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KNN-based approach for the classification of fusarium wilt disease in chickpea based on color and texture features

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Abstract

Chickpea wilt is a widespread agricultural disease that affects production worldwide every year. Rapid and accurate detection of the disease is desirable, but is difficult using traditional methods. Therefore, it is necessary to detect the disease using automatic, rapid, reliable, and simple methods before it completely damages the plant. Herein, we investigate the applicability of machine learning-based texture analysis methods to determine the severity level of Fusarium wilt in chickpea. Various procedures, such as image annotation, augmentation, resizing, and color conversion using different color spaces (RGB, HSV, and Lab*), were performed to develop the model. To perform texture feature extraction, the Gray-Level Run-Length Matrix (GLRLM) and the Gray-Level Occurrence Matrix (GLCM) feature extraction methods were used. To avoid local minima, Bayesian optimization was applied, while to train and test the effectiveness of the proposed model, 15000 images (70–20-10 ratio for training, validation and testing) were used. Finally, multi-class classification models were developed using image classification methods such as K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Networks. The proposed GLRLM-HSV based KNN model performed well in determining the severity level of fusarium wilt of chickpea among five different severity levels, with an accuracy of 94.5%.

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References

  • Aerts, H. J., Velazquez, E. R., Leijenaar, R. T., et al. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 5(1), 4006. https://doi.org/10.1038/ncomms5006

    Article  CAS  PubMed  Google Scholar 

  • Alharan, A. F., Fatlawi, H. K., & Ali, N. S. (2019). A cluster-based feature selection method for image texture classification. Indonesian Journal of Electrical Engineering and Computer Science, 14(3), 1433–1442. https://doi.org/10.11591/ijeecs.v14.i3.pp1433-1442

    Article  Google Scholar 

  • Alqudah, A. M., & Alqudah, A. (2022). Improving machine learning recognition of colorectal cancer using 3d glcm applied to different color spaces. Multimedia Tools and Applications, 81(8), 10839–10860. https://doi.org/10.1007/s11042-022-11946-9

    Article  Google Scholar 

  • Arivazhagan, S., Shebiah, R. N., Ananthi, S., & Varthini, S. V. (2013). Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal, 15(1), 211–217.

    Google Scholar 

  • Aslam, M., Shah, J. A., Hussain, N., et al. (2021). Chickpea advanced lines screening for sources of resistance against two major diseases of chickpea “wilt and blight.” Pakistan Journal of Phytopathology, 33(2), 369–382. https://doi.org/10.33866/phytopathol.033.02.0719

    Article  Google Scholar 

  • Assefa, D., Keller, H., Ménard, C., Laperriere, N., Ferrari, R. J., & Yeung, I. (2010). Robust texture features for response monitoring of glioblastoma multiforme on-weighted and-flair mr images: A preliminary investigation in terms of identification and segmentation. Medical Physics, 37(4), 1722–1736. https://doi.org/10.1118/1.3357289

    Article  PubMed  Google Scholar 

  • Aydın, M. H. (2019). Nohut (Cicer arietinum L)’ta solgunluğa neden olan Fusarium oxysporum’un biyolojik muücadelesi. Türkiye Tarımsal Araştırmalar Dergisi, 6(1), 65–72. https://doi.org/10.19159/tutad.475915

    Article  Google Scholar 

  • Azevedo, D. M., Rocha, F. S., Costa, C. A., et al. (2017). Etiology of root rot and wilt disease of chickpea in Brazil. Tropical Plant Pathology, 42, 273–283. https://doi.org/10.1007/s40858-017-0145-5

    Article  Google Scholar 

  • Bakken, M., Moore, R. J., & From, P. (2019). End-to-end learning for autonomous crop row-following. IFAC-PapersOnLine, 52(30), 102–107. https://doi.org/10.1016/j.ifacol.2019.12.505

    Article  Google Scholar 

  • Barburiceanu, S., Terebes, R. & Meza, S. (2020). Grape leaf disease classification using LBP-derived texture operators and colour. International Conference on Automation, Quality and Testing, Robotics (AQTR) (pp. 1–6). https://doi.org/10.1109/AQTR49680.2020.9130019

  • Barman, U., & Choudhury, R. D. (2020). Soil texture classification using multi class support vector machine. Information Processing in Agriculture, 7(2), 318–332. https://doi.org/10.1016/j.inpa.2019.08.001

