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Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model

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A Correction to this article was published on 18 January 2024

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Abstract

The identification of diseases in plants contributes an important role in captivating disease control methods for the improvement of quality and quantity of crop yield. Mango trees are affected by different diseases and the identification of diseases is a tedious task till now when those diseases are manually detected. This paper proposes the novel hybrid Coyote Grey Wolf optimization (CO-GWO) algorithm for the classification of mango leaves as normal or diseased. The classification process is done through the extraction of significant features from the segmented image. The Neural network (NN) classifier performs the classification task, with the weights being adjusted using the proposed algorithm that acts a major role in the enhancement of the classification accuracy. The effectiveness of the proposed model is evaluated concerning the evaluation metrics, namely accuracy, precision, recall, and F1 measure, and is attained to be 96.7111%, 97.5712%, 97.1504%, and 96.4792%, respectively. This shows the superiority of the proposed technique in the effective classification of mango leaf classification as compared with the existing techniques.

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References

  1. Mutengwa CS, Mnkeni P, Kondwakwenda A (2023) Climate-smart agriculture and food security in Southern Africa: a review of the vulnerability of smallholder agriculture and food security to climate change. Sustainability 15(4):2882

    Article  Google Scholar 

  2. Bhadra S, Dyer AR (2022) Resilience and well-being among the survivors of natural disasters and conflicts. Handbook of health and well-being: challenges, strategies and future trends. Singapore: Springer Nature Singapore, pp 637–667

  3. Chakma S et al (2022) Adapting land degradation and enhancing ethnic livelihood security through fruit production: Evidence from hilly areas of Bangladesh. Agro-biodiversity and Agri-ecosystem Management. Singapore: Springer Nature Singapore 217-238

  4. Kumar, P et al (2023) Achieving biodiversity conservation, livelihood security and sustainable development goals through agroforestry in coastal and island regions of India and Southeast Asia. Agroforestry for sustainable intensification of agriculture in Asia and Africa. Singapore: Springer Nature Singapore, pp 429–486

  5. Kandegama WM, Wishwajith W et al (2022) Impacts of climate change on horticultural crop production in Sri Lanka and the potential of climate-smart agriculture in enhancing food security and resilience. Food Security and Climate-Smart Food Systems: Building Resilience for the Global South. Cham: Springer International Publishing, 67–97

  6. Gurumita NG, Ramesh GP (2022) Mango leaf disease detection using ultrasonic sensor. IEEE International Conference on Data Science and Information System (ICDSIS). IEEE

  7. Rahaman Md et al (2023) A deep learning based smartphone application for detecting mango diseases and pesticide suggestions. Int J Comput Digit Syst 13(1):1–1

  8. Molina-Cárdenas L et al (2023) First report of mango malformation disease caused by Fusarium proliferatum in Mexico. Plant Dis 107(2):581

    Article  Google Scholar 

  9. Ma Y-W, Chen J-L, Shih C-C (2022) An Automatic and Intelligent Internet of Things for Future Agriculture. IT Professional 24(6):74–80

    Article  Google Scholar 

  10. Kumari S, Kumari N (2022) Plant leaf disease identification using machine learning. 11th International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE

  11. Garg R, Sandhu AK, Kaur B (2023) A systematic analysis of various techniques for mango leaf disease detection. International Conference on Disruptive Technologies (ICDT). IEEE

  12. Mohapatra M et al (2022) Botanical leaf disease detection and classification using convolutional neural network: a hybrid metaheuristic enabled approach. Computers 11(5):82

    Article  Google Scholar 

  13. Mohapatra, Madhumini et al (2022) Mango leaf disease detection based on deep learning approach. International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). IEEE

  14. Gautam Vinay et al (2023) ESDNN: A novel ensembled stack deep neural network for mango leaf disease classification and detection. Multimed Tools Appl 1–27

  15. Jain S, Jaidka P (2023) Mango leaf disease classification using deep learning hybrid model. International Conference on Power, Instrumentation, Energy and Control (PIECON). IEEE

  16. Selvakumar A, Balasundaram A (2023) Automated mango leaf infection classification using weighted and deep features with optimized recurrent neural network concept. Imaging Sci J 1–19

  17. Mahbub NI et al (2023) Detect bangladeshi mango leaf diseases using lightweight convolutional neural network. International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE

  18. Sharma A, Kaur H, Prashar D (2023) Generative adversarial networks based approach for data augmentation in mango leaf disease detection system. IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT). IEEE

  19. Saravanan TM et al (2023) Prediction of mango leaf diseases using convolutional neural network. 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE

  20. Mia MR, Roy S, Das SK, Rahman MA (2020) Mango leaf disease recognition using neural network and support vector machine. Iran J Comput Sci 3(3):185–193

