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Singh, Pooja
- Signal Processing Techniques for Identification of Plant Diseases
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1 Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow (U.P.), IN
1 Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow (U.P.), IN
Source
International Journal of Plant Protection, Vol 12, No 2 (2019), Pagination: 132-137Abstract
Plant pathology is a vast science with far reaching impact on human civilization. Selfsufficiency in food production is important for overall prosperity of any Modern Economy. Currently with the advance in overall Agricultural/Horticultural sciences, our overall Food production has been good. However, we neglect the often important factor of disease out-breaks which have economically affected the farmers at different instances. Sometimes, few diseases or symptoms are well known to farmers and could be easily diagnosed and in other cases, expert opinion is required, which is often not easily available. With the advent of cloud computing, penetration of mobile phones and availability of high-speed network, it is very easy to implement soft system, which could help our farmers identify different diseases based on image data captured by their mobile phone. To demonstrate the concept in this paper Alternaria Alternata, Anthracnose, Powdery Mildew in different species like Grape, tomato and Jute is considered. We discuss and implement feature extraction module to objectively construct a disease signature/ unique marker that could be used for specific disease identification across species irrespective of Plant type. In line with this strategy, a software architecture for Tele-pathology in plants is structured such that different diseases could be categorized. In a very short period of time, expert knowledge in the field of Plant Pathology could be objectified into easily usable tools and this would complement the already existing classical extension activities.Keywords
Powdery Mildew, Alternaria Alternata, Anthracnose, Hue Based Segmentation, Image Processing, Feature Extraction, Multi-SVM Classifier.References
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- Singh, Gyan Vardhan and Singh, Pooja (2019). Telepathology in plants for Disease Diagnosis in Agriculture: Review and analysis (Submitted for Publication in International Journal of Plant Protection; ISSN : 0974-2670).
- Zulkifli Bin Husin, Abdul Hallis Bin Abdul Aziz, Ali Yeon Bin Md Shakaff Rohani Binti S Mohamed Farook (2012). Feasibility Study on Plant Chili Disease Detection Using Image Processing Techniques, 2012 Third International Conference on Intelligent Systems Modelling and Simulation.
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- http://www.mathworks.com/ Accessed: Mar. 2, 2016
- Tele-Pathology in Plants for Disease Diagnosis in Agriculture:Review and Analysis
Abstract Views :215 |
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow (U.P.), IN
1 Department of Electronics and Communication Engineering, Amity University, Lucknow Campus, Lucknow (U.P.), IN
Source
International Journal of Plant Protection, Vol 12, No 2 (2019), Pagination: 183-187Abstract
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
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