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Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion

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Abstract

Accurate and fast tomato plant disease identification is significant to enhance its sustainable agricultural productivity. In the conventional technique, human experts in the field of agriculture have been accommodated to find out the anomalies in tomato plants caused by pests, diseases, climatic conditions, and nutritional deficiencies. Automatic tomato leaf disease identification is initially solved through conventional image processing and machine learning approaches which result in less accuracy. In order to produce greater prediction accuracy, deep learning-based classification is introduced. This paper provides an overall review of recent work performed in the field of tomato leaf disease identification using image processing, machine learning, and deep learning approaches. And also discuss both public and private datasets available to detect tomato leaf disease, methods employed, and adopted deep learning frameworks. Consequently, suggestions are provided to figure out the appropriate techniques in order to obtain the better prediction accuracy. Finally, the challenges encountered in implementing the machine learning and deep learning models are discussed.

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References

  • Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Zheng X (2016) Tensorflow: A system for large-scale machine learning. In 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16) (pp. 265–283).

  • Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S (2020) ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science 167:293–301

    Article  Google Scholar 

  • Annabel LSP, Muthulakshmi V (2019) AI-Powered Image-Based Tomato Leaf Disease Detection. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 506–511). IEEE.

  • Arsenovic M, Karanovic M, Sladojevic S, Anderla A, Stefanovic D (2019) Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11(7):939

    Article  Google Scholar 

  • Barbedo JGA (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. Springerplus 2(1):660

    Article  Google Scholar 

  • Barbedo JGA (2017) A review of the main challenges in automatic plant disease identification based on visible range images. Biosyst Eng 144:52–60

    Article  Google Scholar 

  • Basavaiah J, Anthony AA (2020) Tomato leaf disease classification using multiple feature extraction techniques. Wireless Pers Commun 115(1):633–651

    Article  Google Scholar 

  • Bay H, Tuytelaars T, Van Gool L (2006) SURF: speeded up robust features. Springer, Berlin, Heidelberg, pp 404–417

    Google Scholar 

  • Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):299–315

    Article  Google Scholar 

  • Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Zhang Z (2015) Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274.

  • Chen X, Zhou G, Chen A, Yi J, Zhang W, Hu Y (2020) Identification of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet. Comput Electron Agric 178:105730

    Article  Google Scholar 

  • Chollet F (2015) keras.

  • 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) (pp. 1036–1040). IEEE.

  • Durmuş H, Güneş EO, Kırcı M (2017) Disease detection on the leaves of the tomato plants by using deep learning. In 2017 6th International Conference on Agro-Geoinformatics (pp. 1–5). IEEE.

  • Elhassouny A, Smarandache F (2019) Smart mobile application to recognize tomato leaf diseases using Convolutional Neural Networks. In 2019 International Conference of Computer Science and Renewable Energies (ICCSRE) (pp. 1–4). IEEE.

  • Foysal MFA, Islam MS, Abujar S, Hossain SA (2020a) A novel approach for tomato diseases classification based. In proceedings of international joint conference on deep convolutional neural networks computational intelligence. Springer, Singapore

    Google Scholar 

  • Foysal MFA, Islam MS, Abujar S, Hossain SA (2020) A novel approach for tomato diseases classification based on deep convolutional neural networks. In Proceedings of International Joint Conference on Computational Intelligence (pp. 583–591). Springer, Singapore.

  • Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9):2022

    Article  PubMed Central  Google Scholar 

  • Fuentes AF, Yoon S, Lee J, Park DS (2018) High-performance deep neural network-based tomato plant diseases and pests diagnosis system with refinement filter bank. Front Plant Sci 9:1162

    Article  PubMed  PubMed Central  Google Scholar 

  • Fuentes A, Yoon S, Park DS (2019) Deep learning-based phenotyping system with glocal description of plant anomalies and symptoms. Front Plant Sci 10:1321

    Article  PubMed  PubMed Central  Google Scholar 

  • Gadekallu TR, Rajput DS, Reddy MPK, Lakshmanna K, Bhattacharya S, Singh S, Alazab M (2020) A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J Real-Time Image Process. 1–14.

  • Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327(5967):828–831 (PMID:20150492)

    Article  CAS  PubMed  Google Scholar 

  • Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48

    Article  Google Scholar 

  • Hassanien AE, Gaber T, Mokhtar U, Hefny H (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86–96

    Article  Google Scholar 

  • Hlaing CS, Zaw SMM (2017) Model-based statistical features for mobile phone image of tomato plant disease classification. In 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) (pp. 223–229). IEEE.

  • Hlaing CS, Zaw SMM (2018) Tomato plant diseases classification using statistical texture feature and color feature. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) (pp. 439–444). IEEE.

  • Hughes D, Salathé M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060.

  • Jiang D, Li F, Yang Y, Yu S (2020) A tomato leaf diseases classification method based on deep learning. In 2020 chinese control and decision conference (CCDC) (pp. 1446–1450). IEEE.

