Abstract
Agriculture has played important role in the rise of human civilization. It is the backbone of our economic system and is highly dependent on the horticulture. Various diseases may affect the plants which are to be handled by the farmers within time to increase their productivity. Most diseases affecting plants can be diagnosed at an early stage by using their leaf to improve both quality and quantity of fruits. But detection of leaf disease at an early stage is a challenging task. So, to overcome this situation various researchers have presented different techniques some of which were very expensive and can be used by only trained persons. This paper presents a technique for detection and classification of citrus leaf diseases based on texture and color features extracted after using CES enhancement and segmentation of the diseased part. Segmentation is done by using k-means clustering technique. Color features are extracted by separating each component in HSV, YCbCr and LAB color spaces. Feature selection has been done based on ANOVA F-test to skip irrelevant features. Finally, the classification is done with support vector machine, linear discriminant analysis, k-nearest neighbors and multi-layer perceptron. The accuracy parameter is used to analyze the performance of proposed method. The results obtained are quite encouraging.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pandey, R.K., Kumar, S., Jha, R.K.: Crop monitoring using IoT: a neural network approach. In: Proceedings of Soft Computing: Theories and Applications (SoCTA), pp. 123–132 (2017)
Shaikh, R.P., Dhole S.A.: Citrus leaf unhealthy region detection by using image processing technique. In: International Conference on Electronics, Communication and Aerospace Technology—ICECA (2017)
Gavhale, K.R., Hajari, K.O.: Unhealthy region of citrus leaf detection using image processing techniques. In: International Conference for Convergence of Technology (2014)
Gui, J., Hao, L., Zhang, Q., Bao, X.: New method for soybean leaf disease detection based on modified salient regions. Int. J. Multimed. Ubiquitous Eng. 10(6), 45–52 (2015)
Dey, A.K., Sharma, M., Meshram, M.R.: Image processing based leaf rot disease, detection of betel vine. In: International Conference on Computational Modeling & Security 7 (2016)
Kulkarni, H.A., Patil, A.: Applying image processing technique to detect plant diseases. Int. J. Mod. Eng. Res. 2(5), 3661–3664 (2012)
Al-Tarawneh, M.S.: An empirical investigation of olive leave spot disease using auto-crop** segmentation and fuzzy C-means classification. World Appl. Sci. J. 23(9), 1207–1211 (2013)
Ramesh, S.M., Shanmugam, A.: A new technique for enhancement of color images by scaling the discrete cosine transform coefficients. Int. J. Electron. Commun. Technol. IJECT 2(1) (2011)
Yadav, J., Sharma, M.: A review of K-mean algorithm. Int. J. Eng. Trends Technol. 4(7) (2013)
Zhou, Y., Troncoso, S.S.: A reassessment of ANOVA reporting practices: a review of three APA journals. J. Methods Meas. Soc. Sci. 8(1), 3–19 (2017)
Chandrakantha, L.: Learning ANOVA concepts using simulation. In: Proceedings of the 2014 Zone 1 Conference of the American Society for Engineering Education, Bridgeport, pp. 1–5 (2014)
Bonello, J., Garg, L., Garg, G., Audu, E.E.: Effective data acquisition for machine learning algorithm in EEG signal processing. In: Proceedings of Soft Computing: Theories and Applications (SoCTA), vol. 2, pp. 233–244 (2016)
Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, H., Rani, R., Mahajan, S. (2020). Detection and Classification of Citrus Leaf Disease Using Hybrid Features. In: Pant, M., Sharma, T., Verma, O., Singla, R., Sikander, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1053. Springer, Singapore. https://doi.org/10.1007/978-981-15-0751-9_67
Download citation
DOI: https://doi.org/10.1007/978-981-15-0751-9_67
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0750-2
Online ISBN: 978-981-15-0751-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)