    Article  Google Scholar 

  • Barman, U., Pathak, C. & Mazumder, N. K. (2023). Comparative assessment of pest damage identification of coconut plant using damage texture and color analysis. Multimedia Tools and Applications, 82, 25083–25105. https://doi.org/10.1007/s11042-023-14369-2

  • Bayraktar, H., & Dolar, F. (2009). Genetic diversity of wilt and root rot pathogens of chickpea, as assessed by RAPD and ISSR. Turkish Journal of Agriculture and Forestry, 33(1), 1–10. https://doi.org/10.3906/tar-0709-37

    Article  CAS  Google Scholar 

  • Bayraktar, H., Dolar, F., & Maden, S. (2008). Use of RAPD and ISSR markers in detection of genetic variation and population structure among Fusarium oxysporum f sp ciceris isolates on chickpea in Turkey. Journal of Phytopathology, 156(3), 146–154. https://doi.org/10.1111/j.1439-0434.2007.01319.x

    Article  CAS  Google Scholar 

  • Belay, A. J., Salau, A. O., Ashagrie, M., & Haile, M. B. (2022). Development of a chickpea disease detection and classification model using deep learning. Informatics in Medicine Unlocked, 31, 100970. https://doi.org/10.1016/j.imu.2022.100970

    Article  Google Scholar 

  • Benco, M., Hudec, R., Kamencay, P., Zachariasova, M., & Matuska, S. (2014). An advanced approach to extraction of colour texture features based on GLCM. International Journal of Advanced Robotic Systems, 11(7), 104. https://doi.org/10.5772/58692

    Article  Google Scholar 

  • Benčo, M., & Hudec, R. (2007). Novel method for color textures features extraction based on GLCM. Radioengineering, 16(4), 65.

    Google Scholar 

  • Bernardes, R. C., De Medeiros, A., da Silva, L., et al. (2022). Deep-learning approach for fusarium head blight detection in wheat seeds using low-cost imaging technology. Agriculture, 12(11), 1801. https://doi.org/10.3390/agriculture12111801

    Article  Google Scholar 

  • Chu, A., Sehgal, C. M., & Greenleaf, J. F. (1990). Use of gray value distribution of run lengths for texture analysis. Pattern Recognition Letters, 11(6), 415–419. https://doi.org/10.1016/0167-8655(90)90112-F

    Article  Google Scholar 

  • Das, S., & Jena, U. R. (2016). Texture classification using combination of LBP and GLRLM features along with KNN and multiclass SVM classification. In 2016 2nd international conference on communication control and intelligent systems (CCIS) (pp. 115–119). IEEE. https://doi.org/10.1109/CCIntelS.2016.7878212

  • Dasarathy, B. V., & Holder, E. B. (1991). Image characterizations based on joint gray level—run length distributions. Pattern Recognition Letters, 12(8), 497–502. https://doi.org/10.1016/0167-8655(91)80014-2

    Article  Google Scholar 

  • Dhaya, R. (2020). Flawless identification of Fusarium oxysporum in tomato plant leaves by machine learning algorithm. Journal of Innovative Image Processing (JIIP), 2(04), 194–201. https://doi.org/10.36548/jiip.2020.4.004

    Article  Google Scholar 

  • Diker, A. (2022). An efficient model of residual based convolutional neural network with Bayesian optimization for the classification of malarial cell images. Computers in Biology and Medicine, 148, 105635. https://doi.org/10.1016/j.compbiomed.2022.105635

    Article  Google Scholar 

  • Dilmaç, M., Dinler, H., & Kaki, B. (2020). Nonpatojen Fusarium spp 'lerinin nohutta Fusarium solgunluğuna karşı in vitro koşullarda antagonist etkilerinin belirlenmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 7(3), 775–792. https://doi.org/10.30910/turkjans.697883

    Article  Google Scholar 

  • Egrioglu, E., Aladag, C. H., Yolcu, U., Uslu, V. R., & Basaran, M. A. (2009). A new approach based on artificial neural networks for high order multivariate fuzzy time series. Expert Systems with Applications, 36(7), 10589–10594. https://doi.org/10.1016/j.eswa.2009.02.057

    Article  Google Scholar 

  • Elangovan, K., & Nalini, S. (2017). Plant disease classification using image segmentation and SVM techniques. International Journal of Computational Intelligence Research, 13(7), 1821–1828.

