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Plant leaf disease classification and damage detection system using deep learning models

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Abstract

Agriculture is the primary source of livelihood for about 70% of the rural population in India. The crop variety cultivated in India is very diverse. There are more than 500 crop varieties grown in India. Despite the technological advances, the agricultural practices are still manual and involve less automation than western countries. Most of the diseases affecting a plant will reflect the damage in the leaves. The diseases affecting the plant can thus be identified from the leaf images. This paper presents an automatic plant leaf damage detection and disease identification system. The first stage of the proposed method identifies the type of the disease based on the plant leaf image using DenseNet. The DenseNet model is trained on images categorized according to their nature, i.e., healthy and the type of the disease. This model is then used for testing new leaf images. The proposed DenseNet model produced a classification accuracy of 100%, with fewer images used during the training stage. The second stage identifies the damage in the leaf using deep learning-based semantic segmentation. Each RGB pixel value combination in the image is extracted, and supervised training is performed on the pixel values using the 1D Convolutional Neural Network (CNN). The trained model can detect the damage present in the leaves at a pixel level. Evaluation of the proposed semantic segmentation resulted in an accuracy of 97%. The third stage suggests a remedy for the disease based on the disease type and the damage state. The proposed method detects various defects in different plants in the experimental analysis, namely apple, grape, potato, and strawberry. The proposed model is compared with the existing techniques and obtained better performance in comparison with those methods.

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References

  1. Akhtar A, Khanum A, Khan SA, Shaukat A (2013) Automated plant disease analysis (APDA): Performance Comparison of Machine Learning Techniques. IEEE International Conference on Frontiers of Information Technology (FIT), pp 60-65

  2. Barbados, Jayme GA (2018) Factors influencing the use of deep learning for plant disease recognition. Biosyst Eng 172:84-91

  3. Barbedo JGA, Koenigkan LV, Santos TT (2016) Identifying multiple plant diseases using digital image processing. Biosyst Eng 147:104–116

    Article  Google Scholar 

  4. Bhong VS, Pawar BV (2016) Study and analysis of cotton leaf disease detection using image processing. International Journal of Advanced Research in Science, Engineering and Technology 3(2):1447–1454

  5. Das D, Singh M, Mohanty SS, Chakravarty S (2020) Leaf disease detection using support vector machine. In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, pp 1036-1040

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

  7. Gavhale KR, Gawande U, Hajari KO (2014) Unhealthy region of citrus leaf detection using image processing techniques,. IEEE, International Conference for Convergence of Technology, Pune, pp 1-6

  8. Hou C, Zhuang J, Tang Yu, He Y, Miao A, Huang H, Luo S (2021) Recognition of early blight and late blight diseases on potato leaves based on graph cut segmentation. J Agric Food Res 5:100154

    Article  Google Scholar 

  9. Iqbal Z, Khan MA, Sharif M, Shah JH, Ur Rehman MH, Javed K (2018) An automated detection and classification of citrus plant diseases using image processing techniques: A review. Comput Electron Agric 153:12–32

    Article  Google Scholar 

  10. Islam M, Dinh A, Wahid K, Bhowmik P (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE). IEEE, pp 1-4

  11. Jalal AS, Dubey SR (2012) Detection and classification of apple fruit diseases using complete local binary patterns. IEEE Third International Conference on Computer and Communication Anand Singh Jalal, Shiv Ram Dubey, Technology, pp 978-0-7695-4872

  12. Kamlapurkar SR (2016) Detection of plant leaf disease using image processing approach. Int J Sci Res Publ 6(2):73–76

    Google Scholar 

  13. Kim M (2021) Apple leaf disease classification using superpixel and CNN. Advances in Computer Vision and Computational Biology. Springer, Cham, pp 99–106

    Chapter  Google Scholar 

  14. Kutty SB, Abdullah NE, Hashim H, Rahim AAA, Kusim AS, Yaakub TNT, Yunus PNAM, Rahman NFA (2013) Classification of watermelon leaf diseases using neural network analysis. IEEE, Business Engineering and Industrial Applications Colloquium (BEIAC), Langkawi, pp 459 – 464

  15. Liu X, Deng Z, Yang Y (2019) Recent progress in semantic image segmentation. Artif Intell Rev 52(2):1089–1106

    Article  Google Scholar 

  16. Luna-Benoso B, Martínez-Perales JC, Cortés-Galicia J, Flores-Carapia R, Silva-García VM (2021) Detection of diseases in tomato leaves by color analysis. Electronics 10(9):1055

    Article  Google Scholar 

  17. Mavridou E, Vrochidou E, Papakostas GA, Pachidis T, Kaburlasos VG (2019) Machine vision systems in precision agriculture for crop farming. J Imaging 5(12):89

  18. Mokhtar U, Alit MAS, Hassenian AE, Hefny H (2015) Tomato leaves diseases detection approach based on support vector machines. IEEE, pp 978-1-5090-0275-7/15

  19. Rao US, Swathi R, Sanjana V, Arpitha L, Chandrasekhar K, Naik PK (2021) International Conference on Computing Systemits Applications (ICCSA-2021): Deep Learning Precision Farming: GrapesMango Leaf Disease Detection by Transfer Learning. Global Transitions Proceedings

  20. Sachin D, Khirade AB, Patil (2015) Plant disease detection using image processing. IEEE, International Conference on Computing Communication Control and Automation, Pune, pp 768-771

  21. Sannaki SS, Rajpurohit VS, Nargund VB, Kulkarni P (2013) Diagnosis and classification of grape leaf diseases using neural network. IEEE, Tiruchengode, pp 1 – 5

  22. Sardogan M, Tuncer A, Ozen Y (2018) Plant leaf disease detection and classification based on CNN with LVQ algorithm. In 3rd International Conference on Computer Science and Engineering (UBMK). IEEE, pp 382-385

  23. Sharma P, Hans P, Grupta SC (2020) Classification of plant leaf diseases using machine learning and image preprocessing techniques. In 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, pp 480-484

  24. Smith LN, Zhang W, Hansen MF, Smith ML (2018) Innovative 3D and 2D machine vision methods for the analysis of plants and crops in the field. Comput Ind 97:122–131

    Article  Google Scholar 

  25. Steward PRA, Dougill J, Thierfelder C, Pittelkow CM, Stringer LC, Kudzala M, Shackelford GE (2018) The adaptive capacity of maize-based conservation agriculture systems to climate stress in tropical and subtropical environments: A meta-regression of yields. Agric Ecosyst Environ 251:194–202

    Article  Google Scholar 

  26. Sujatha R, Chatterjee JM, Jhanjhi NZ, Brohi SN (2021) Performance of deep learning vs machine learning in plant leaf disease detection. Microprocess Microsyst 80:103615

    Article  Google Scholar 

  27. Thomas S, Kuska MT, Bohnenkamp D, Brugger A, Alisaac E, Wahabzada M, Behmann J, Mahlein A-K (2018) Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. J Plant Dis Prot 125(1):5–20

    Article  Google Scholar 

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  1. Department of Electrical, Electronics and Communications Engineering, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India

    B. Sai Reddy & S. Neeraja

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  1. B. Sai Reddy
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  2. S. Neeraja
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Correspondence to B. Sai Reddy.

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Sai Reddy, B., Neeraja, S. Plant leaf disease classification and damage detection system using deep learning models. Multimed Tools Appl 81, 24021–24040 (2022). https://doi.org/10.1007/s11042-022-12147-0

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  • DOI: https://doi.org/10.1007/s11042-022-12147-0

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