FOREX Press I. J. of Electrical & Electronics Research
Support Open Access
  • Home/
  • IJEER-100343

Research Article |

Lime Diseases Detection and Classification Using Spectroscopy and Computer Vision

Author(s): Hardikkumar Sudhirbhai Jayswal1 and Dr. Jitendra Prabhakar Chaudhari2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 3

Publisher : FOREX Publication

Published : 22 September 2022

e-ISSN : 2347-470X

Page(s) : 677-683

DOI: https://doi.org/10.37391/IJEER.100343

Abstract

In the agricultural industry, plant diseases and pests pose the greatest risks. Lime is rich 10 source of vitamin C which works as an immunity booster in human body. Because of the late and manually diseases detection in lime causes a vast loss in crop production worldwide. The most common diseases are found in limes are lime canker, lemon scab, brown rot, sooty mould and Armillaria. In this paper we used imaging and non-imaging (spectral based sensing) methods with the combination of machine learning technique to detect the lime canker and sooty mould diseases. Image acquirement, pre-processing, segmentation and classification are all steps in the imaging methodology, which is then followed by feature extraction. In non-imaging methodology a multispectral sensor (Spectrometer) is used with 400 nm to 1000 nm wavelength to detect the diseases. training set and test set ratio is fixed for both techniques are 75% and 25% respectively. When it comes to identifying and classifying lime disease, spectroscopy has a 99% efficiency rating compared to imaging methodology's 96%.

Keywords: Plant diseases, classification, spectroscopy




Hardikkumar Sudhirbhai Jayswal, Assistant Professor, Department of Information Technology, Devang Patel Institute of Advance Technology and Research, Charotar University of Science and Technology, Gujarat, India; Email: jayswalhardik300986@gmail.com

Dr. Jitendra Prabhakar Chaudhari, Associate Professor, V T Patel Department of Electronics and Communication Engineering Chandubhai S Patel Institute College of Technology, Charotar University of Science and Technology, Gujarat, India

