Abstract
Plant leaf disease (PLD) recognition’s current techniques lack proper segmentation and locating similar disorders due to overlapping features in different plants. For this reason, we propose a framework to overcome the challenges of tracing Region of Interest(ROI) under different image backgrounds, uneven orientations, and illuminations. Initially, modified Adaptive Centroid Based Segmentation (ACS) is applied to find K’s optimal value from PLDs and then detect ROIs accurately, irrespective of the background. Later, features are extracted using a modified Histogram Based Local Ternary Pattern (HLTP) that outperforms for PLDs with uneven illumination and orientation, capitalizing on linear interpolation and statistical threshold in neighbors. Finally, Histogram-based gradient boosting is utilized to reduce biasness for similar features while detecting disorders. The proposed framework recognizes twelve PLDs having an overall accuracy of 99.34% while achieves 98.51% accuracy for PLDs with more than one symptom, for instance, fungal and bacterial symptoms.
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
Vasilyev, A.A., Vasilyev, G.N.S.: Processing plants for post-harvest disinfection of grain. In: Proceedings of the 2nd International Conference on Intelligent Computing and Optimization (ICO 2019) , Advances in Intelligent Systems and Computing 1072, 501–505 (2019)
Borse, K., Agnihotri, P.G.: Prediction of crop yields based on fuzzy rule-based system (FRBS) using the takagi sugeno-kang approach. In: Proceedings of the International Conference on Intelligent Computing and Optimization (ICO 2018), Advances in Intelligent Systems and Computing 866, 438–447 (2018)
Boulent, J., Foucher, S., Théau, J., St-Charles, P.L.: Convolutional neural networks for the automatic identification of plant diseases. Frontiers in Plant Science 10 (2019)
Brahimi, M., Mahmoudi, S., Boukhalfa, K., Moussaoui, A.: Deep interpretable architecture for plant diseases classification. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), pp. 111–116. IEEE (2019)
Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agriculture 145, 311–318 (2018)
Ke, G., Meng, Q., Finey, T., Wang, T., Chen, Ma, W., Ye, Q., Liu, T.Y.: Lightgbm: a highly efficient gradient boosting decision tree. In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. pp. 1–3 (2017)
Liang, W.J., Zhang, H., Zhang, G.F., Cao, H.X.: Rice blast disease recognition using a deep convolutional neural network. Scientific Reports 9(1), 1–10 (2019)
Mohanty, S.P., Hughes, D.P., Salathé, M.: Using deep learning for image-based plant disease detection. Front. Plant Sci. 7, 1419 (2016)
Pantazi, X., Moshou, D., Tamouridou, W.: Automated leaf disease detection in different crop species through image feature analysis and one class classifiers. Comput. Electron. Agric. 156, 96–104 (2019)
Sharma, P., Berwal, Y.P.S., Ghai, W.: Performance analysis of deep learning cnn models for disease detection in plants using image segmentation. Information Processing in Agriculture (2019)
Singh, V., Misra, A.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agric. 4, 41–49 (2017)
Taha H., Rassem, B.E.K.: Completed local ternary pattern for rotation invariant texture classification. The Scientific World Journal, p. 10 (2014)
Too, E.C., Yujian, L., Njuki, S., Yingchun, L.: A comparative study of fine-tuning deep learning models for plant disease identification. Comput. Electron. Agric. 161, 272–279 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Hossain, S.M.M., Deb, K. (2021). Plant Leaf Disease Recognition Using Histogram Based Gradient Boosting Classifier. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_47
Download citation
DOI: https://doi.org/10.1007/978-3-030-68154-8_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68153-1
Online ISBN: 978-3-030-68154-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)