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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]

 

AUTHOR(S)

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

 

KEYWORDS

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

 

ABSTRACT

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.

 

REFERENCES

[1] S. Abbas, “Climate change and cotton production: an empirical investigation of Pakistan”, Environmental Science and Pollution Research 27, 2020, p. 29580-29588.
[2] H. Zhao, C. Yang,.W. Guo, L. Zhang and D. Zhang, “Automatic estimation of crop disease severity levels based on vegetation index normalization”, Remote Sensing 12, no. 12, 2020, p.1930.
[3] W. Jia, Y. Tian, R. Luo, Z. Zhang, J. Lian, and Y. Zheng., “Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot”, Computers and Electronics in Agriculture 172, 2020, p.105380.
[4] A.Johannes, A. Picon, A. Alvarez-Gila, J. Echazarra, S. Rodriguez-Vaamonde, A. D. Navajas, and A. Ortiz-Barredo., “Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case”, Computers and electronics in agriculture 138, ,2017, p. 200-209.
[5] K. Simonyan, and Z.Andrew, “Very deep convolutional networks for large-scale image recognition”, arXiv preprint arXiv 2014,p.1409.1556
[6] L. Amrao, S.Akhter, M. N. Tahir, I. Amin, R. W. Briddon, S. Mansoor., “Cotton leaf curl disease in Sindh province of Pakistan is associated with recombinant begomovirus components”, Virus research 153, no. 1, 2010, p. 161-165.
[7] T.Wang, J. A. Thomasson, C. Yang, T. Isakeit, and R. L. Nichols., “Automatic classification of cotton root rot disease based on UAV remote sensing”, Remote Sensing 12, no. 8, 2020, p. 1310.
[8] T.Wang, J. A. Thomasson, T. Isakeit, C. Yang, and R. L. Nichols., “A Plant-by-Plant Method to Identify and Treat Cotton Root Rot Based on UAV Remote Sensing”, Remote Sensing 12, no. 15 , 2020, p. 2453.
[9] D. Krisnandi, H. F. Pardede, R. S. Yuwana, V. Zilvan, “Diseases classification for tea plant using concatenated convolution neural network“, CommIT (Communication & Information Technology) Journal, vol. 13, no. 02, pp. 67–77, 2019.
[10] R. Thapa, , S. Noah, B. Serge, and K. Awais, “The Plant Pathology 2020 challenge dataset to classify foliar disease of apples”, arXiv preprint arXiv:2004., 2020, p.11958
[11] D. Argüeso, P. Artzai, I. Unai, M. Alfonso, M. G. San-Emeterio, B. Arantza, and A-Gila.Aitor Alvarez-Gila. "Few-Shot Learning approach for plant disease classification using images taken in the field”, Computers and Electronics in Agriculture 175, 2020 p.105542.
[12] Rothe, P. R., and R. V. Kshirsagar., “Cotton leaf disease identification using pattern recognition techniques”, In 2015 International conference on pervasive computing (ICPC), IEEE, 2015, p. 1-6.
[13] S. S Sannakki, V. S Rajpurohit, V B Nargund, P. Kulkarni4, “Diagnosis and classification of grape leaf diseases using neural networks”, In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), IEEE, 2013. pp. 1-5.
[14] A. Akhtar, A. Khanum, S. A. Khan, A. Shaukat, “Automated plant disease analysis (APDA): performance comparison of machine learning techniques”, In 2013 11th International Conference on Frontiers of Information Technology, IEEE, 2013. pp. 60-65.
[15] A. Alexandre, Bernardes, G. Jonathan., Rogeri, B Roberta, Oliveira, N. Marranghello, A. S. Pereira, A. F. Araujo and R. João Manuel R, S. Tavares, “Identification of foliar diseases in cotton crop”, In Topics in Medical Image Processing and Computational Vision, Springer, Dordrecht, 2013. p. 67-85.
[16] J. TIAN, Q. HU, X. MA3; M. HAN, “An improved KPCA/GA-SVM classification model for plant leaf disease recognition”, Journal of Computational Information Systems 8, no. 18 , 2012, p. 7737-7745.
[17] S. P.Mohanty, D. P.Hughes. and M. Salathé, “Using deep learning for image-based plant disease detection”, Frontiers in plant science 7, 2016 p.1419.
[18] 15. Shaikh, R.A.; Memon, I.; Arain, R.H.; Maitlo, A.; and Shaikh, H. (2018). A Contemporary Approach for Object Recognition Using Spatial Layout and Low Level Features’ Integration”, Multimedia Tools and Applications
[19] P. R. Rothe, R. V. Kshirsagar, “Automated extraction of digital images features of three kinds of cotton leaf diseases”, In 2014 International Conference on Electronics, Communication and Computational Engineering (ICECCE) IEEE,, 2014, p. 67-71.
[20] Revathi, P., and M. Hemalatha, “Classification of cotton leaf spot diseases using image processing edge detection techniques”, In 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET),. IEEE, 2012. p. 169-173
[21] Tian, Chunwei, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin., “Deep learning on image denoising: An overview”, Neural Networks, 2020.
[22] Hussain, H.; Gao, H. and Shaikh, R.A. (2016). Segmentation of Connected Characters in Text-Based CAPTCHAs for Intelligent Character Recognition. Multimedia Tools and Applications
[23] L. D. Apostolopoulos, T. A. Mpesiana, “Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks”, Physical and Engineering Sciences in Medicine 43, no.2, 2020, p.635-640.