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IJSTR >> Volume 10 - Issue 5, May 2021 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

A Novel Framework For Disease Severity Level Identification Of Cotton Plant Using Machine Learning Techniques

[Full Text]



Aurangzeb Magsi, Riaz Ahmed Shaikh, Zulfiqar Ali Shar, Rafaqat Hussain Arain, Asad Ali Soomro



Biotechnology, Disease Identification, Machine Learning, Features Extraction, Image Processing, Cotton Plant, Artificial Intelligence



The World is moving with technological revolution. Computers are considered as the principal object in almost all the fields of life. In this concern, needs of biotechnology applications is immensely required to solve complex problems. Cotton plant is an important sector in the field of agriculture. Disease to that particular plant may also cause a loss to the agriculture sector. This paper is aims at dealing with cotton disease and its time based severity. Cotton plant is among those imperative plants which grow majorly in Pakistan and has a huge impact on its economy. Yield amount and quality of cotton plant is compromised every year damaging by some highly harmful diseases. Since, in this paper we presents a methodology to identify the severity level of a common and complex disease namely Cotton leaf curl Disease (CLCuD) by using methods of image processing and machine learning techniques. Color and texture features are used to extract values of an input image while deep learning method is use for decision making purpose. For experimentation process, a dataset of 1600 images is set. A Deep Convolutional Neural Network (CNN) is use for the classification. Cumulatively, 89.4% accuracy is received with the proposed model in term of proper identification and classification. This research work will be beneficial for local as well international harvesters and can be used to take time based preventive measures in order to reduce loss percentage.



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