Recognition of Disease in Leaves Using Genetic Algorithm and Neural Network Based Feature Selection

D Angayarkanni; L Jayasimman

doi:10.17485/IJST/v16i19.218

Article

Recognition of Disease in Leaves Using Genetic Algorithm and Neural Network Based Feature Selection

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i19.218

Year: 2023, Volume: 16, Issue: 19, Pages: 1444-1452

Original Article

Recognition of Disease in Leaves Using Genetic Algorithm and Neural Network Based Feature Selection

D Angayarkanni^1*, L Jayasimman²

¹Research Scholar, Department of Computer Science, Bishop Heber College (Autonomous), Affiliated to Bharathidasan University, Trichy, India
²Assistant Professor, Department of Computer Science, Bishop Heber College (Autonomous), Affiliated to Bharathidasan University, Trichy, India

*Corresponding Author
Email: [email protected]

Received Date:30 January 2023, Accepted Date:08 April 2023, Published Date:14 May 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives : To suggest a suitable image recognition approach for the early recognition of leaf diseases using hybrid features with genetic algorithm and neural network feature selection technique to maximize the accuracy. Methods: Various image processing techniques are utilized to recognize disease in the leaf. In the pre-processing phase, CNN based de-noising is utilized to remove noise from the image. Next, disease part of the leaf is segmented by Pixel wise Classification approach with an optimization technique. Then, the colour and the segmented area of an image is used to remove out texture features. With GLCM and SIFT technique. Then the appropriate features are selected by Genetic Algorithm and Neural Network based feature selection. Extracted features are classified using an Ensemble classifier for recognizing disease in the leaf. For this experiment images are taken from the plant village data set. In this work, the disease caused by Alternaria solani and Pytopthara infestans pathogens is considered for recognizing the disease in the leaf. 1500 leaves are used. From each image, 1654 features are extracted. There is a 70% training and 30% test data split. Classification accuracy, precision and recall measures are considered for evaluating the proposed work’s performance. Findings: The proposed work gives 97.7% classification accuracy, 97.3% precision and 97.5% recall measures with various visual feature descriptors and GANN feature selection. Novelty: The in-depth investigation compares the proposed descriptor GANN_SVM and GANN ES detection and classification technique to local and global SVMs. The suggested descriptor outperforms current approaches for diagnosing Alternaria solani and Pytopthara infestans leaves and the method can be applied to all plants infected leaves.

Keywords: Genetic Algorithm; Leaf disease detection; Image processing; Neural Network; ensemble classifier