    Google Scholar 

  • Elsheikh, A. H., Muthuramalingam, T., Shanmugan, S., et al. (2021). Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718. Journal of Materials Research and Technology, 15, 3622–3634. https://doi.org/10.1016/j.jmrt.2021.09.119

    Article  CAS  Google Scholar 

  • Endes, A. (2021). Influence of culture media, temperature, pH and light regime on mycelial growth of Ascochyta rabiei. International Journal of Agriculture Forestry and Life Sciences, 5(1), 87–93.

    Google Scholar 

  • Fekri-Ershad, S. (2020). Bark texture classification using improved local ternary patterns and multilayer neural network. Expert Systems with Applications, 158, 113509. https://doi.org/10.1016/j.eswa.2020.113509

    Article  Google Scholar 

  • Fekriershad, S., & Tajeripour, F. (2017). Color texture classification based on proposed impulse-noise resistant color local binary patterns and significant points selection algorithm. Sensor Review, 37(1), 33–42. https://doi.org/10.1108/SR-07-2016-0120

    Article  Google Scholar 

  • Feng, H., Gonzalez Viejo, C., Vaghefi, N., Taylor, P. W., Tongson, E., & Fuentes, S. (2022). Early detection of Fusarium oxysporum infection in processing tomatoes (Solanum lycopersicum) and pathogen–soil interactions using a low-cost portable electronic nose and machine learning modeling. Sensors, 22(22), 8645. https://doi.org/10.3390/s22228645

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Galloway, M. M. (1975). Texture analysis using gray level run lengths. Computer Graphics and Image Processing, 4(2), 172–179. https://doi.org/10.1016/S0146-664X(75)80008-6

    Article  Google Scholar 

  • Gelbart, M. A., Snoek, J., & Adams, R. P. (2014). Bayesian optimization with unknown constraints. In Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI'14) (pp. 250–259).

  • Ghyar, B. S., & Birajdar, G. K. (2017). Computer vision based approach to detect rice leaf diseases using texture and color descriptors. International conference on inventive computing and informatics (ICICI) (pp. 1074–1078). https://doi.org/10.1109/ICICI.2017.8365305

  • Haralick, R. M., Shanmugam, K., & Dinstein, I. H. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 6, 610–621. https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  • Hashem, A., Tabassum, B. & Abd_Allah, E.F. (2020). Omics approaches in chickpea fusarium wilt disease management. In B. Singh, G. Singh, K. Kumar, S. Nayak, & N. Srinivasa (Eds.), Management of Fungal Pathogens in Pulses: Current Status and Future Challenges (pp. 57–72). Fungal Biology. Springer, Cham. https://doi.org/10.1007/978-3-030-35947-8_4

  • Hayit, T., Erbay, H., Varçın, F., Hayit, F., & Akci, N. (2021). Determination of the severity level of yellow rust disease in wheat by using convolutional neural networks. Journal of Plant Pathology, 103(3), 923–934. https://doi.org/10.1007/s42161-021-00886-2

    Article  Google Scholar 

  • Hayıt, T. (2023). An investigation of the effect of dataset sample created via image scraping on convolutional neural network based image classification. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 11(3), 1388–1398. https://doi.org/10.29130/dubited.1120967

    Article  Google Scholar 

  • Hayıt, T., Erbay, H., Varçın, F., Hayıt, F. & Akci, N. (2023). The classification of wheat yellow rust disease based on a combination of textural and deep features. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-15199-y

  • Hlaing, C. S. & Zaw, S. M. M. (2018). Tomato plant diseases classification using statistical texture feature and color feature. 17th International Conference on Computer and Information Science (ICIS) (pp. 439–444). https://doi.org/10.1109/ICIS.2018.8466483

  • Hossain, E., Hossain, M. F. & Rahaman, M. A. (2019). A color and texture based approach for the detection and classification of plant leaf disease using knn classifier. International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1–6). https://doi.org/10.1109/ECACE.2019.8679247

