Abstract
Detecting a disease in plants is one of the challenging works. Identifying the disease through naked eyes is difficult. India is famous for agriculture. There were no modern techniques used in machine learning to find disease in banana leaf. Diseases like bacterial wilt and Black Sigatoka in banana leaf cause massive loss to the farmers. With the help of image processing technique and support vector machine algorithm, we can detect the disease called Black Sigatoka in banana leaf. Since this technique is cost effective, it is helpful for the farmers and one can easily detect the disease.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Prabukumar M, Balamurali J (2014) Image processing and pattern classification technique in a machine vision system that identifies and classifies the plant diseases based on the visual symptoms. Int. J. Adv Res Comput Sci
Al Hiary H, Bani Ahmad S Reyalat M (2014) Fast and accurate detection and classification of plant diseases. Int J Comput Appl
Namrata K (2017) Leaf based disease detection using “GLCM and SVM”. Int J Sci Eng Technol
Ijsea.com (2019) [Online]Available:https://www.ijsea.com/archive/vol.7/issue8/IJSEA0708/003
Thamizharasi A (2016) Detection and grading of diseases in banana leaves using machine learning 7(7)
Surya P, Kumar S (2013) Assessment of banana fruit maturity by image processing technique. J. Food Sci. Technol
Mainkar P, Ghorpade S, Adawadkar M (2015). Plant leaf disease detection and classification using image processing techniques. Int J Innovative Emerg Res Eng
Acknowledgements
The authors particularly wish to acknowledge all the teachers for their support, encouragement, and invaluable guidance in preparation of this research. Thanks to the kaggle.com website for the dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Upadhyay, A., Oommen, N.M., Mahadik, S. (2021). Identification and Assessment of Black Sigatoka Disease in Banana Leaf. In: Goar, V., Kuri, M., Kumar, R., Senjyu, T. (eds) Advances in Information Communication Technology and Computing. Lecture Notes in Networks and Systems, vol 135. Springer, Singapore. https://doi.org/10.1007/978-981-15-5421-6_24
Download citation
DOI: https://doi.org/10.1007/978-981-15-5421-6_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5420-9
Online ISBN: 978-981-15-5421-6
eBook Packages: EngineeringEngineering (R0)