Abstract
Plant diseases and pernicious insects are a huge danger to food security and agriculture sector. It is crucial to identify and recognize the type of plant disease to help the farmer. This information can help to make appropriate decision about an increase in the crop productivity. The detection process marks the beginning of a series of activities to fight the diseases and reduce their spread. The technology used in medical procedures has not been adequate to detect all diseases on time, and that is why some diseases turn out to become pandemics because they are hard to detect on time. To address these issues, new technologies that use image processing, computer vision and deep learning approaches to identify various illnesses in plants are being developed. The results of these methods have shown that they can produce fast, accurate disease detection with a good economic impact. In the present work, a comparison of the performances of various CNN architectures—AlexNet, LeNet, VGG 16 and a novel proposed architecture based on evaluation metrics over two datasets is made out. Analyzing the performance of various models, it is concluded that the VGG 16 is the best architecture with an accuracy of approximately 96%. AlexNet architecture resulted an accuracy of 95%, but the training time was comparatively large. The proposed architecture is a modified LeNet model which has a comparatively less accuracy than VGG 16 and AlexNet but the training time is very less and is reduced by 90% compared to AlexNet. The present work also included creating a user interface to upload the diseased leaf image and get the required diagnosis report including the disease name, its causes, symptoms and treatment suggestions.
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References
D. Srinivasa Rao, R. Babu Ch, V. Sravan Kiran et al., Plant disease classification using deep bilinear CNN. Intell. Autom. Soft Comput. 31(1), 161–176 (2022)
S. Nandhini, K. Ashokkumar, An automatic plant leaf disease identification using DenseNet-121 architecture with a mutation-based henry gas solubility optimization algorithm. Neural Comput. Appl. 34(7), 5513–5534 (2022)
M.H. Saleem, S. Khanchi, J. Potgieter, K.M. Arif, Image-based plant disease identification by deep learning meta-architectures. Plants 9(11), 1451 (2020)
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (2020)
S. Kumar, K. Prasad, A. Srilekha, T. Suman, B.P. Rao, J.N. Vamshi Krishna, Leaf disease detection and classification based on machine learning, in IEEE International Conference on Smart Technologies in Computing, Electrical and Electronics (2020), pp. 361–365
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. 25, (2019)
R. Kaundal, A.S. Kapoor, G.P.S. Raghava, Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinf. 7, 485 (2019)
A. Fuentes, S. Yoon, S.C. Kim, D.S. Park, A robust deeplearning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17(9), 2022 (2018)
A. Kamilaris, F.X. Prenafeta-Boldú, Deep learning in agriculture: a survey. Comput. Electron. Agric. 147(July 2017), 70–90 (2018)
K. Yu, L. Lin, M. Alazab, L. Tan, B. Gu, Deep learning-based traffic safety solution for a mixture of autonomous and manual vehicles in a 5G-enabled intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 22(7), 4337–4347 (2020)
J.G.A. Barbedo, Digital image processing techniques for detecting quantifying and classifying plant diseases. Springer Plus 2(660), 1–12 (2015)
S. Yun, W. Xianfeng, Z. Shanwen, Z. Chuanlei, PNN based crop disease recognition with leaf image features and meteorological data. Int. J. Agric. Biol. Eng. 8(4), 60 (2015)
S.P. Mohanty, D.P. Hughes, M. Salathé, Using deep learning for image-based plant disease detection. Front. Plant Sci. 7(September), 1–10 (2016)
S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, D. Stefanovic, Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 4, 613–623 (2016)
K.P. Mokthar, Deep learning models for plant disease detection and diagnosis. Comput. Electron. Agric. 145(September 2015), 311–318 (2015)
M. Brahimi, K. Boukhalfa, A. Moussaoui, Deep learning for tomato diseases: classification and symptoms visualization. Appl. Artif. Intell. 31(4), 299–315 (2017)
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, C. Hill, A. Arbor, “Going Deeper with Convolutions,” pp. 1–9, 2014
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Deepti, K. Comparative Analysis of Machine Learning Techniques for Plant Disease Detection-Data Deployment. J. Inst. Eng. India Ser. B 104, 837–849 (2023). https://doi.org/10.1007/s40031-023-00897-w
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DOI: https://doi.org/10.1007/s40031-023-00897-w