International Journal of Plant Protection

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Tele-Pathology in Plants for Disease Diagnosis in Agriculture:Review and Analysis


Affiliations
1 Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow (U.P.), India
     

   Subscribe/Renew Journal


Early diagnosis of diseases play a crucial role in increasing the agricultural productivity and ensuring food security. Specially, in many parts of the world, immediate disease identification remains difficult due to the lack of necessary infrastructure. Besides that, many challenges are noticed to identify the plant diseases correctly such as multiple and simultaneous disorders in a single plant, different disorders having similar symptoms etc. In spite of all the challenges, deep learning approaches have shown promise in classifying the complex diseases correctly. As digital India is advancing, smart agricultural systems will provide assistance to farmers, and “Tele-pathology in plants” is the way forward. In this context, a literature review on classification of different kinds of approaches and techniques has been presented with the objective focus on designing an inclusive system architecture for Tele-pathology in plants. Discreet studies focusing on specific verticals are present among the research community but a holistic structural approach formalizing the use cases is missing. The purpose of this research is to propose and explain a system architecture with interplay among different system blocks such as crop disease imagedataset, annotation of digital image dataset by consultation with the domain expert, generation of disease markers and establishing different algorithmic techniques.

Keywords

Artificial Intelligence, Tele-Pathology, Neural Networks, Image based Plant Disease Identification.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Albert, I.P., Alexander, A.D., Alexander, V.I. and Maxim, E.V. (2007). Three level neural network for data clusterzation on images of infected crop field. J. Res. & Applications Agric. Engineering, 52:137.

  • Babu, M.S.P. and Rao, B.S. (2007). Leaves recognition using back-propagation neural network - advice for pest and disease control on crops. In IndiaKisan.Net, pages 1{11, Expert Advissory System.

  • Camargo, A. and Smith, J.S. (2009a). An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems Engineering, 102:9.

  • Camargo, A. and Smith, J.S. (2009b). Image pattern classification for the identification of disease causing agents in plants. Computers & Electronics Agric., 66:121.

  • Fujita, E., Kagiwada, S., Uga, H., Cap, H.Q., Suwa, K. and Iyatomi, H. (2018). A deep learning approach for on-site plant leaf detection. In 14th International Colloquium on Signal Processing & its Applications, pages 79{83, Batu Feringghi, Malaysia.

  • Jayas, D.S., Narvankar, D.S., Singh, C.B. and White, N.D.G. (2009). Assessment of soft x-ray imaging for detection of fungal infection in wheat. Biosystems Engineering, 103:49.

  • Khalid, F., Lili, N.A. and Borhan, N.M. (2011). Classification of herbs plant diseases via hierarchical dynamic articial neural network after image removal using kernel regression framework. Internat. J. Computer Sci. & Engineering (IJCSE), 3:15.

  • Mohanty, S.P., Hughes, D. and Salathe, M. (2016). Using deep learning for image-based plant disease detection. Front. Plant Sci., 7:1.

  • Patil, J.K. and Kumar, R. (2011). Advances in image processing for detection of plant diseases. J. Adv. Bioinformatics Applications & Res., 2 : 135.

  • Ranjan, K.R., Prasad, R. and Sinha, A.K. (2006). Amrapalika: An expert system for the diagnosis of pests, diseases, disorders in indian mango. Knowledge Based System, 19:9.

  • Rothe, P.R. and Kshirsagar, R.V. (2015). Cotton leaf disease identification using pattern recognition techniques. In International Conference on Pervasive Computing (ICPC), pages 1{6, Andhra University, India.

  • Sanchis, J.G., Aleixos, B.N. and Molto, E. (2009). Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features. Biosystems Engineering, 103:137

  • Singh, Gyan Vardhan and Singh, Pooja (2019). Signal Processing techniques for Identification of Plant Diseases (Submitted for Publication in International Journal of Plant Protection; ISSN : 0974-2670).

  • Suman, T. and Dhruvakumar, T. (2015). Classification of paddy leaf diseases using shape and color features. Internat.

  • J. Electrical & Electronics Engineers, 7 (1) : 239-250 Zhang, J., Zheng, L. and Wang, Q. (2009). Mean-shift-based color segmentation of images containing green vegetation. Computers & Electronics Agric., 65 : 93.

  • Zhou, Y., Tang, J., Hu, Y., Yao, Q., Guan, Z. and Yang, B. (2009). Application of support vector machine for detecting rice diseases using shape and color texture features. In nternational Conference on Engineering Computation, pages 79; 83, Hong Kong, China.


Abstract Views: 282

PDF Views: 0




  • Tele-Pathology in Plants for Disease Diagnosis in Agriculture:Review and Analysis

Abstract Views: 282  |  PDF Views: 0

Authors

Gyan Vardhan Singh
Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow (U.P.), India
Pooja Singh
Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow (U.P.), India

Abstract


Early diagnosis of diseases play a crucial role in increasing the agricultural productivity and ensuring food security. Specially, in many parts of the world, immediate disease identification remains difficult due to the lack of necessary infrastructure. Besides that, many challenges are noticed to identify the plant diseases correctly such as multiple and simultaneous disorders in a single plant, different disorders having similar symptoms etc. In spite of all the challenges, deep learning approaches have shown promise in classifying the complex diseases correctly. As digital India is advancing, smart agricultural systems will provide assistance to farmers, and “Tele-pathology in plants” is the way forward. In this context, a literature review on classification of different kinds of approaches and techniques has been presented with the objective focus on designing an inclusive system architecture for Tele-pathology in plants. Discreet studies focusing on specific verticals are present among the research community but a holistic structural approach formalizing the use cases is missing. The purpose of this research is to propose and explain a system architecture with interplay among different system blocks such as crop disease imagedataset, annotation of digital image dataset by consultation with the domain expert, generation of disease markers and establishing different algorithmic techniques.

Keywords


Artificial Intelligence, Tele-Pathology, Neural Networks, Image based Plant Disease Identification.

References