Abstract
Agricultural takes a major percentage in a country’s economic growth. Crop production plays an essential role in agriculture. Countries’ economical growth rate is reduced due to less crop production. Foods are essential for every living being, since we need proper food for survival. Hence, it is essential for every farmer to cultivate a healthy plant to increase the crop production. However, in nature, every plant can get attacked by some sort of disease but the level of damage occurred to the crops are different for every plant. If a fully matured plant get affected by a simple disease, it will not affect the full plant but if a small plant gets affected by the same disease, it causes severe damage to the plant, as we cannot manually monitor the plants and cannot detect the disease occurring in the plants everyday. Huge manpower is needed to monitor every plant in the farm so it needs time for monitoring every crop in the field. In this paper, image recognition using conventional neural network (CNN) has been proposed to reduce the time complexity and manpower requirement. The proposed algorithm accurately detects the type of diseases that occurs in the plants.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ghosh P Article published in shareyouressays.com named as essay on Indian agriculture. Mayukh Bardhan, Digital Marketer, explains the present situation of Indian agriculture in Quora.com for the question “What is the present scenario of Indian agriculture”
Ghaiwat SN, Arora P (2014) Detection and classification of plant leaf diseases using image processing techniques: a review. Int J Recent Adv Eng Technol 2(3): 2347–2812. ISSN (Online)
Priyadarshini K (2019) Navigating visually impaired and sightless people using auditory guidelines. Int J Adv Res Comput Commun Eng 8(5) May
Karpathy A et al (2014) Large-scale video classification with convolutional neural networks. In: IEEE conference on computer vision and pattern recognition (CVPR)
Tsoi AC, Back AD, Giles CL (1997) Member face recognition: a convolutional neural-network approach Steve Lawrence. IEEE Trans Neural Netw 8(1) January
Preethi Rajaiah R, John Britto R (2014) Optic disc boundary detection and cup segmentation for prediction of glaucoma, Int J Sci Eng Technol Res (IJSETR) 3(10), 2665–2672
Shabina S (2014) Smart Helmet using RF and WSN technology for underground mines safety. In: Proceedings of international conference on intelligent computing applications, pp 305–309
H. Martin Hunke (1994) Locating and tracking of human faces with neural networks. CMU{CS{94{155 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 c H. Martin Hunke, August
Sudhakar S Towards Data Science. Deep-Learning Deep Learning projects implemented by Shreenidhi Sudhakar.github.com. https://towardsdatascience.com/convolution-neural-network-e9b864ac1e6cshree6791/Deep-Learning
Baluja S, Rowley HA, Kanade T Neural network-based face detection. School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA 15213, USA. har@cs.cmu.edu http://www.cs.cmu.edu/˜har, baluja@cs.cmu.edu, http://www.cs.cmu.edu/˜baluja, tk@cs.cmu.edu, http://www.cs.cmu.edu/˜tk
Convolutional Neural Networks for Document Image Classification Le Kang, Jayant Kumar, Peng Ye, YiLi†, DavidDoermann, University of Maryland, College Park, MD, USA {lekang, jayant, pengye, doermann}@umiacs.umd.edu †NICTA and ANU yi.li@cecs.anu.edu.au
Lawrence S, Lee Giles C, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural network approach. IEEE Trans Neural Netw 8(1):98–113
Hinton J (2012) Coursera lectures on neural networks. https://www.coursera.org/learn/neural-networks
Xu L, Ren JSJ, Liu C, Jia J Deep convolutional neural network for image deconvolution. Lenovo Research & Technology, xulihk@lenovo.com. Lenovo Research & Technology, jimmy.sj.ren@gmail.com. Microsoft Research, celiu@microsoft.com. The Chinese University of Hong Kong, leojia@cse.cuhk.edu.hk
An introduction to convolutional neural networks. https://www.researchgate.net/publication/285164623_An_Introduction_to_Convolutional_Neural_Networks. Accessed 23 Dec 2018
TensorFlow Object Detection API. https://github.com/tensorflow/models/tree/master/research/object_detection
TensorFlow detection model zoo. https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md
Ren S, He K, Girshick R, Sun J, Faster R-CNN: towards real-time object detection with region proposal networks
Kagaya H, Aizawa K, Food Detection and Recognition Using Convolutional Neural Network, Graduate School of Interdisciplinary Information Studies The University of Tokyo, kagaya@hal.t.utokyo.ac.jp. Dept. Information and Communication Eng. The University of Tokyo, aizawa@hal.t.utokyo.ac.jp, Makoto Ogawa foo.log Inc. ogawa@foo-log.co.jp
LabelImg. https://github.com/tzutalin/labelImg
Sureswaran R, Bazar HA, Abouabdalla O, Manasrah AM, El-Taj H (2009) Active e-mail system SMTP protocol monitoring algorithm. In: 2009 2nd IEEE international conference on broadband network & multimedia technology. https://doi.org/10.1109/icbnmt.2009.5348490
Klensin J, Editor, Network Working Group, Request forComments:2821 AT&T Laboratories,Obsoletes:821,974, 1869 April 2001, Updates: 1123Category:Standards Track
Klensin J (2001) SMTP Simple mail transfer protocol, RFC 2821, April
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Maheswari, M., Daniel, P., Srinivash, R., Radha, N. (2021). Detection of Diseased Plants by Using Convolutional Neural Network. In: Suma, V., Bouhmala, N., Wang, H. (eds) Evolutionary Computing and Mobile Sustainable Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 53. Springer, Singapore. https://doi.org/10.1007/978-981-15-5258-8_61
Download citation
DOI: https://doi.org/10.1007/978-981-15-5258-8_61
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5257-1
Online ISBN: 978-981-15-5258-8
eBook Packages: EngineeringEngineering (R0)