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International Journal Of Electrical, Electronics And Computers(IJEEC)

Smart Plant Disease Detection System

Bhoopendra Joshi , Abhinav Kumar , Satyam Kashyap , Nooruddin Nagdi , Sukhdarshan Vinayak , Dinesh Verma

International Journal of Electrical, Electronics and Computers (IJECC), Vol-6,Issue-4, July - August 2021, Pages 13-16, 10.22161/eec.64.4

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Article Info: Received: 15 Jun 2021; Accepted: 03 Jul 2021; Date of Publication: 09 Jul 2021


Food is one of the basic needs of human being. Population is increasing day by day. So, it has become important to grow sufficient amount of crops to feed such a huge population. Agricultural intervention in the livelihood of rural India is about 58%. But with the time passing by, plants are being affected with many kinds of diseases, which cause great harm to the agricultural plant productions. It is very difficult to monitor the plant diseases. It requires tremendous amount of work, expertise in the plant diseases, and also require the excessive processing speed and time. Hence, image processing is used for the detection of plant diseases by just capturing the images of the leaves and comparing it with the data sets available. Latest and fostering technologies like Image processing is used to rectify such issues very effectively. In this project, four consecutive stages are used to discover the type of disease. The four stages include pre-processing, leaf segmentation, feature extraction and classification. This paper aims to support and help the farmers in an efficient way.

CNN Algorithm, Disease Detection.

[1] Detection of unhealthy plant leaves using image processing and genetic algorithm with Arduino2018 International Conference on Power, Signals, Control and Computation (EPSCICON)
[2] Tanvimehera, vinaykumar, pragyagupta "Maturity and disease detection in tomato using computer vision" 2016 Fourth international conference on parallel, distributed and grid computing (PDGC)
[3] Ms.Poojapawer, Dr.Varsha Tukar, Prof. Parvinpatil "Cucumber Disease detection using artificial neural network"
[4] Mukesh Kumar Tripathi, Dr. Dhananjay, D. Maktedar'' Recent Machine Learning Based Approaches for Disease Detection and Classification of Agricultural Products'' International Conference on Electrical, Electronics and Optimization Techniques (ICEEOT)-2016.
[5] Gittaly Dhingra, Vinay Kumar, Hem Dutt Joshi, “Study of digital image processing techniques for leaf disease detection and classification,” Springer-Science, 29 November 2017
[6] Shitala Prasad, Sateesh K. Peddoju, Debashis Ghosh,“Multi-resolution mobile vision system for plant leaf disease diagnosis,” pp. 379–388, Springer-Verlag London 2015
[7] Shanwen Zhang, Zhuhong You, Xiaowei Wu,” Plant disease leaf image segmentation based on superpixel clustering and EM algorithm,” Springer, June 2017.
[8] Keyvan Asefpour Vakilian & Jafar Massah,” An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) Plants using digital image processing,” Vol. 46, No. 13,1580–1588, Taylor &Francis, 2013
[9] Mohammed Brahimi, Kamel Boukhalfa & Abdelouahab Moussaoui,” Deep Learning for Tomato Diseases: Classification and Symptoms Visualization,” vol. 31, no.4, 299–315, Taylor & Francis, 2017.
[10] H.Al-Hiary, S. Bani-Ahmad, M.Reyalat, M.Braik & Z.AlRahamneh, “Fast and Accurate Detection and Classification of Plant Diseases”, International Journal of Computer Applications, Vol.17,No.1, pp.31-38.March 2011.
[11] Yuanyuan Shao, Guantao Xuan, Yangyan Zhu, Yanling Zhang, Hongxing Peng, Zhongzheng Liu & Jialin Hou,” Research on automatic identification system of tobacco diseases”, vol. 65, no. 4, 252–259, Taylor & Francis, 2017
[12] Vijai Singh, A.K. Misra,” Detection of plant leaf diseases using image segmentation and soft computing Techniques, “Information Processing In Agriculture 4 (2017) 41–49 , science direct, 2017