References

Jose D, Santhi K. Early Detection and Classification of Apple Leaf Diseases by utilizing IFPA Genetic Algorithm with MC-SVM, SVI and Deep Learning Methods. Indian Journal Of Science And Technology. 2022;15(29):1440–1450. Available from: https://doi.org/10.17485/IJST/v15i29.1235
Singh V. Sunflower leaf diseases detection using image segmentation based on particle swarm optimization. Artificial Intelligence in Agriculture. 2019;3:62–68. Available from: https://doi.org/10.1016/j.aiia.2019.09.002
Jadhav SB, Udup VR, Patil SB. Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier. International Journal of Electrical and Computer Engineering (IJECE). 2019;9(5):4077. Available from: http://doi.org/10.11591/ijece.v9i5.pp4077-4091
Kalaivani S, Shantharajah SP, Padma T. Agricultural leaf blight disease segmentation using indices based histogram intensity segmentation approach. Multimedia Tools and Applications. 2020;79(13-14):9145–9159. Available from: https://doi.org/10.1007/s11042-018-7126-7
Tian K, Li J, Zeng J, Evans A, Zhang L. Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Computers and Electronics in Agriculture. 2019;165:104962. Available from: https://doi.org/10.1016/j.compag.2019.104962
Chiesa M, Maioli G, Colombo GI, Piacentini L. GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets. BMC Bioinformatics. 2020;21(1):1–11. Available from: https://doi.org/10.1186/s12859-020-3400-6
Javidan SM, Banakar A, Vakilian KA, Ampatzidis Y. Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning. Smart Agricultural Technology. 2023;3:100081. Available from: https://doi.org/10.1016/j.atech.2022.100081
Nanehkaran YA, Zhang D, Chen J, Tian Y, Al-Nabhan N. Recognition of plant leaf diseases based on computer vision. Journal of Ambient Intelligence and Humanized Computing. 2020. Available from: https://doi.org/10.1007/s12652-020-02505-x
Panigrahi KP, Das H, Sahoo AK, Moharana SC. Maize leaf disease detection and classification using machine learning algorithms. 2020. Available from: https://doi.org/10.1007/978-981-15-2414-1_66
Kumar A, Kumar A, Vishwakarma AK. Multilevel Crop Image Segmentation using Bacterial Foraging Optimization Based on Minimum Cross Entropy. 2021 International Conference on Control, Automation, Power and Signal Processing (CAPS). 2021. Available from: https://doi.org/10.1109/CAPS52117.2021.9730680
Mukhopadhyay S, Paul M, Pal R, De D. Tea leaf disease detection using multi-objective image segmentation. 2020. Available from: https://doi.org/10.1007/s11042-020-09567-1
Anasta N, Setyawan FXA, Fitriawan H. Disease detection in banana trees using an image processing-based thermal camera. IOP Conference Series: Earth and Environmental Science. 2021;739(1):012088. Available from: https://doi.org/10.1088/1755-1315/739/1/012088
Kartikeyan P, Shrivastava G. Review on Emerging Trends in Detection of Plant Diseases using Image Processing with Machine Learning. International Journal of Computer Applications. 2021;174(11):39–48. Available from: https://doi.org/10.5120/ijca2021920990
Restrepo-Arias JF, Branch-Bedoya JW, Awad G. Plant Disease Detection Strategy Based on Image Texture and Bayesian Optimization with Small Neural Networks. Agriculture. 1964;12(11):1964. Available from: https://doi.org/10.3390/agriculture12111964
Harakannanavar SS, Rudagi JM, Puranikmath VI, Siddiqua A, Pramodhini R. Plant leaf disease detection using computer vision and machine learning algorithms. Global Transitions Proceedings. 2022;3(1):305–310. Available from: https://doi.org/10.1016/j.gltp.2022.03.016
Ulutaş H, Aslantaş V. Design of Efficient Methods for the Detection of Tomato Leaf Disease Utilizing Proposed Ensemble CNN Model. Electronics. 2023;12(4):827. Available from: https://doi.org/10.3390/electronics12040827
Rehman S, Khan MA, Alhaisoni M, Armghan A, Tariq U, Alenezi F, et al. A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition. Computers, Materials & Continua. 2023;75(1):697–714. Available from: https://doi.org/10.32604/cmc.2023.035183
Abasabadi S, Nematzadeh H, Motameni H, Akbari E. Hybrid feature selection based on SLI and genetic algorithm for microarray datasets. The Journal of Supercomputing. 2022;78(18):19725–19753. Available from: https://doi.org/10.1007/s11227-022-04650-w
Wu Z, Jiang F, Cao R. Research on recognition method of leaf diseases of woody fruit plants based on transfer learning. Scientific Reports. 2022;12(1):15385. Available from: https://doi.org/10.1038/s41598-022-18337-y
Sathiya V, Josephine DMS, Dr V, Jeyabalaraja. Plant Disease Classification of Basil and Mint Leaves using Convolutional Neural Networks”. International Journal of Intelligent Systems and Applications in Engineering. 2023;(2) 153–163. Available from: https://ijisae.org/index.php/IJISAE/article/view/2606/1188
Alshammari H, Gasmi K, Krichen M, Ammar LB, Abdelhadi MO, Boukrara A, et al. Optimal Deep Learning Model for Olive Disease Diagnosis Based on an Adaptive Genetic Algorithm. Wireless Communications and Mobile Computing. 2022;2022:1–13. Available from: https://doi.org/10.1155/2022/8531213
Find open datasets and Machine Learning Projects. Available from: https://www.kaggle.com/datasetsa

Copyright

© 2023 Angayarkanni & Jayasimman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)