    [1] Balakrishna, K., & Rao, M. (2019). Tomato plant leaves disease classification using KNN and PNN. International Journal of Computer Vision and Image Processing (IJCVIP), 9 (1), 51-63.[Cross Ref]
    [2] Couture, J. J., Singh, A., Charkowski, A. O., Groves, R. L., Gray, S. M., Bethke, P. C., & Townsend, P. A. (2018). Integrating spectroscopy with potato disease management. Plant disease, 102(11), 2233-2240.[Cross Ref]
    [3] Lakshmi, T. N., Gopi, V., Sankar, T. G., Sarada, G., Lakshmi, L. M., Ramana, K. T. V., & Gopal, K. (2014). Status of diseases in sweet orange and acid lime orchards in Andhra Pradesh, India. International Journal of Current Microbiology and Applied Sciences, 3(5), 513-518.[Cross Ref]
    [4] Farber, C., Mahnke, M., Sanchez, L., & Kurouski, D. (2019). Advanced spectroscopic techniques for plant disease diagnostics. A review. TrAC Trends in Analytical Chemistry, 118, 43-49.[Cross Ref]
    [5] Swapnil Sapre, Iti Gontia-Mishra, Vishwa Vijay Thakur, Sumana Sikdar, Sharad Tiwari (2021). Chapter 20 - Molecular techniques used in plant disease diagnosis. Food Security and Plant Disease Management, Woodhead Publishing, 405-421.[Cross Ref]
    [6] Loey, M., ElSawy, A., & Afify, M. (2020). Deep Learning in Plant Diseases Detection for Agricultural Crops: A Survey. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 130 11(2), 41-58.[Cross Ref]
    [7] Prajapati, H. B., Shah, J. P., & Dabhi, V. K. (2017). Detection and classification of rice plant diseases. Intelligent Decision Technologies, 11(3), 357-373.[Cross Ref]
    [8] Farber, C., Mahnke, M., Sanchez, L., & Kurouski, D. (2019). Advanced spectroscopic techniques for plant disease diagnostics. A review. TrAC Trends in Analytical Chemistry, 118, 43-49.[Cross Ref]
    [9] Khaled, A. Y., Abd Aziz, S., Bejo, S. K., Nawi, N. M., Seman, I. A., & Onwude, D. I. (2018). Early detection of diseases in plant tissue using spectroscopy–applications and limitations. Applied Spectroscopy Reviews, 137 53(1), 36-64.[Cross Ref]
    [10] Tulshan, A.S. and Raul, N., 2019, July. Plant Leaf Disease Detection using Machine Learning. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.[Cross Ref]
    [11] Chopda, J., Raveshiya, H., Nakum, S. and Nakrani, V., 2018, January. Cotton Crop Disease Detection using Decision Tree Classifier. In 2018 International Conference on Smart City and Emerging Technology (ICSCET) (pp. 1-5). IEEE.[Cross Ref]
    [12] Doh, B., Zhang, D., Shen, Y., Hussain, F., Doh, R.F. and Ayepah, K., 2019, September. Automatic Citrus Fruit Disease Detection by Phenotyping Using Machine Learning. In 2019 25th International Conference on Automation and Computing (ICAC) (pp. 1-5). IEEE.[Cross Ref]
    [13] Hossain, E., Hossain, M.F. and Rahaman, M.A., 2019, February. A Color and Texture Based Approach for the Detection and Classification of Plant Leaf Disease Using KNN Classifier. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.[Cross Ref]
    [14] Jaisakthi, S.M., Mirunalini, P. and Thenmozhi, D., 2019, February. Grape Leaf Disease Identification using Machine Learning Techniques. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-6). IEEE.[Cross Ref]
    [15] Sehgal, A. and Mathur, S., 2019, June. Plant Disease Classification Using SOFT COMPUTING Supervised Machine Learning. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 75-80). IEEE.[Cross Ref]
    [16] Singh, D., Jain, N., Jain, P., Kayal, P., Kumawat, S. and Batra, N., 2020. Plant Doc: a dataset for visual plant disease detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (pp. 249-253).[Cross Ref]
    [17] Arsenovic, M., Karanovic, M., Sladojevic, S., Anderla, A. and Stefanovic, D., 2019. Solving current limitations of deep learning based approaches for plant disease detection. Symmetry, 11(7), p.939.[Cross Ref]
    [18] Geetharamani, G. and Pandian, A., 2019. Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers & Electrical Engineering, 76, pp.323-338.[Cross Ref]
    [19] Costa, J., Silva, C. and Ribeiro, B., 2019, July. Hierarchical Deep Learning Approach for Plant Disease Detection. In Iberian Conference on Pattern Recognition and Image Analysis (pp. 383-393). Springer, Cham.[Cross Ref]
    [20] de Luna, R.G., Dadios, E.P. and Bandala, A.A., 2018, October. Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition. In TENCON 2018-2018 IEEE Region 10 Conference (pp. 1414-1419). IEEE.[Cross Ref]
    [21] Kuska, M. T., Behmann, J., Namini, M., Oerke, E. C., Steiner, U., & Mahlein, A. K. (2019). Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew. PloS one, 14(3), e0213291.[Cross Ref]
