Open Access Open Access  Restricted Access Subscription or Fee Access

PlanTech: Early Detection of Plant Disease Based on HWSN Using Deep Learning


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irea.v9i3.20720

Abstract


Plant diseases are a major threat to food security. Thus, the identification of these diseases is crucial to alleviate the problem. Deep learning combined with image processing has proven to be efficient in order to identify such diseases accurately, automatically and quickly. This article presents a general architecture for plant disease prediction based on the Hybrid Wireless Sensor Network, the Internet of Things, and Deep Learning algorithms. In this work, the focus is on learning models for plant disease detection to be implemented afterward on the structure for actual trials. Thus, several deep learning architectures have been tested, i.e. ResNet50, 101, and 152, AlexNet, VGG, SqueezeNet, DenseNet, and Inception. In addition to that, a new parallel architecture has been developed and evaluated by combining two deep learning architectures. Several optimizers such as SGD and ADAM have been leveraged in order to improve the performances. The public published database PlantVillage is used for model learning, which contains 54.306 images of agricultural plant leaves. As a result, the best validation accuracy score of 98,09% is achieved using the presented para model with ADAM optimizer running with the 80/20 splitting data on the 14th epoch.
Copyright © 2021 Praise Worthy Prize - All rights reserved.

Keywords


Deep Learning; Convolution Neural Network; Computer Vision; Plant Disease; Hybrid Wireless Sensor Network

Full Text:

PDF


References


E. Briones Alonso, L. Cockx, and J. Swinnen, Culture and food security, Glob. Food Secur., vol. 17, pp. 113–127, Jun. 2018.
https://doi.org/10.1016/j.gfs.2018.02.002

A. Y. Prosekov and S. A. Ivanova, Food security: The challenge of the present, Geoforum, vol. 91, pp. 73–77, May 2018.
https://doi.org/10.1016/j.geoforum.2018.02.030

T. Parrón, M. Requena, A. F. Hernández, and R. Alarcón, Environmental exposure to pesticides and cancer risk in multiple human organ systems, Toxicol. Lett., vol. 230, no. 2, pp. 157–165, Oct. 2014.
https://doi.org/10.1016/j.toxlet.2013.11.009

M. Valcke et al., Human health risk assessment on the consumption of fruits and vegetables containing residual pesticides: A cancer and non-cancer risk/benefit perspective, Environ. Int., vol. 108, pp. 63–74, Nov. 2017.
https://doi.org/10.1016/j.envint.2017.07.023

E. F. S. Authority, The 2013 European Union report on pesticide residues in food, EFSA J., vol. 13, no. 3, p. 4038, 2015.
https://doi.org/10.2903/j.efsa.2015.4038

N. Alexandratos and J. Bruinsma, World agriculture towards 2030/2050: the 2012 revision, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA), 288998, Jun. 2012. Accessed: Jan. 02, 2021. [Online].
Available: https://ideas.repec.org/p/ags/faoaes/288998.html

Y. Mekonnen, L. Burton, A. Sarwat, and S. Bhansali, IoT Sensor Network Approach for Smart Farming: An Application in Food, Energy and Water System, in 2018 IEEE Global Humanitarian Technology Conference (GHTC), San Jose, CA, Oct. 2018, pp. 1–5.
https://doi.org/10.1109/ghtc.2018.8601701

M. M. Maha, S. Bhuiyan, and M. Masuduzzaman, Smart Board for Precision Farming Using Wireless Sensor Network, in 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, Jan. 2019, pp. 445–450.
https://doi.org/10.1109/icrest.2019.8644215

M. S. Farooq, S. Riaz, A. Abid, K. Abid, and M. A. Naeem, A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming, IEEE Access, vol. 7, pp. 156237–156271, 2019.
https://doi.org/10.1109/access.2019.2949703

F. Kiani and A. Seyyedabbasi, Wireless Sensor Network and Internet of Things in Precision Agriculture, Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 6, 2018.
https://doi.org/10.14569/IJACSA.2018.090614

A. Triantafyllou, P. Sarigiannidis, and S. Bibi, Precision Agriculture: A Remote Sensing Monitoring System Architecture †, Information, vol. 10, no. 11, p. 348, Nov. 2019.
https://doi.org/10.3390/info10110348

J. G. Ramírez-Gil, G. O. G. Martínez, and J. G. Morales Osorio, Design of electronic devices for monitoring climatic variables and development of an early warning system for the avocado wilt complex disease, Comput. Electron. Agric., vol. 153, pp. 134-143, Oct. 2018.
https://doi.org/10.1016/j.compag.2018.08.002

A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, and D. P. Hughes, Deep Learning for Image-Based Cassava Disease Detection, Front. Plant Sci., vol. 8, 2017.
https://doi.org/10.3389/fpls.2017.01852

A. El Attaoui, M. Hazmi, A. Jilbab, and A. Bourouhou, Wearable Wireless Sensors Network for ECG Telemonitoring Using Neural Network for Features Extraction, Wirel. Pers. Commun., Nov. 2019.
https://doi.org/10.1007/s11277-019-06967-x