  • Jinzhu LU, Di CUI, Jiang H (2013) Discrimination of tomato yellow leaf curl disease using hyperspectral imaging. In 2013 Kansas City, Missouri, July 21-July 24, 2013. American Society of Agricultural and Biological Engineers, United States

    Google Scholar 

  • Karthik R, Hariharan M, Anand S, Mathikshara P, Johnson A, Menaka R (2020) Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput 86:105933

    Article  Google Scholar 

  • Kaur M, Bhatia R (2019) Development Of An Improved Tomato Leaf Disease Detection And Classification Method. In 2019 IEEE Conference on Information and Communication Technology (pp. 1–5). IEEE.

  • Kaushik M, Prakash P, Ajay R, Veni S (2020) Tomato leaf disease detection using convolutional neural network with data augmentation. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 1125–1132). IEEE.

  • Krizhevsky A, Sutskever I, Geoffrey E. Hinton (2012) Imagenet classification with deep convolutional neural networks.In Proceedings of the 25th International Conference on Neural Information Processing Systems NIPS’12. 1: 1097–1105, USA. Curran Associates Inc.

  • Kumar A, Vani M (2019) Image Based Tomato Leaf Disease Detection. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (pp. 1–6). IEEE.

  • LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  • Liu J, Wang X (2020a) Tomato diseases and pests detection based on improved Yolo V3 convolutional neural network. Front Plant Sci 11:898

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu J, Wang X (2020b) Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model. Plant Methods 16:1–16

    Article  CAS  Google Scholar 

  • Lu J, Cui D, Jiang H (2013) Discrimination of tomato yellow leaf curl disease using hyperspectral imaging. American Society of Agricultural and Biological Engineers, Kansas City, Missouri

    Google Scholar 

  • Lu J, Zhou M, Gao Y, Jiang H (2018) Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves. Precision Agric 19(3):379–394

    Article  Google Scholar 

  • Ma J, Li X, Wen H (2015a) A keyframe extraction method for processing greenhouse vegetables production monitoring video. Comput Electron Agric 111:92–102

    Article  Google Scholar 

  • Ma J, Li X, Zhang L (2015b) Monitoring video capture system for identification of greenhouse vegetable diseases. Trans Chin Soc Agric Mach 46(3):282–287

    CAS  Google Scholar 

  • Ma J, Li X, Wen H, Chen YY, Fu ZT, Zhang L (2015c) Monitoring video capture system for identification of greenhouse vegetable diseases. Trans Chin Soc Agric Mach 46(3):282–287

    CAS  Google Scholar 

  • Ma J, Du K, Zhang L, Zheng F, Chu J, Sun Z (2017) A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Comput Electron Agric 142:110–117

    Article  Google Scholar 

  • Mishra RK, Jaiswal RK, Kumar D, Saabale PR, Singh A (2014) Management of major diseases and insect pests of onion and garlic: A comprehensive review. J Plant Breed Crop Sci 6(11):160–170

    Article  Google Scholar 

  • Mokhtar U, Ali MA, Hassenian AE, Hefny H (2015) Tomato leaves diseases detection approach based on support vector machines. In 2015 11th International Computer Engineering Conference (ICENCO) (pp. 246–250). IEEE.

  • Mokhtar U, Ali MA, Hassanien AE, Hefny H (2015b) Identifying two of tomatoes leaf viruses using support vector machine. In information systems design and intelligent applications. Springer, New Delhi

    Google Scholar 

  • Mokhtar U, El Bendary N, Hassenian AE, Emary E, Mahmoud MA, Hefny H, Tolba MF (2015c) SVM-based detection of tomato leaves diseases. intelligent systems. Springer International Publishing, Berlin, pp 641–652

    Google Scholar 

  • Muthukannan K, Latha P (2015) Fuzzy inference system based unhealthy region classification in plant leaf image. Int J Comput Info Eng 8(11):2103–2107

    Google Scholar 

  • Nandhini S, Ashokkumar K (2021) Improved crossover based monarch butterfly optimization for tomato leaf disease classification using convolutional neural network. Multimedia Tools and Applications. 1–28.

  • Oerke EC (2006) Crop losses to pests. J Agric Sci 144:31

    Article  Google Scholar 

  • Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lerer A (2017) Automatic differentiation in pytorch.

  • Prabhakar M, Purushothaman R, Awasthi DP (2020) Deep learning based assessment of disease severity for early blight in tomato crop. Multimed Tools Appl 79(39):28773–28784

    Article  Google Scholar 

  • Rajasekaran T, Anandamurugan S (2019) Challenges and applications of wireless sensor networks in smart farming—a survey In Advances in big data and cloud computing. Springer, Singapore, pp 353–361

    Google Scholar 

  • Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Computer Science 133:1040–1047

    Article  Google Scholar 

  • Ren S, He K, Girshick R, Sun J (2017) Faster R-CNN: towards realtime object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137–1149

    Article  PubMed  Google Scholar 

  • Ribeiro E, Uhl A, Wimmer G, Ha¨fner M (2016) Exploring deep learning and transfer learning for colonic polyp classification. Comput Math Methods Med. 1–16

  • Riley MB, Williamson MR, Maloy O (2002) Plant disease diagnosis. Plant Health Instr

  • Rubanga DP, Loyani LK, Richard M, Shimada S (2020) A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants. arXiv preprint arXiv:2004.04023.

