Abstract
This article presents a neural network and machine vision-based approach to classify the vegetables as normal or affected. The farmers will have great difficulty if there is a change from one disease control to another. The examination through an open eye to classify the diseases by name is more expensive. The texture and color features are used to identify and classify different vegetables into normal or affected using a neural network and machine vision. The mixture of both the features is proved to be more effective. The results of experiments show that the proposed methodology extensively supports the accuracy in automatic detection of affected and normal vegetables. The applications in packing and grading of vegetables are the outcome of this research article.
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
Similar content being viewed by others
References
Karargyris, A., et al.: Combination of texture and shape features to detect pulmonary abnormalities in digital chest X-rays. Int. J. Pattern Recognit. Artif. Intell. 11(1), 99–106 (2016)
Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4(1), 41–49 (2016)
Raut, S., Fulsunge, A.: Plant disease detection in image processing using matlab. Int. J. Innovative Res. Sci. Eng. Technol. 6(6), 10373–10381 (2017)
Mokhtar, U., et al.: SVM-based detection of tomato leaves diseases. In: Filev, D., Filev, D., et al. (eds.) Intelligent Systems 2014. AISC, vol. 323, pp. 641–652. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11310-4_55
Ghaiwat, S.N., Arora, P.: Detection and classification of plant leaf diseases using image processing techniques: a review. Int. J. Recent Adv. Eng. Technol. 2(3), 1–7 (2014)
Badnakhe, M.R., Deshmukh, P.R.: An application of k-means clustering and artificial intelligence in pattern recognition for crop diseases. In: International Conference on Advancements in Information Technology With workshop of ICBMG 2011. IPCSIT, pp. 134–138. IACSIT Press, Singapore (2011)
Arivazhagan, S., Newlin, S.R., Ananthi, S., Varthini, S.V.: Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agric. Eng. Int. CIGR 15(1), 211–217 (2013)
Kulkarni, A.H., Patil, A.R.K.: Applying image processing technique to detect plant diseases. Int. J. Mod. Eng. Res. 2(5), 3661–3664 (2012)
Naikwadi, S., Amoda, N.: Advances in image processing for detection of plant diseases. Int. J. Appl. Innovation Eng. Manage. 2(11), 168–175 (2013)
Danti, A., Madgi, M., Anami, B.: Identification of common Indian leafy vegetables based on statistical measures on combined color and texture features. In: Sridhar, V., Sheshadri, H., Padma, M. (eds.) Emerging Research in Electronics, Computer Science and Technology. LNEE, vol. 248, pp. 381–389. Springer, New Delhi (2014). https://doi.org/10.1007/978-81-322-1157-0_38
Chaudhary, P., Chaudhari, A.K., Cheeran, N.A., Godara, S.: Color transform based approach for disease spot detection on plant leaf. Int. J. Comput. Sci. Telecommun. 3(6), 65–70 (2012)
Rumpf, T., Mahlein, A.-K., Steiner, U., Oerke, E.-C., Dehne, H.-W., Plumer, L.: Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Comput. Electron. Agric. 74(1), 91–99 (2010)
Al-Bashish, D., Braik, M., Bani-Ahmad, S.: Detection and classification of leaf diseases using K-meansbased segmentation and neural-networks-based classification. Inf. Technol. J. 10(2), 267–275 (2011)
Danti, A., Madgi, M., Anami, B.: Mean and range color features based identification of common Indian leafy vegetables. Int. J. Sig. Process. Image Process. Pattern Recognit. 5(3), 151–160 (2012)
Bernardes, A.A., et al.: Identification of foliar diseases in cotton crop. In: Tavares, J., Natal Jorge, R. (eds.) Topics in Medical Image Processing and Computational Vision. LNCVB, vol. 8, pp. 67–84. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-0726-9_4
Haralick, R.M., Shanmugam, K., Dinstein, I.: Texture features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)
Acknowledgement
We wish to express gratitude to our beloved Principal, Dr Basavaraj Anami, K.L.E. Institute of Technology, Hubballi for his advice.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Madgi, M., Danti, A. (2019). Automatic Classification of Normal and Affected Vegetables Based on Back Propagation Neural Network and Machine Vision. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1037. Springer, Singapore. https://doi.org/10.1007/978-981-13-9187-3_47
Download citation
DOI: https://doi.org/10.1007/978-981-13-9187-3_47
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9186-6
Online ISBN: 978-981-13-9187-3
eBook Packages: Computer ScienceComputer Science (R0)