    [22] Teena, M., et al. "Potential of machine vision techniques for detecting fecal and microbial contamination of food products: a review." Food and Bioprocess Technology 6.7 (2013): 1621-1634.[Cross Ref]
    [23] Everard, C. D., Kim, M. S., & Lee, H. (2014). A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves. Journal of Food Engineering, 143, 139- 174 145.[Cross Ref]
    [24] Li, X. L., Ma, Z. H., Zhao, L. L., Li, J. H., & Wang, H. G. (2014). Application of near infrared spectroscopy to qualitative identification and quantitative determination of Puccinia strii formis f. sp. tritici and P. recondita f. sp. tritici. Guang pu xue yu guang pu fen xi= Guang pu, 34(3), 643-647.[Cross Ref]
    [25] Jayswal, H. S., & Chaudhari, J. P. Plant Leaf Disease Detection and Classification using Conventional Machine Learning and Deep Learning.[Cross Ref]
    [26] Gavhale, K.R., Gawande, U. and Hajari, K.O., 2014, April. Unhealthy region of citrus leaf detection using image processing techniques. In International Conference for Convergence for Technology-2014 (pp. 1-6). IEEE[Cross Ref]
    [27] Thangadurai, K. and Padmavathi, K., 2014, February. Computer visionimage enhancement for plant leaves disease detection. In 2014 World Congress on Computing and Communication Technologies (pp. 173-175). IEEE.[Cross Ref]
    [28] Jhuria, M., Kumar, A. and Borse, R., 2013, December. Image processing for smart farming: Detection of disease and fruit grading. In 2013 IEEE Second International Conference on Image Information Processing 88 (ICIIP-2013) (pp. 521-526). IEEE.[Cross Ref]
    [29] Kumari, C.U., Prasad, S.J. and Mounika, G., 2019, March. Leaf Disease Detection: Feature Extraction with K-means clustering and Classification with ANN. In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1095-1098). IEEE.[Cross Ref]
    [30] Ratnasari, E.K., Mentari, M., Dewi, R.K. and Ginardi, R.H., 2014, September. Sugarcane leaf disease detection and severity estimation based on segmented spots image. In Proceedings of International Conference on Information, Communication Technology and System (ICTS) 2014 (pp. 93-98). IEEE[Cross Ref]
    [31] Schikora, M., Schikora, A., Kogel, K.H., Koch, W. and Cremers, D., 2010. Probabilistic classification of disease symptoms caused by Salmonella on Arabidopsis plants. INFORMATIK 2010. Service Science–Neue Perspektiven für die Informatik. Band 2.[Cross Ref]
    [32] Ramesh, S., 2018, September. Rice Blast Disease Detection and Classification Using Machine Learning Algorithm. In 2018 2nd International Conference on Micro-Electronics and Telecommunication Engineering (ICMETE) (pp. 255-259). IEEE.[Cross Ref]
    [33] Albayati, J.S.H. and Üstündağ, B.B., 2020. Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks for Apple Leaf. International Journal of Computational Intelligence Systems.[Cross Ref]
    [34] Doh, B., Zhang, D., Shen, Y., Hussain, F., Doh, R.F. and Ayepah, K., 2019, September. Automatic Citrus Fruit Disease Detection by Phenotyping Using Machine Learning. In 2019 25th International Conference on Automation and Computing (ICAC) (pp. 1-5). IEEE.[Cross Ref]
    [35] Sehgal, A. and Mathur, S., 2019, June. Plant Disease Classification Using SOFT COMPUTING Supervised Machine Learning. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 75-80). IEEE.[Cross Ref]
    [36] Shah, N. and Jain, S., 2019, February. Detection of Disease in Cotton Leaf using Artificial Neural Network. In 2019 Amity International Conference on Artificial Intelligence (AICAI) (pp. 473-476). IEEE.[Cross Ref]
    [37] Sun, G., Jia, X. and Geng, T., 2018. Plant diseases recognition based on image processing technology. Journal of Electrical and Computer Engineering, 2018.[Cross Ref]
    [38] Alias, N., Nashat, S., Zakaria, L., Najimudin, N. and Abdullah, M.Z., 2011, November. Classification gel electrophoretic image of DNA Fusarium Graminearum featuring support vector machine. In 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 109-114). IEEE.[Cross Ref]
    [39] https://www.idil-fibres-optiques.com/product/multispectral-sensor-pixelsensor/[Cross Ref]
    [40] https://www.sony.co.in/electronics/interchangeable-lens-cameras/ilce-6400/specifications[Cross Ref]
    [41] R Gomathi, S Selvankumaran (2022), A Novel Medical Image Segmentation Model with Domain Generalization Approach. IJEER 10(2), 312-319. DOI: 10.37391/IJEER.100242.[Cross Ref]

Hardikkumar S. Jayswal and Dr. Jitendra P. Chaudhari (2022), Lime Diseases Detection and Classification Using Spectroscopy and Computer Vision. IJEER 10(3), 677-683. DOI: 10.37391/IJEER.100343.