  • Sabrol H, Kumar S (2016a) Fuzzy and neural network based tomato plant disease classification using natural outdoor images. Indian J Sci Technol 9(44):1–8

    Article  Google Scholar 

  • Sabrol H, Kumar S (2016b) Intensity based feature extraction for tomato plant disease recognition by classification using decision tree. Int J Comput Sci Inf Secur 14(9):622–626

    Google Scholar 

  • Sabrol H, Satish K (2016) Tomato plant disease classification in digital images using classification tree. In 2016 International Conference on Communication and Signal Processing (ICCSP) (pp. 1242–1246). IEEE.

  • Sainath TN, Kingsbury B, Saon G et al (2015) Deep convolutional neural networks for large-scale speech tasks. Neural Netw 64:39–48

    Article  PubMed  Google Scholar 

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

  • Shalaby MY, Al-Zahrani KH, Baig MB, Straquadine GS, Aldosari F (2011) Threats and challenges to sustainable agriculture and rural development in Egypt: implications for agricultural extension. J Anim Plant Sci 21(3):581–588

    Google Scholar 

  • Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

    Article  PubMed  Google Scholar 

  • Shijie J, Peiyi J, Siping H (2017) Automatic detection of tomato diseases and pests based on leaf images. In 2017 Chinese Automation Congress (CAC) (pp. 2537–2510). IEEE.

  • Singh D, Jain N, Jain P, Kayal P, Kumawat S, Batra, N (2020) PlantDoc: a dataset for visual plant disease detection. In Proceedings of the 7th ACM IKDD CoDS and 25th COMAD (pp. 249–253).

  • Slavin P (2016) Climate and famines: a historical reassessment. Wiley Interdiscip Rev Clim Change 7(3):433–447. https://doi.org/10.1002/wcc.395

    Article  Google Scholar 

  • Sonnet, https://sonnet.dev/. Accessed Sept. 11, 2019.

  • Strange RN, Scott PR (2005) Plant disease: a threat to global food security. Annu Rev Phytopathol 43(1):83–116

    Article  CAS  PubMed  Google Scholar 

  • Suryawati E, Sustika R, Yuwana RS, Subekti A, Pardede HF (2018) Deep structured convolutional neural network for tomato diseases detection. In 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 385–390). IEEE.

  • Thangaraj R, Anandamurugan S, Kaliappan VK (2020) Automated tomato leaf disease classification using transfer learning-based deep convolution neural network. J Plant Dis Prot. 1–14.

  • Tian K, Li J, Zeng J, Evans A, Zhang L (2019) Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Comput Electron Agric 165:104962

    Article  Google Scholar 

  • Tm P, Pranathi A, SaiAshritha K, Chittaragi NB, Koolagudi SG (2018) Tomato leaf disease detection using convolutional neural networks. In 2018 Eleventh International Conference on Contemporary Computing (IC3) (pp. 1–5). IEEE.

  • Vedaldi A, Jia Y, Shelhamer E, Donahue J, Karayev S, Long J, Darrell T (2014) Convolutional architecture for fast feature embedding. Cornell University, arXiv: 1408.5093 v12014.

  • Wang X, Liu J (2021) Tomato anomalies detection in greenhouse scenarios based on YOLO-Dense. Front Plant Sci 12:533

    Google Scholar 

  • Wang YY, Li ZM, Wang L, Wang M (2013) A scale invariant feature transform based method. J Inf Hiding Multimed Signal Process 4(2):73–89

    Google Scholar 

  • Xie C, Shao Y, Li X, He Y (2015) Detection of early blight and late blight diseases on tomato leaves using hyperspectral imaging. Sci Rep 5:16564

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xu Y, Yu G, Wang Y, Wu X, Ma Y (2017) Car detection from low altitude UAV imagery with the faster R-CNN. J Adv Transport 11:1–11

    CAS  Google Scholar 

  • Zhang K, Wu Q, Liu A, Meng X (2018) Can deep learning identify tomato leaf disease?. Advances in Multimedia.

  • Zhao L, Jia K (2016) Multiscale CNN’s for brain tumor segmentation and diagnosis. Comput Math Methods Med 2016:1–8

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

    Rajasekaran Thangaraj

  2. Department of Information Technology, Kongu Engineering College, Perundurai, Tamilnadu, India

    S. Anandamurugan

  3. Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India

    P Pandiyan

  4. Konkuk Aerospace Design Airworthiness Institute, Konkuk University, Seoul, Republic of Korea

    Vishnu Kumar Kaliappan

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  1. Rajasekaran Thangaraj
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  2. S. Anandamurugan
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  3. P Pandiyan
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  4. Vishnu Kumar Kaliappan
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Correspondence to S. Anandamurugan.

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Thangaraj, R., Anandamurugan, S., Pandiyan, P. et al. Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion. J Plant Dis Prot 129, 469–488 (2022). https://doi.org/10.1007/s41348-021-00500-8

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