  • Hwang, S. W., Lee, T., Kim, H., Chung, H., Choi, J. G., & Yeo, H. (2021). Classification of wood knots using artificial neural networks with texture and local feature-based image descriptors. Holzforschung, 76(1), 1–13. https://doi.org/10.1515/hf-2021-0051

    Article  CAS  Google Scholar 

  • Iniyan, S., Jebakumar, R., Mangalraj, P., Mohit, M. & Nanda, A. (2020). Plant disease identification and detection using support vector machines and artificial neural networks. In S. Dash, C. Lakshmi, S. Das, & B. Panigrahi (Eds.), Artificial Intelligence and Evolutionary Computations in Engineering Systems, 1056, (pp. 15–27). Springer, Singapore. https://doi.org/10.1007/978-981-15-0199-9_2

  • Iqbal, S. M., Haq, I. U., Bukhari, A., Ghafoor, A., & Haqqani, A. M. (2005). Screening of chickpea genotypes for resistance against Fusarium wilt. Mycopath, 3, 1–5.

    Google Scholar 

  • Ishak, S., Rahiman, M. H. F., Kanafiah, S. N. A. M., & Saad, H. (2015). Leaf disease classification using artificial neural network. Jurnal Teknologi, 77(17), 109–114. https://doi.org/10.11113/jt.v77.6463

    Article  Google Scholar 

  • Jain, S., & Sood, M. (2019). SVM classification of cell survival/apoptotic death for color texture images of survival receptor proteins. International Journal on Emerging Technologies, 10(2), 23–28.

    Google Scholar 

  • Jiménez-Díaz, R. M., Castillo, P., del Mar Jiménez-Gasco, M., Landa, B. B., & Navas-Cortés, J. A. (2015). Fusarium wilt of chickpeas: Biology, ecology and management. Crop Protection, 73, 16–27. https://doi.org/10.1016/j.cropro.2015.02.023

    Article  Google Scholar 

  • Kairuddin, W. N. H. W., & Mahmud, W. M. H. W. (2017). Texture feature analysis for different resolution level of kidney ultrasound images. IOP Conference Series: Materials Science and Engineering, 226(1), 012136. https://doi.org/10.1088/1757-899X/226/1/012136

    Article  Google Scholar 

  • Khaldi, B., Khaldi, Y., Azzaoui, H., Aiadi, O. & Kherfi, M. L. (2022) Morphological operations and artificial neural networks for multi-scale colored texture classification. 4th International Conference on Pattern Analysis and Intelligent Systems (PAIS) (pp. 1–6). https://doi.org/10.1109/PAIS56586.2022.9946877

  • Kulinavar, P., & Hadimani, V. I. (2017). Classification of leaf disease based on multiclass SVM classifier. International Journal of Advance Research, Ideas and Innovations in Technology, 3(4), 321–327.

    Google Scholar 

  • Kumar, K. V., Shanmuga Priyan, R. & Santhosh, V. S. (2021). Plant disease classification using image segmentation and SVM techniques. Annals of the Romanian Society for Cell Biology, 25(4), 11204–11211.

  • Kumari, C. U., Prasad, S. J. & Mounika, G. (2019). Leaf disease detection: feature extraction with K-means clustering and classification with ANN. 3rd international conference on computing methodologies and communication (ICCMC) (pp. 1095–1098). https://doi.org/10.1109/ICCMC.2019.8819750

  • Lahmiri, S., Tadj, C., Gargour, C., & Bekiros, S. (2023). Optimal tuning of support vector machines and k-NN algorithm by using Bayesian optimization for newborn cry signal diagnosis based on audio signal processing features. Chaos, Solitons & Fractals, 167, 112972. https://doi.org/10.1016/j.chaos.2022.112972

    Article  Google Scholar 

  • Landa, B. B., Navas-Cortés, J. A., & Jiménez-Díaz, R. M. (2004). Integrated management of Fusarium wilt of chickpea with sowing date, host resistance, and biological control. Phytopathology, 94(9), 946–960. https://doi.org/10.1094/PHYTO.2004.94.9.946

    Article  PubMed  Google Scholar 

  • Landa, B. B., Navas-Cortés, J. A., del Mar Jimenez-Gasco, M., Katan, J., Retig, B., & Jiménez-Díaz, R. M. (2006). Temperature response of chickpea cultivars to races of Fusarium oxysporum f. sp. ciceris causal agent of Fusarium wilt. Plant Disease, 90(3), 365–374. https://doi.org/10.1094/PD-90-0365

