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Research Articles

Tomato plant leaf disease detection using generative adversarial network and deep convolutional neural network

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Pages 1-9 | Received 31 May 2022, Accepted 18 Dec 2022, Published online: 06 Jan 2023

References

  • Dhingra G, Kumar V, Joshi HD. Study of digital image processing techniques for leaf disease detection and classification. Multimed Tools Appl. 2018;77(15):19951–20000.
  • Patel A, Joshi B. A survey on the plant leaf disease detection techniques. Int J Adv Res Comput Commun Eng. 2017;6(1):229–231.
  • Kaur S, Pandey S, Goel S. Plants disease identification and classification through leaf images: a survey. Arch Comput Methods Eng. 2019;26(2):507–530.
  • Stilwell M. The global tomato online news processing in 2018. Cited 2022 October 15. https://www.tomatonews.com/
  • Schreinemachers P, Simmons EB, Wopereis MC. Tapping the economic and nutritional power of vegetables. Glob Food Sec. 2018;16:36–45.
  • Wang R, Lammers M, Tikunov Y, et al. The rin, nor and Cnr spontaneous mutations inhibit tomato fruit ripening in additive and epistatic manners. Plant Sci. 2020;294:110436.
  • Harvey CA, Rakotobe ZL, Rao NS, et al. Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. Philos Trans R Soc, B. 2014;369(1639):20130089.
  • Joshi BM, Bhavsar H. Plant leaf disease detection and control: a survey. J Inf Optim Sci. 2020;41(2):475–487.
  • Mahum R, Munir H, Mughal Z-U-N, et al. A novel framework for potato leaf disease detection using an efficient deep learning model. Human Ecol Risk Assess: An Int J. 2022:1–24.
  • Moussafir M, Chaibi H, Saadane R, et al. Design of efficient techniques for tomato leaf disease detection using genetic algorithm-based and deep neural networks. Plant Soil. 2022:475:1–16.
  • Pantazi XE, Moshou D, Tamouridou AA. Automated leaf disease detection in different crop species through image features analysis and one-class classifiers. Comput Electron Agric. 2019;156:96–104.
  • Kumar S, Sharma B, Sharma VK, et al. Plant leaf disease identification using exponential spider monkey optimization. Sustainable Comput: Inf Syst. 2018;28:1-25.
  • Kaur S, Pandey S, Goel S. Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Proc. 2018;12(6):1038–1048.
  • Ouhami M, Hafiane A, Es-Saady Y, et al. Computer vision, IoT and data fusion for crop disease detection using machine learning: a survey and ongoing research. Remote Sens. 2021;13(13):2486.
  • Ahmad A, Saraswat D, Gamal AE. A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools. Smart Agric Technol. 2022;3:100083.
  • Mohanty SP, Hughes DP, Salathé M. Using deep learning for image-based plant disease detection. Front Plant Sci. 2016;7:1419.
  • Ramcharan A, Baranowski K, McCloskey P, et al. Deep learning for image-based cassava disease detection. Front Plant Sci. 2017;8:1852.
  • Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–318.
  • Pandey A, Jain K. A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images. Ecol Inform. 2022;70:101725.
  • Abbas A, Jain S, Gour M, et al. Tomato plant disease detection using transfer learning with C-GAN synthetic images. Comput Electron Agric. 2021;187:106279. DOI:10.1016/j.compag.2021.106279
  • Bedi P, Gole P. Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artif Intell Agric. 2021;5:90–101. DOI:10.1016/j.aiia.2021.05.002
  • Ashwinkumar S, Rajagopal S, Manimaran V, et al. Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks. Mater Today Proc. 2022;51:480–487. DOI:10.1016/j.matpr.2021.05.584
  • Hernández S, López JL. Uncertainty quantification for plant disease detection using Bayesian deep learning. Appl Soft Comput. 2020;96:106597. DOI:10.1016/j.asoc.2020.106597
  • Tiwari V, Joshi RC, Dutta MK. Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecol Inform. 2021;63:101289. DOI:10.1016/j.ecoinf.2021.101289
  • Shah D, Trivedi V, Sheth V, et al. ResTS: residual deep interpretable architecture for plant disease detection. Inf Process Agric. 2022;9(2):212–223. DOI:10.1016/j.inpa.2021.06.001
  • Wang D, Wang J, Li W, et al. T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases. Comput Electron Agric. 2021;190:106468. doi:10.1016/j.compag.2021.106468.
  • Tahir MB, Khan MA, Javed K, et al. WITHDRAWN: recognition of apple leaf diseases using deep learning and variances-controlled features reduction. 2021:104027. DOI:10.1016/j.micpro.2021.104027
  • Adeel A, Khan MA, Akram T, et al. Entropy-controlled deep features selection framework for grape leaf diseases recognition. Expert Syst. 2022;39(7):e12569.
  • Agarwal M, Gupta SK, Biswas KK. Development of efficient CNN model for tomato crop disease identification. Sustainable Comput: Inf Syst. 2020;28:100407.
  • Chowdhury ME, Rahman T, Khandakar A, et al. Automatic and reliable leaf disease detection using deep learning techniques. AgriEngineering. 2021;3(2):294–312.
  • Sharma P, Berwal YPS, Ghai W. Performance analysis of deep learning CNN models for disease detection in plants using image segmentation. Inf Process Agric. 2020;7(4):566–574. doi:10.1016/j.inpa.2019.11.001.
  • Sachdeva G, Singh P, Kaur P. Plant leaf disease classification using deep convolutional neural network with Bayesian learning. Mater Today Proc. 2021;45:5584–5590. doi:10.1016/j.matpr.2021.02.312.
  • Pratap US, Chouhan SS, Jain S, et al. Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access. 2019;7:43721–43729. doi:10.1109/ACCESS.2019.2907383.
  • Sinha A, Shekhawat RS. A novel image classification technique for spot and blight diseases in plant leaves. Imaging Sci J. 2020;68(4):225–239.
  • Lu Y, Yi S, Zeng N, et al. Identification of rice diseases using deep convolutional neural networks. Neurocomputing. 2017;267:378–384.
  • Barbedo JG. Factors influencing the use of deep learning for plant disease recognition. Biosystems Eng. 2018;172:84–91.
  • Singh AK, Sreenivasu SVN, Mahalaxmi USBK, et al. Hybrid feature-based disease detection in plant leaf using convolutional neural network, Bayesian optimized SVM, and random forest classifier. J Food Qual. 2022;2022:1–16.
  • Rajathi V, Bhavani RR, Wiselin Jiji G. Varicose ulcer (C6) wound image tissue classification using multidimensional convolutional neural networks. The Imaging Sci J. 2019;67(7):374–384.
  • Phil Kim Convolutional neural network. In: MATLAB deep learning. Berkeley, CA: Apress; 2017. p. 121–147.
  • Bhangale KB, Kothandaraman M. Survey of deep learning paradigms for speech processing. Wirel Pers Commun. 2022:Volume 123,1–37.
  • Konečný J, Liu J, Richtárik P, et al. Mini-batch semi-stochastic gradient descent in the proximal setting. IEEE J Sel Top Signal Process. 2015;10(2):242–255.
  • PlantVillage. Cited 2021 July 30. https://www.kaggle.com/emmarex/plantdisease

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