    Article  Google Scholar 

  21. Arivazhagan S, Vineth Ligi S (2018) Mango leaf diseases identification using convolutional neural network. Int J Pure Appl Mathematics 120(6):11067–11079

    Google Scholar 

  22. Gining RAJM, Fauzi SSM, Yusoff NM, Razak TR, Ismail MH, Zaki NA, Abdullah F (2021) Harumanis mango leaf disease recognition system using image processing technique. IJEECS 23(1):378–386

    Article  Google Scholar 

  23. Rajbongshi A, Khan T, Pramanik MMRA, Tanvir SM, Siddiquee NRC (2021) Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach. IJEECS 23(3):1681–1688

    Article  Google Scholar 

  24. Rao U, Sanath R, Swathi V, Sanjana L, Arpitha KC, Naik PK (2021) Deep learning precision farming: grapes and mango leaf disease detection by transfer learning. Glob Transit Proceed 2(2):535–544

    Article  Google Scholar 

  25. Rao PRK, Swathi K (2020) Mango plant disease detection using modified multi support vector machine algorithm. PalArch’s Journal of Archaeology of Egypt/Egyptology 17(7):10567–10577

    Google Scholar 

  26. Deeba K, Amutha B (2020) ResNet-deep neural network architecture for leaf disease classification. Microprocess Microsyst 103364

  27. Prasetyo E, Adityo RD, Suciati N, Fatichah C (2018) Mango leaf classification with boundary moments of centroid contour distances as shape features. In 2018 International Seminar on Intelligent Technology and Its Applications (ISITIA), pp. 317–320. IEEE

  28. Jabid T, Kabir MH, Chae O (2010) Local directional pattern (LDP) for face recognition." In 2010 digest of technical papers international conference on consumer electronics (ICCE), pp. 329–330. IEEE

  29. Chakraborti T, McCane B, Mills S, Pal U (2018) Loop descriptor: local optimal-oriented pattern. IEEE Signal Process Lett 25(5):635–639

    Article  Google Scholar 

  30. Chouhan SS, Singh UP, Jain S (2020) Web facilitated anthracnose disease segmentation from the leaf of mango tree using Radial Basis Function (RBF) neural network. Wirel Pers Commun 113(2):1279–1296

    Article  Google Scholar 

  31. Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Applic 30(2):413–435

    Article  Google Scholar 

  32. Pierezan J, Coelho LDS (2018) Coyote optimization algorithm: a new metaheuristic for global optimization problems. In 2018 IEEE congress on evolutionary computation (CEC), pp. 1–8. IEEE

  33. Indriani OR, Kusuma EJ, Sari CA, Rachmawanto EH (2017) Tomatoes classification using K-NN based on GLCM and HSV color space. In 2017 international conference on innovative and creative information technology (ICITech), pp. 1–6. IEEE

  34. Kour VP, Arora S (2019) Particle swarm optimization based support vector machine (P-SVM) for the segmentation and classification of plants. IEEE Access 7:29374–29385

  35. Kumar SR, Sowrirajan S (2016) Automatic leaf disease detection and classification using hybrid features and supervised classifier. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 5(6):4556–4563

  36. Jhuria M, Kumar A, Borse R (2013) Image processing for smart farming: detection of disease and fruit grading. In 2013 IEEE second international conference on image information processing (ICIIP-2013), pp. 521–526. IEEE

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Authors and Affiliations

  1. Department of Computer Science Engineering, SRM Institute of Science and Technology, Chennai, 600089, India

    J. Seetha

  2. Department of IT, Vignan’s Foundation for Science, Technology and Research (Deemed to be University) Vadlamudi, Guntur District, AP, India, 522213

    Ramakrishnan Ramanathan

  3. Department of ECE, GLA University, Mathura, Uttar Pradesh, India

    Vishal Goyal

  4. Department of Civil Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, Tamil Nadu, 602105, India

    M. Tholkapiyan

  5. Department of Computer Science Engineering, Koneru Lakshmaiah Education Foundation, Deemed to Be University, Guntur, AP, 522502, India

    C. Karthikeyan

  6. Department of ECE, Jaypee University of Engineering and Technology, Guna, 473226, India

    Ravi Kumar

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  1. J. Seetha
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  3. Vishal Goyal
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  4. M. Tholkapiyan
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  5. C. Karthikeyan
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  6. Ravi Kumar
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Correspondence to J. Seetha.

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Seetha, J., Ramanathan, R., Goyal, V. et al. Mango leaf disease classification using hybrid Coyote-Grey Wolf optimization tuned neural network model. Multimed Tools Appl 83, 17699–17725 (2024). https://doi.org/10.1007/s11042-023-16964-9

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  • DOI: https://doi.org/10.1007/s11042-023-16964-9

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