    Article  PubMed  Google Scholar 

  • Leslie, J.F. & Summerell, B.A. (2006) The Fusarium Laboratory Manual. Blackwell Publishing, Hoboken, (pp. 1–2). https://doi.org/10.1002/9780470278376

  • Madiwalar, S. C. & Wyawahare, M. (2017). Plant disease identification: A comparative study.  International Conference on Data Management, Analytics and Innovation (ICDMAI) (pp. 13–18). https://doi.org/10.1109/ICDMAI.2017.8073478

  • Manisha, Dhull, S. K. & Singh, K. K. (2020). ECG beat classifiers: a journey from ANN to DNN. Procedia Computer Science, 167, 747–759. https://doi.org/10.1016/j.procs.2020.03.340

  • Nalini, T. & Rama, A. (2022). Impact of temperature condition in crop disease analyzing using machine learning algorithm. Measurement: Sensors, 24, 100408. https://doi.org/10.1016/j.measen.2022.100408

  • Nene, Y. L., Reddy, M. V., Haware, M. P., Ghanekar, A. M., Amin, K. S., Pande, S. & Sharma, M. (2012). Field diagnosis of chickpea diseases and their control. information bulletin no. 28 (revised). Technical Report. International Crops Research Institute for the Semi-Arid Tropics, Patancheru, Andhra Pradesh, India.

  • Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51–59. https://doi.org/10.1016/0031-3203(95)00067-4

    Article  Google Scholar 

  • Panigrahi, K.P., Das, H., Sahoo, A.K. & Moharana, S.C. (2020). Maize Leaf Disease Detection and Classification Using Machine Learning Algorithms. In H. Das, P. Pattnaik, S. Rautaray, & K. C. Li (Eds.), Progress in Computing, Analytics and Networking, 1119, (pp. 659–669). Springer, Singapore. https://doi.org/10.1007/978-981-15-2414-1_66

  • Patnaik, V., Mohanty, M., & Subudhi, A. K. (2021). Identification of healthy biological leafs using hybrid-feature classifier. The Imaging Science Journal, 69(5–8), 239–253. https://doi.org/10.1080/13682199.2022.2157533

    Article  Google Scholar 

  • Pham, T. N., Van Tran, L., & Dao, S. V. T. (2020). Early disease classification of mango leaves using feed-forward neural network and hybrid metaheuristic feature selection. IEEE Access, 8, 189960–189973. https://doi.org/10.1109/ACCESS.2020.3031914

    Article  Google Scholar 

  • Pietikainen, M. K. (2000). Texture analysis in machine vision. World Scientific. https://doi.org/10.1142/4483

    Article  Google Scholar 

  • Prakash, R. M., Saraswathy, G. P., Ramalakshmi, G., Mangaleswari, K. H. & Kaviya, T. (2017). Detection of leaf diseases and classification using digital image processing. International conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1–4). https://doi.org/10.1109/ICIIECS.2017.8275915

  • Qiu, R., Yang, C., Moghimi, A., Zhang, M., Steffenson, B. J., & Hirsch, C. D. (2019). Detection of fusarium head blight in wheat using a deep neural network and color imaging. Remote Sensing, 11(22), 2658. https://doi.org/10.3390/rs11222658

    Article  Google Scholar 

  • Raju, P. P. C., Balachander, B. & Neeharika, S. (2022). Comparison of haralick texture features and gray level run length matrix features for analyzing textural variation in cotton leaves to identify spot disease. International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) (pp. 1–17). https://doi.org/10.1109/MACS56771.2022.10023043

  • Rangarajan, A. K., Whetton, R. L., & Mouazen, A. M. (2022). Detection of fusarium head blight in wheat using hyperspectral data and deep learning. Expert Systems with Applications, 208, 118240. https://doi.org/10.1016/j.eswa.2022.118240

    Article  Google Scholar 

  • Ranjan, M., Weginwar, M. R., Joshi, N., & Ingole, A. B. (2015). Detection and classification of leaf disease using artificial neural network. International Journal of Technical Research and Applications, 3(3), 331–333.

