zhang, Y. Qiao, F. Meng, C. Fan, M. Zhang, “Identification of maize leaf diseases using improved deep convolutional neural networks,” IEEE Access, 6: 30370–30377, 2018. [39] H. Wu, T. Wiesner-Hanks, E.L. Stewart, C. DeChant, N. Kaczmar, M.A. Gore, R.J. Nelson, H. Lipson, “Autonomous detection of plant disease symptoms directly from aerial imagery,” Plant Phenome J., 2(1): 1–9, 2019. [40] K.P. Panigrahi, A.K. Sahoo, H. Das, “A CNN approach for corn leaves disease detection to support digital agricultural system,” in Proc. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184): 678–683, 2020. [41] M. Sibiya, M. Sumbwanyambe, “A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks,” AgriEngi neering, 1(1): 119–131, 2019. [42] K. Garg, S. Bhugra, B. Lall, “Automatic quantification of plant disease from field image data using deep learning,” in Proc. the IEEE/CVF Winter Conference on Applications of Computer Vision: 1965–1972, 2021. [43] R.A. Priyadharshini, S. Arivazhagan, M. Arun, A. Mirnalini, “Maize leaf disease classification using deep convolutional neural networks,” Neural Comput. Appl., 31(12): 8887–8895, 2019. [44] T. Wiesner-Hanks, E.L. Stewart, N. Kaczmar, C. DeChant, H. Wu, R. J. Nelson, H. Lipson, M. A. Gore, “Image set for deep learn ing: field images of maize annotated with disease symptoms,” BMC research notes, 11(1): 1–3, 2018. [45] P. Bhatt, S. Sarangi, A. Shivhare, D. Singh, S. Pappula, “Iden tification of diseases in corn leaves using convolutional neural networks and boosting,” in ICPRAM: 894–899, 2019. [46] E.L. da Rocha, L. Rodrigues, J.F. Mari, “Maize leaf disease classification using convolutional neural networks and hyperparameter optimization,” in Proc. Anais do XVI Workshop de Vis˜ao Computacional: 104–110, SBC, 2020 [47] A. Waheed, M. Goyal, D. Gupta, A. Khanna, A. E. Hassanien, H.M. Pandey, “An optimized dense convolutional neural network model for disease recognition and classification in corn leaf,” Comput. Electron. Agric., 175: 105456, 2020. [48] Z. Lin, S. Mu, A. Shi, C. Pang, X. Sun, et al., “A novel method of maize leaf disease image identification based on a multichannel convolutional neural network,” Trans. ASABE, 61(5): 1461–1474, 2018. [49] J. Liu, M. Wang, L. Bao, X. Li, “Efficientnet based recognition of maize diseases by leaf image classification,” J. Phys Conf. Ser., 1693: 012148, IOP Publishing, 2020. [50] X. Sun, J. Wei, “Identification of maize disease based on transfer learning,” J. Phys. Conf. Ser., 1437: 012080, IOP Publishing, 2020. [51] M. Syarief, W. Setiawan, “Convolutional neural network for maize leaf disease image classification,” Telkomnika, 18(3): 1376–1381, 2020. [52] S. Mishra, R. Sachan, D. Rajpal, “Deep convolutional neural network based detection system for real-time corn plant disease recognition,” Procedia Comput. Sci., .167: 2003–2010, 2020. [53] J. Tian, Y. Zhang, Y. Wang, C. Wang, S. Zhang, T. Ren, “A method of corn disease identification based on convolutional neu ral network,” in Proc. 2019 12th International Symposium on Computational Intelligence and Design (ISCID): 245–248, 2019. [54] A. Afifi, A. Alhumam, A. Abdelwahab, “Convolutional neural network for automatic identification of plant diseases with limited data,” Plants, 10(1): 28, 2021. [55] Y. Duan, W. Zheng, X. Lin, J. Lu, J. Zhou, “Deep adversarial metric learning,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition: 2780–2789, 2018.
آمار تعداد مشاهده مقاله: 708 تعداد دریافت فایل اصل مقاله: 628

سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب

پیوندهای مفید

پیوندهای مفید

اخبار و اعلانات

آمار

A Survey of Deep Learning Techniques for Maize Leaf Disease Detection: Trends from 2016 to 2021 and Future Perspectives