G. Hu, X. Yang, Y. Zhang, and M. Wan, Identification of tea leaf diseases by using an improved deep convolutional neural network, Sustain. Comput. Inform. Syst., vol. 24, p. 100353, Dec. 2019.
https://doi.org/10.1016/j.suscom.2019.100353

B. Liu, Y. Zhang, D. He, and Y. Li, Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks, Symmetry, vol. 10, no. 1, p. 11, Jan. 2018.
https://doi.org/10.3390/sym10010011

K. Kc, Z. Yin, M. Wu, and Z. Wu, Depthwise separable convolution architectures for plant disease classification, Comput. Electron. Agric., vol. 165, p. 104948, Oct. 2019.
https://doi.org/10.1016/j.compag.2019.104948

Guan Wang, Yu Sun, Jianxin Wang, Automatic Image-Based Plant Disease Severity Estimation Using Deep Learning, Computational Intelligence and Neuroscience, vol. 2017, Article ID 2917536, 8 pages, 2017.
https://doi.org/10.1155/2017/2917536

K. P. Ferentinos, Deep learning models for plant disease detection and diagnosis, Comput. Electron. Agric., vol. 145, pp. 311–318, Feb. 2018.

E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, A comparative study of fine-tuning deep learning models for plant disease identification, Comput. Electron. Agric., vol. 161, pp. 272–279, Jun. 2019.
https://doi.org/10.1016/j.compag.2018.03.032

Pinzon-Arenas, J., Jimenez-Moreno, R., Pachon-Suescun, C., Place Recognition with DAG-CNN, (2020) International Review of Automatic Control (IREACO), 13 (2), pp. 58-66.
https://doi.org/10.15866/ireaco.v13i2.17053

Jimenez-Moreno, R., Martinez, D., A Novel Parallel Convolutional Network Architecture for Depth-Dependent Object Recognition, (2019) International Review of Automatic Control (IREACO), 12 (2), pp. 76-81.
https://doi.org/10.15866/ireaco.v12i2.16467

Tolebi, G., Dairbekov, N., Kurmankhojayev, D., Link Flow Estimation on an Isolated Intersection Based on Deep Learning Models, (2020) International Review of Automatic Control (IREACO), 13 (1), pp. 19-26.
https://doi.org/10.15866/ireaco.v13i1.18213

Pinzón-Arenas, J., Jiménez-Moreno, R., Pachón-Suescún, C., Handwritten Word Searching by Means of Speech Commands Using Deep Learning Techniques, (2019) International Review on Modelling and Simulations (IREMOS), 12 (4), pp. 253-263.
https://doi.org/10.15866/iremos.v12i4.17166

R. Arora, A. Basu, P. Mianjy, and A. Mukherjee, Understanding Deep Neural Networks with Rectified Linear Units, ArXiv161101491 Cond-Mat Stat, Feb. 2018, Accessed: Jan. 02, 2021. [Online].
Available: http://arxiv.org/abs/1611.01491

A. Kamilaris and F. X. Prenafeta-Boldú, Deep learning in agriculture: A survey, Comput. Electron. Agric., vol. 147, pp. 70–90, Apr. 2018.
https://doi.org/10.1016/j.compag.2018.02.016

S. Ruder, An overview of gradient descent optimization algorithms, ArXiv160904747 Cs, Jun. 2017, Accessed: Dec. 17, 2020. [Online].
Available: http://arxiv.org/abs/1609.04747

K. He, X. Zhang, S. Ren, and J. Sun, Deep Residual Learning for Image Recognition, ArXiv151203385 Cs, Dec. 2015, Accessed: Jan. 02, 2021. [Online].
Available: http://arxiv.org/abs/1512.03385

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Adv. Neural Inf. Process. Syst., vol. 25, pp. 1097–1105, 2012.
https://doi.org/10.1145/3065386

K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” ArXiv14091556 Cs, Apr. 2015, Accessed: Jan. 02, 2021. [Online].
Available: http://arxiv.org/abs/1409.1556

F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and textless0.5MB model size, ArXiv160207360 Cs, Nov. 2016, Accessed: Jan. 02, 2021. [Online].
Available: http://arxiv.org/abs/1602.07360

G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, Densely Connected Convolutional Networks, ArXiv160806993 Cs, Jan. 2018, Accessed: Jan. 02, 2021. [Online].
Available: http://arxiv.org/abs/1608.06993

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, Rethinking the Inception Architecture for Computer Vision, ArXiv151200567 Cs, Dec. 2015, Accessed: Jan. 02, 2021. [Online].
https://doi.org/10.1109/cvpr.2016.308
Available: http://arxiv.org/abs/1512.00567

S. P. Mohanty, D. P. Hughes, and M. Salathé, Using Deep Learning for Image-Based Plant Disease Detection, Front. Plant Sci., vol. 7, p. 1419, 2016.
https://doi.org/10.3389/fpls.2016.01419


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize