Abstract
In recent years, the applications of the computer vision concepts and information communication technology has been observed in number of applications including home automation, healthcare, smart cities, precision agriculture etc. Internet of Things (IoT) is the underlying technology that indulges in almost all part of world infrastructure with the indispensable concept of connecting every device for collecting, contributing, experiencing, and analyzing the information. Smart or precision farming is known for achieving intelligence in agriculture. Therefore, in this article, an effort has been made towards automated disease detection from the plant leaves. For this a novel framework, a method named as IoT_FBFN using Fuzzy Based Function Network (FBFN) enabled with IoT has been proposed. At first, the images of leaf are acquired. Then these images are preprocessed and features are extracted using the Scale-invariant feature transform method. Finally, FBFN is used for the detection of the galls caused by the insect named as Pauropsyllatuberculate. The training process of the network is by optimizing with the help of Firefly algorithm, this increases the efficiency of the network. The proposed IoT_FBFN network having the computational power of fuzzy logic and learning adaptability of neural network achieves higher accuracy for identification and classification of galls when compared with the other approaches. The article concludes with the challenges encountered and future works.
Similar content being viewed by others
References
Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through internet of things. IEEE Internet of Things Journal, 1(2), 112–121. https://doi.org/10.1109/JIOT.2013.2296516
Stankovic, J. A. (2014). Research Directions for the Internet of Things. IEEE Internet of Things Journal, 1(1), 3–9. https://doi.org/10.1109/JIOT.2014.2312291
Zhou, M., Fortino, G., Shen, W., Mitsugi, J., Jobin, J., Bhattacharyya, R. (2016) Internet of Things for Smart Automated Systems. IEEE Transactions on Automation Science and Engineering, 13(3), 1225–1229, https://doi.org/10.1109/TASE.2016.2579538.
Nandhini, S. A., Hemalatha, R., Radha, S., & Indumathi, K. (2017). Web enabled plant disease detection system for agricultural applications using WMSN. Wireless Pers Commun. https://doi.org/10.1007/s11277-017-5092-4
Popović, T., Latinović, N., Pešić, A., Zečević, Ž, Krstajić, B., & Djukanović, S. (2017). Architecting an IoT-enabled platform for precision agriculture and ecological monitoring: A case study. Computers and Electronics in Agriculture, 140, 255–265. https://doi.org/10.1016/j.compag.2017.06.008
Talavera, J. M., Tobón, L. E., Gómez, J. A., Culman, M. A., Aranda, J. M., Parra, D. T., Quiroz, L. A., Hoyos, A., & Garreta, L. E. (2017). Review of IoT applications in agro-industrial and environmental fields. Computers and Electronics in Agriculture, 142, 283–297. https://doi.org/10.1016/j.compag.2017.09.015
Chouhan, S. S., Kaul, A., Singh, U. P., & Jain, S. (2018). Bacterial foraging optimization based Radial Basis Function Neural Network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards Plant Pathology. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2800685
Kaur, S., Pandey, S., & Goel, S. (2018). Plants disease identification and classification through leaf images: A survey. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-018-9255-6
Chouhan, S. S., Kaul, A., & Singh, U. P. (2018). Image segmentation using computational intelligence techniques: Review. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-018-9257-4
Xu, L., Collier, R., & O’Hare, G. M. (2017). A survey of clustering techniques in WSNs and consideration of the challenges of applying such to 5G IoT scenarios. IEEE Internet of Things Journal, 4(5), 1229–1249. https://doi.org/10.1109/JIOT.2017.2726014
Xu, G., Ngai, E. C. H., & Liu, J. (2018). Ubiquitous transmission of multimedia sensor data in Internet of Things. IEEE Internet of Things Journal, 5(1), 403–414. https://doi.org/10.1109/JIOT.2017.2762731
Pan, J., & McElhannon, J. (2018). Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things Journal, 5(1), 439–449. https://doi.org/10.1109/JIOT.2017.2767608
Ding, G. (2018). An amateur drone surveillance system based on the cognitive Internet of Things. IEEE Communications Magazine. https://doi.org/10.1109/MCOM.2017.1700452
Yang, C., Shen, W., & Wang, X. (2018). The Internet of Things in manufacturing key issues and potential applications. IEEE Systems, Man, & Cybernetics Magazine. https://doi.org/10.1109/MSMC.2017.2702391
Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48. https://doi.org/10.1016/j.biosystemseng.2017.09.007
Mohanraj, I., Ashokumar, K. and Naren, J. “Field Monitoring and Automation using IOT in Agriculture Domain,” 6th International Conference On Advances In Computing & Communications, ICACC 2016, pp. 931–939, https://doi.org/10.1016/j.procs.2016.07.275.
Karim, F., Karim, F. Monitoring system using web of things in precision agriculture. The 12th International Conference on Future Networks and Communications (FNC 2017), 402–409, https://doi.org/10.1016/j.procs.2017.06.083.
Kaloxylos, A., Wolfert, J., Verwaart, T., Terol, C.M., Brewster, C., Robbemond, R. and Sundmaker, H. (2013) The use of future internet technologies in the agriculture and food sectors: integrating the supply chain. 6th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2013), 51–60, https://doi.org/10.1016/j.protcy.2013.11.009.
J Yu, W Zhang (2013) Study on Agricultural Condition Monitoring and Diagnosing of Integrated Platform Based on the Internet of Things. CCTA 2012, Part I, IFIP AICT 392, 2012, 244–250
Zhou, L., Song, L., Xie, C., Zhang, J. (2013) Applications of Internet of Things in the Facility Agriculture. CCTA, Part I, IFIP AICT 392, 2012, 297–303.
