Abstract
The potato is the fourth largest food crop and is grown in many places. Potato crops are mainly infected with fungi so they suffer from early blight diseases and late blight diseases. Crop diseases must be detected and recognized because plant diseases have a significant effect on production. Smart farming using machine learning allows infected crops to be identified automatically. Real-time control of disease and management increases productivity and reduces agricultural losses. This study proposes a highly efficient CNN (convolutional neural network) architecture that is suitable for potato disease detection. A database is created for the training set using image processing. Adam is used as the optimizer and cross-entropy is used for model analysis. Softmax is used as the final judgment function. The convolution layer and resources are minimized but accuracy is maintained. The experimental results show that the proposed model detects plant disease with 99.53% accuracy and reduces parameter usage by an average of 99.39%.
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References
Kaur R, Kang SS. An enhancement in classifier support vector machine to improve plant disease detection. In: Proceedings of the 3rd international conference on MOOCs, innovation and technology in education (MITE), Amritsar, 2015, pp. 135–40.
Islam M, Dinh A, Wahid K, Bhowmik P (2017) Detection of potato diseases using image segmentation and multiclass support vector machine. In: Proceedings of Canadian conference on electrical and computer engineering (CCECE), Windsor, 2017, pp. 1–4.
Hossain S, Mou RM, Hasan MM, Chakraborty S, Razzak MA. Recognition and detection of tea leaf’s diseases using support vector machine. In: Proceedings of 2018 IEEE 14th international colloquium on signal processing and its applications (CSPA), Batu Feringghi, 2018, pp. 150–54.
Prakash RM, Saraswathy GP, Ramalakshmi G, Mangaleswari KH, Kaviya T. Detection of leaf diseases and classification using digital image processing. In: Proceedings of international conference on innovations in information, embedded and communication system (CIIECS), Coimbatore, 2017, pp. 1–4.
Iqbal MdA, Talukder KH. Detection of potato disease using image segmentation and machine learning. In: Proceedings of 2020 international conference on wireless communications signal processing and networking (WiSPNET), India, 2020, pp. 43–47.
Pinki FT, Khatun N, Islam SMM. Content based paddy leaf disease recognition and remedy prediction using support vector machine. In: Proceedings of 2017 20th international conference of computer and information technology (ICCIT), Dhaka, 2017, pp. 1–5.
Pardede HF, Suryawati E, Sustika R, Zilvan V. Unsupervised convolutional autoencoder-based feature learning for automatic detection of plant diseases. In: Proceedings of 2018 international conference on computer, control, informatics and its applications (IC3INA), 2018, pp. 158–62.
Tm P, Pranathi A, SaiAshritha K, Chittaragi NB, Koolagudi SG. Tomato leaf disease detection using convolutional neural networks. In: Proceedings of 2018 Eleventh international conference on contemporary computing (IC3), Noida, 2018, pp. 1–5.
Turkoglu M, Hanbay D. Apricot disease identification based on attributes obtained from deep learning algorithms. In: Proceedings of the international conference on artificial intelligence and data processing (IDAP), Malatya, Turkey, 2018, pp. 1–4.
Suryawati E, Sustika R, Yuwana RS, Subekti A, Pardede HF. Deep structured convolutional neural network for tomato diseases detection. In: Proceedings of the international conference on advanced computer science and information systems (ICACSIS), Yogyakarta, 2018, pp. 385–90.
Lee TY, Yu JY, Chang YC, Yang JM. Health detection for potato leaf with convolutional neural network. In: Proceedings of international conference on computing, analytics and networks (Indo-Taiwan ICAN), India, 2020, pp. 1–5.
Chollet F. Building powerful image classification models using very little data. The Keras Blog, 05/06/2015 (2015). https://blog.keras.io/. Accessed 14 July 2018.
Shin DH, Park RH, Yang S, Jung JH. Block-based noise estimation using adaptive Gaussian filtering. IEEE Trans Consum Electron. 2005;51(1):218–26.
Hu R, Tian B, Yin S, Wei S. Efficient hardware architecture of softmax layer in deep neural network. In: Proceedings of international conference on digital signal processing (DSP), Shanghai, China, 2018, pp. 1–5.
Jupyter. https://jupyter.org/. Accessed 14 May 2020.
Kingma DP, Ba J. Adam: a method for stochastic optimization. In: Proceedings of international conference on learning representations (ICLR), San Diego, 2015, pp. 1–15.
Simonyan K, Zisserman A.Very deep convolutional networks for large-scale image recognition. In: Proceedings of computational and biological learning society, 2015, pp. 1–14.
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This study was funded by the Ministry of Science and Technology of the Republic of China, Taiwan, for partially supporting this research under Contract No. MOST 108-2221-E-027-091.
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This article is part of the topical collection “Applications of Cloud Computing, Data Analytics and Building Secure Networks” guest-edited by Rajnish Sharma, Pao-Ann Hsiung and Sagar Juneja.
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Lee, TY., Lin, A., Yu, JY. et al. High Efficiency Disease Detection for Potato Leaf with Convolutional Neural Network. SN COMPUT. SCI. 2, 297 (2021). https://doi.org/10.1007/s42979-021-00691-9
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DOI: https://doi.org/10.1007/s42979-021-00691-9