Abstract
In India, Tomato is broadly yielded crop. There are many diseases which affects the production of Tomato crop. We need better and perfect guidance for the correct prediction of leaf diseases and to differentiate between the similar diseases in visuals. Using deep learning, we can decrease the tediousness in the disease detection. This paper analyze and compare the convolution neural network models like VGG architecture (built from scratch), VGG architecture (using pre-trained weights), GoogLeNet, AlexNet to detect the of tomato plant diseases using leaves images. Resizing and conversion of input dataset is done in image pre-processing. The open access dataset of images containing 6 different disease classes and a healthy class is used for the model training.
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References
BASF: https://www.basf.com/in/en.html. Last accessed 14 May 2021
Paperspace: https://blog.paperspace.com/popular-deep-learning-architectures-alexnet-vgg-googlenet/. Last accessed 14 May 2021
Indumathi, R., Saagari, N., Thejuswini, V., Swarnareka, R.: Leaf disease detection and fertilizer suggestion. In: 2019 IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–7. IEEE, Pondicherry, India (2019)
Ferentinos, K.P.: Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145, 311–318 (2018)
Ramesh, S., Hebbar, R., Vinod, P., Niveditha, M., Pooja, R., Prasad Bhat, N., Shashank, N.: Plant disease detection using machine learning. In: International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), pp. 41–45. IEEE, Banglore, India (2018)
Sharma, P., Hans, P., Gupta, S.C.: Classification of plant leaf diseases using machine learning and image preprocessing techniques. In: 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 480–484. IEEE, Noida, India (2020)
Sardogan, M., Tuncer, A., Ozen, Y.: Plant leaf disease detection and classification based on CNN with LVQ algorithm. In: 3rd International Conference on Computer Science and Engineering (UBMK), pp. 382–385. IEEE, Sarajevo, Bosnia and Herzegovina (2018)
Jiang, P., Chen, Y., Liu, B., He, D., Liang, C.: Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. In: IEEE Access, vol. 7, pp. 59069–59080. IEEE (2019)
Rangarajan, A., Purushothaman, R., Ramesh, A.: Tomato crop disease classification using pre-trained deep learning algorithm. Proc. Comput. Sci. 133, 1040–1047 (2018)
Kaggle: https://www.kaggle.com/emmarex/plantdisease. Last accessed 14 May 2021
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. ar**v:1409.1556 (2014)
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1–9. IEEE, Boston, MA, USA (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural network. Adv. Neural Inf. Process. Syst. 1097–1105 (2012)
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Kathole, V., Munot, M. (2022). Deep Learning Models for Tomato Plant Disease Detection. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_52
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DOI: https://doi.org/10.1007/978-981-19-0840-8_52
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