    Google Scholar 

  • Resti, Y., Irsan, C., Putri, M. T., Yani, I., Ansyori, A., & Suprihatin, B. (2022). Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor. Science and Technology Indonesia, 7(1), 29–35. https://doi.org/10.26554/sti.2022.7.1.29-35

    Article  Google Scholar 

  • Salau, A. O. & Jain, S. (2019). Feature extraction: a survey of the types, techniques, applications. International conference on signal processing and communication (ICSC) (pp. 158–164). https://doi.org/10.1109/ICSC45622.2019.8938371

  • Saragih, T. H., Fajri, D. M. N. & Rakhmandasari, A. (2020). Comparative study of decision tree, K-nearest neighbor, and modified K-nearest neighbor on jatropha curcas plant disease identification. KINETIK: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 5(1). https://doi.org/10.22219/kinetik.v5i1.1012

  • Shah, N. & Jain, S. (2019). Detection of disease in cotton leaf using artificial neural network. Amity International Conference on Artificial Intelligence (AICAI) (pp. 473–476). https://doi.org/10.1109/AICAI.2019.8701311

  • Shakoor, M. H., Boostani, R., Sabeti, M., & Mohammadi, M. (2023). Feature selection and mapping of local binary pattern for texture classification. Multimedia Tools and Applications, 82(5), 7639–7676. https://doi.org/10.1007/s11042-022-13470-2

    Article  Google Scholar 

  • Sharma, M., Nagavardhini, A., Thudi, M., Ghosh, R., Pande, S., & Varshney, R. K. (2014). Development of DArT markers and assessment of diversity in Fusarium oxysporum f sp ciceris wilt pathogen of chickpea (Cicer arietinum L.). BMC genomics, 15(1), 1–14. https://doi.org/10.1186/1471-2164-15-454

    Article  CAS  Google Scholar 

  • Shearer, S. A., & Holmes R. G. (1990). Plant identification using color co-occurrence matrices. Transactions of the ASABE, 33, 1237–1244.

  • Singh, R. N., Krishnan, P., Bharadwaj, C., & Das, B. (2023). Improving prediction of chickpea wilt severity using machine learning coupled with model combination techniques under field conditions. Ecological Informatics, 73, 101933. https://doi.org/10.1016/j.ecoinf.2022.101933

    Article  Google Scholar 

  • Stockman, G. & Shapiro, L. G. (2001). Computer Vision. Upper Saddle River, NJ, USA: Prentice Hall PTR.

  • Su, W. H., Zhang, J., Yang, C., Page, R., Szinyei, T., Hirsch, C. D., & Steffenson, B. J. (2020). Automatic evaluation of wheat resistance to fusarium head blight using dual mask-RCNN deep learning frameworks in computer vision. Remote Sensing, 13(1), 26. https://doi.org/10.3390/rs13010026

    Article  Google Scholar 

  • Suttapakti, U. & Bunpeng, A. (2019). Potato leaf disease classification based on distinct color and texture feature extraction. International Symposium on Communications and Information Technologies (ISCIT) (82–85). https://doi.org/10.1109/ISCIT.2019.8905128

  • Thai, L. H., Hai, T. S., & Thuy, N. T. (2012). Image classification using support vector machine and artificial neural network. International Journal of Information Technology and Computer Science, 4(5), 32–38. https://doi.org/10.5815/ijitcs.2012.05.05

    Article  Google Scholar 

  • Thibault, G., Fertil, B., Navarro, C., Pereira, S., Cau, P., Levy, N., Sequeira, J., & Mari J.L. (2013). Shape and texture indexes application to cell nuclei classification. International Journal of Pattern Recognition and Artificial Intelligence, 27(01), 1357002. https://doi.org/10.1142/S0218001413570024

    Article  Google Scholar 

  • Thibault, G. (2009). Indices de forme et de texture: de la 2D vers la 3D: application au classement de noyaux de cellules (Doctoral dissertation, Aix-Marseille 2)

  • Üreten, K., Erbay, H., & Maraş, H. H. (2020). Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network. Clinical Rheumatology, 39, 969–974. https://doi.org/10.1007/s10067-019-04487-4

    Article  PubMed  Google Scholar 

  • Vaishnnave, M. P., Devi, K. S., Srinivasan, P. & Jothi, G. A. P. (2019). Detection and classification of groundnut leaf diseases using KNN classifier. International Conference on System, Computation, Automation and Networking (ICSCAN) (pp. 1–5). https://doi.org/10.1109/ICSCAN.2019.8878733