M. J. Gomes. The Internet of Things as an Integrated Service Platform to Increase Value to the Agriculture Stakeholders. Putting Tradition into Practice: Heritage, Place and Design, Lecture Notes in Civil Engineering3, https://doi.org/10.1007/978-3-319-57937-5.
Abouzahir, S., Sadik, M. Sabir, E. (2017) IoT-empowered smart agriculture: A real-time light-weight embedded segmentation system, UNet 2017, LNCS 10542, 319–332, https://doi.org/10.1007/978-3-319-68179-5_28
Zhang, R., Hao, F., & Sun, X. (2017). The design of agricultural machinery service management system based on Internet of Things. Procedia Computer Science, 107, 53–57. https://doi.org/10.1016/j.procs.2017.03.055
Zhang, S., Wang, H., Huang, W., & You, Z. (2018). Plant diseased leaf segmentation and recognition by fusion of superpixel, K-means and PHOG. Optik, 157, 866–872. https://doi.org/10.1016/j.ijleo.2017.11.190
Ojha, T., Misra, S., & Raghuwanshi, N. S. (2015). Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges”. Computers and Electronics in Agriculture, 118, 66–84. https://doi.org/10.1016/j.compag.2015.08.011
Khyade, M. S., Kasote, D. M., & Vaikos, N. P. (2014). Alstonia scholaris (L.) R.Br. and Alstonia macrophylla Wall. Ex G. Don: A comparative review on traditional uses, phytochemistry and pharmacology. Journal of Ethnopharmacology, 153, 1–18. https://doi.org/10.1016/j.jep.2014.01.025
Pratap, B., Chakraborthy, G. S., & Mogha, N. (2013). Complete Aspects Of Alstonia Scholaris. International Journal of PharmTech Research, 5(1), 17–26.
Khatale, Vaishali Laxman, & More, D. B. (2016). A Review on Saptaparna (Alstonia Scholaris R. Br). International Ayurvedic Medical Journal, 4(03), 334–337.
Muhammad, S., Khan, Z., Zaheer, A., Siddiqui, M. F., Masood, M. F., & Sarangzai, A. M. (2014). Alstonia Scholaris (L.) R.Br. - Planted Bioindicator along different Road-Sides of Lahore City. Pak. J. Bot., 46(3), 869–873.
Kumar, S. R., Arumugam, T., Anandakumar, C., Balakrishnan, S., & Rajavel, D. (2013). Use of plant species in controlling environmental pollution- a review. Bull. Env. Pharmacol. Life Sci., 2(2), 52–63.
Talukdar, P., Das, K., Dhar, S., Talapatra, S. N., & Swarnakar, S. (2016). Galls on Alstonia scholarisleaves as air pollution indicator. World Scientific News, 52, 181–194.
Albert, S., Padhiar, A., Gandhi, D., & Nityanand, P. (2011). “Morphological, anatomical and biochemical studies on the foliar galls of Alstonia scholaris(Apocynaceae). Revista Brasil Bot, 34(3), 343–358.
Saini, D., & Sarin, R. (2012). “SDS-PAGE Analysis of Leaf Galls of Alstonia scholaris (L.) R. Br. J Plant Pathol Microb. https://doi.org/10.4172/2157-7471.1000121
Charfi, N., Trichili, H., Alimi, A. M., & Solaiman, B. (2017). Bimodal biometric system for hand shape and palmprint recognition based on SIFT sparse representation. Multimed Tools Appl, 76, 20457–20482. https://doi.org/10.1007/s11042-016-3987-9
Patil, S. B., & Sinha, G. R. (2017). “Distinctive Feature Extraction for Indian Sign Language (ISL) Gesture using Scale Invariant Feature Transform (SIFT). J. Inst. Eng. India Ser. B, 98(1), 19–26. https://doi.org/10.1007/s40031-016-0250-8
Agarwal, V., & Bhanot, S. (2017). Radial basis function neural network-based face recognition using firefly algorithm. Neural Comput & Applic. https://doi.org/10.1007/s00521-017-2874-2
Kora, P. (2017). ECG based Myocardial Infarction detection using Hybrid Firefly Algorithm. Computer Methods and Programs in Biomedicine, 152, 141–148. https://doi.org/10.1016/j.cmpb.2017.09.015
Ariyaratne, M. K. A., & Fernando, T. G. I. (2014). A comparative study on nature inspired algorithms with firefly algorithm. International Journal of Engineering and Technology, 4(10), 611–617.
Kaur, M., & Ghosh, S. (2016). Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm. Applied Soft Computing, 49, 868–886. https://doi.org/10.1016/j.asoc.2016.09.019
Lin, C.-K. (2005). Adaptive critic autopilot design of bank-to-turn missiles using fuzzy basis function networks. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 35(2), 197–297. https://doi.org/10.1109/TSMCB.2004.842246
Mar, J., Kuo, C. C., & Lou, L. S. (2012). FBFN-based pointing error correction architecture in wind-force environments. IEEE Antennas and Wireless Propagation Letters, 11, 559–563.
Acknowledgement
This work was supported by grant No. 8-68/FDC/RPS (POLICY-1/2019/20) from the All India Council of Technical Education (AICTE), India.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chouhan, S.S., Singh, U.P. & Jain, S. Automated Plant Leaf Disease Detection and Classification Using Fuzzy Based Function Network. Wireless Pers Commun 121, 1757–1779 (2021). https://doi.org/10.1007/s11277-021-08734-3
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-021-08734-3