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B S, Lokesh
- A Study of Emerging Image Processing and Machine Learning Methodologies for Classification of Plant Leaf Disease
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Authors
Asif Hassan
1,
Lokesh B S
2
Affiliations
1 Department of Electronics & Communication Engineering, Dr. SMCE, Bangalore, Karnataka-562132, IN
2 Department of Electronics & Communication Engineering, SIET, Tumakuru, Karnataka-572106, IN
1 Department of Electronics & Communication Engineering, Dr. SMCE, Bangalore, Karnataka-562132, IN
2 Department of Electronics & Communication Engineering, SIET, Tumakuru, Karnataka-572106, IN
Source
International Journal of Advanced Networking and Applications, Vol 13, No 4 (2022), Pagination: 5057-5062Abstract
Agriculture and productivity are extremely important to a country's economy. Plants becoming infected with diseases are a natural occurrence, but it can result in significant losses in agricultural productivity if sufficient precautions are not taken to identify the disease and apply certain pesticides in a timely manner. As a result, it's critical to have certain automated ways for detecting plant leaf diseases that save time and effort. Many people presented a number of automated approaches to detect and classify plant leaf diseases with varying levels of accuracy due to developments in image processing and machine learning techniques. In this study, we examine a number of current strategies that have been developed in this field. As a result, we may draw conclusions about the performances and what further improvements can be made to design more efficient systems in the future.Keywords
Fuzzy Logic, Gray Level Co-Inference Matrix, Image Processing, Machine Learning, Plant Leaf Disease.Full Text
References
- H. Park, J. S. Eun and S. H. Kim, "Image-based disease diagnosing and predicting of the crops through the deep learning mechanism", In Information and Communication Technology Convergence (ICTC) IEEE 2017 International Conference on, pp. 129-131, 2017.
- K. Elangovan and S. Nalini, "Plant disease classification using image segmentation and SVM techniques", International Journal of Computational Intelligence Research, vol. 13, no. 7, pp. 1821-1828, 2017.
- S. H. Lee, C. S. Chan, S. J. Mayo and P. Remagnino, "How deep learning extracts and learns leaf features for plant classification", Pattern Recognition, vol. 71, pp. 1-13, 2017.
- K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis", Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018.
- Sandesh Raut and Amit Fulsunge, "Plant Disease Detection in Image Processing Using MATLAB", IJIRSET, vol. 6, no. 6, 2017.
- Sonal P Patel and Arun Kumar Dewangan, "A Comparative Study on Various Plant Leaf Diseases Detection and Classification", (IJSRET), vol. 6, no. 3, March 2017.
- Prakash M. Mainkar, Shreekant Ghorpade and Mayur Adawadkar, "Plant Leaf Disease Detection and Classification Using Image Processing Techniques", IJIERE, vol. 2, no. 4, 2015.
- C.V. Giriraja, C. M. Siddharth, Ch. Saketa and M. Sai Kiran, "Plant health analyser", Advances in Computing Communications and Informatics (ICACCI) 2017 International Conference on, pp. 1821-1825, 2017.
- D. W. Zhang and J. Wang, "Design on image features recognition system of cucumber downy mildew based on BP algorithm", Journal of Shenyang Jianzhu University (Natural Science), vol. 25, pp. 574-578, May 2009.
- D. T. Zhao, Y. H. Chai and C. L. Zhang, "Inspection of soybean frogeye spot based on image procession", Journal of Northeast Agricultural University, vol. 41, pp. 119-124, April 2010.
- Y. W. Tian and Y. Niu, "Applied research of support vector machine on recognition of cucumber disease", Journal of Agricultural Mechanization Research, vol. 31, pp. 36-39, March 2009.
- G. L. Li, Z. H. Ma and H. G. Wang, "Image recognition of grape downy mildew and grape powdery mildew based on support vector machine", CCTA, pp. 151-162, 2011.
- R. C. Shinde, J. Mathew C and C. Y. Patil, "Wood defects classification using laws texture energy measures and supervised learning approach", Adv. Eng. Informatics, vol. 34, no. September, pp. 125135, 2017.
- A. S. Setiawan, Elysia, J. Wesley and Y. Purnama, "Mammogram Classification using Law's Texture Energy Measure and Neural Networks", Procedia Comput. Sci., vol. 59, pp. 92-97, 2015.
- M. Rachidi, A. Marchadier, C. Gadois, E. Lespessailles, C. Chappard and C. L. Benhamou, "Laws' masks descriptors applied to bone texture analysis: An innovative and discriminant tool in osteoporosis", Skeletal Radiol., vol. 37, no. 6, pp. 541-548, 2008.
- N. Kaur and V. Devendran, Research Article Plant leaf disease detection using ensemble classification and feature extraction, Turkish Journal of Computer and Mathematics Education, vol. 12, no. 11, pp. 2339-23352, 2021.
- Smita Naikwadi and Niket Amoda, "Advances in Image Processing for Detection of Plant Diseases", International Journal of Application or Innovation in Engineering & Management (IJAIEM), vol. 2, no. 11, November 2013.
- A. Adedoja, P. A. Owolawi and T. Mapayi, "Deep Learning Based on NASNet for Plant Disease Recognition Using Leave Images", 2019 International Conference on Advances in Big Data Computing and Data Communication Systems (icABCD), pp. 08851029, August 2019.