  • Victoria, A. H., & Maragatham, G. (2021). Automatic tuning of hyperparameters using Bayesian optimization. Evolving Systems, 12, 217–223. https://doi.org/10.1007/s12530-020-09345-2

    Article  Google Scholar 

  • VijayaLakshmi, B., & Mohan, V. (2016). Kernel-based PSO and FRVM: An automatic plant leaf type detection using texture, shape, and color features. Computers and Electronics in Agriculture, 125, 99–112. https://doi.org/10.1016/j.compag.2016.04.033

    Article  Google Scholar 

  • Wang, Z., Yin, C. & Zhao, W. (2011). GLCM parameters of channel texture analysis. International Exposition and Annual Meeting, San Antonio, Texas, 18–23 September, 2011, SEG-2011-1989.

  • Westerlund, F. V., Campbell, R. N., & Kimble, K. A. (1974). Fungal root rots and wilt of chickpea in California. Phytopathology, 64(4), 432–436.

    Google Scholar 

  • Xian, T. S., & Ngadiran, R. (2021). Plant diseases classification using machine learning. In Journal of Physics: Conference Series, 1962(1), 012024. https://doi.org/10.1088/1742-6596/1962/1/012024

    Article  Google Scholar 

  • Xing, Z., & Jia, H. (2019). Multilevel color image segmentation based on GLCM and improved salp swarm algorithm. IEEE Access, 7, 37672–37690. https://doi.org/10.1109/ACCESS.2019.2904511

    Article  Google Scholar 

  • Yazid, M. H. B. A., Talib, M. S., & Satria, M. H. (2019). Flower pollination neural network for heart disease classification. In IOP Conference Series: Materials Science and Engineering, 551(1), 012072. https://doi.org/10.1088/1757-899X/551/1/012072

    Article  Google Scholar 

  • Yıldırım, Ü. A., & Güldür, M. E. (2019). Tescilli nohut çeşitlerinde fusarium dayanıklılığıının belirlenmesi. Harran Tarım ve Gıda Bilimleri Dergisi, 23(2), 218–225. https://doi.org/10.29050/harranziraat.461816

    Article  Google Scholar 

  • Yurttakal, A. H., Erbay, H., İkizceli, T., & Karaçavuş, S. (2020). Detection of breast cancer via deep convolution neural networks using MRI images. Multimedia Tools and Applications, 79, 15555–15573. https://doi.org/10.1007/s11042-019-7479-6

    Article  Google Scholar 

  • Zhang, H., Hung, C. L., Min, G., Guo, J. P., Liu, M., & Hu, X. (2019). GPU-accelerated GLRLM algorithm for feature extraction of MRI. Scientific Reports, 9(1), 10883. https://doi.org/10.1038/s41598-019-46622-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This study was supported with project 221O532 by The Scientific and Technological Research Council of Türkiye (TÜBİTAK) and project 6602c-ZF/18-230 by Yozgat Bozok University Scientific Research Project Coordination Unit.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering and Architecture, Yozgat Bozok University, Yozgat, 66900, Türkiye

    Tolga Hayit

  2. Department of Plant Protection, Faculty of Agriculture, Yozgat Bozok University, Yozgat, 66900, Türkiye

    Ali Endes

  3. Yozgat Bozok University, Tourism Faculty Department of Gastronomy and Culinary Arts, Yozgat Bozok University, Yozgat, 66900, Türkiye

    Fatma Hayit

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  1. Tolga Hayit
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  2. Ali Endes
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  3. Fatma Hayit
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Contributions

TH designed the models and analyzed the data. TH and AE collected images. AE and FH labeled images and supervised the experiments. All authors wrote the manuscript and read and approved the final manuscript.

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Correspondence to Tolga Hayit.

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The authors declare that they have no potential conflict of interest.

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Ali Endes and Fatma Hayit contributed equally to this work.

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Hayit, T., Endes, A. & Hayit, F. KNN-based approach for the classification of fusarium wilt disease in chickpea based on color and texture features. Eur J Plant Pathol 168, 665–681 (2024). https://doi.org/10.1007/s10658-023-02791-z

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