Development of a Mobile Application for Plant Disease Detection using Parameter Optimization Method in Convolutional Neural Networks Algorithm
Abstract
Plant diseases are a serious problem in agriculture that affects both the quantity and quality of the harvest. To address this issue, authors developed a mobile software capable of detecting diseases in plants by analyzing their leaves using a smartphone camera. This research used the Convolutional Neural Networks (CNN) method for this purpose. In the initial experiments, authors compared the performance of four deep learning architectures: VGG-19, Xception, ResNet-50, and InceptionV3. Based on the results of the experiments, authors decided to use the CNN Xception as it yielded good performance. However, the CNN algorithm does not attain its maximum potential when using default parameters. Hence, authors goal is to enhance its performance by implementing parameter optimization using the grid search algorithm to determine the optimal combination of learning rate and epoch values. The experimental results demonstrated that the implementation of parameter optimization in CNN significantly improved accuracy in potato plants from 96.3% to 97.9% and in maize plants from 87.6% to 93.4%.
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References
M. Arsenovic, M. Karanovic, S. Sladojevic, A. Anderla, and D. Stefanovic. Solving current limitations of deep learning-based approaches for plant disease detection. Symmetry. Vol. 11, No. 7, pp. 939, 2019. DOI: https://doi.org/10.3390/sym11070939
A. Elhassouny, and F. Smarandache, Smart Mobile Application to Recognize Tomato Leaf Diseases using Convolutional Neural Networks, 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), pp. 1-4, 2019. DOI: https://doi.org/10.1109/ICCSRE.2019.8807737
V.R. Meeradevi, M.R. Mundada, S.P. Sawkar, R.S. Bellad, and P.S. Keerthi, Design and Development of Efficient Techniques for Leaf Disease Detection using Deep Convolutional Neural Networks, 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), pp. 153-158, 2020. DOI: https://doi.org/10.1109/DISCOVER50404.2020.9278067
M. Gehlot, and M.L. Saini, Analysis of Different CNN Architectures for Tomato Leaf Disease Classification, 2020 5th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-6, 2020. DOI: https://doi.org/10.1109/ICRAIE51050.2020.9358279
U. Mokhtar, M.A.S. Ali, A.E. Hassenian, and H. Hefny, Tomato leaves diseases detection approach based on Support Vector Machines. 2015 11th International Computer Engineering Conference (ICENCO), pp 246-250, 2015. DOI: https://doi.org/10.1109/ICENCO.2015.7416356
J.M Al-Tuwaijari, M.A. Jasim, M.A.B. Raheem, Deep Learning Techniques Toward Advancement of Plant Leaf Diseases Detection, 2020 2nd Al-Noor International Conference for Science and Technology (NICST), pp. 7-12, 2020. DOI: https://doi.org/10.1109/NICST50904.2020.9280320
H. Al Farizi, Deteksi penyakit pada daun tembakau menggunakan metode Gray Level co-occurrence matrix, Final Project of Electronic Engineering Polytechnic Institute of Surabaya, 2020.
G. Irmak, A. Saygili, Tomato Leaf Disease Detection and Classification using Convolutional Neural Networks, 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), pp 1-5, 2020. DOI: https://doi.org/10.1109/ASYU50717.2020.9259832
E.L. Rocha, L.F.R. Moreira, and J.F. Mari, Maize Leaf Disease Classification Using Convolutional Neural Networks and Hyperparameter Optimization, XVI Workshop de Visão Computacional (WVC 2020), pp 104-110, 2020. DOI: https://doi.org/10.5753/wvc.2020.13489
J. Shah, H.B. Prajapati, and V. Dabhi, A Survey on Detection and Classification of Rice Plant Diseases, 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), pp 1-8, 2016. DOI: https://doi.org/10.1109/ICCTAC.2016.7567333
M. Al-Amin, T.A. Bushra, and M. Nazmul Hoq, Prediction of Potato Disease from Leaves using Deep Convolution Neural Network towards a Digital Agricultural System, 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp 1-5, 2019. DOI: https://doi.org/10.1109/ICASERT.2019.8934933
D. Tiwari D, M. Ashish M, N. Gangwar, A. Sharma, S. Patel, and S. Bhardwaj, Potato Leaf Diseases Detection Using Deep Learning, 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp 461-466, 2020. DOI: https://doi.org/10.1109/ICICCS48265.2020.9121067
A. Abbas, S. Jain, M. Gour, and S. Vankudothu, Tomato Plant Disease Detection Using Transfer Learning With C-GAN Synthetic Images, Computers and Electronics in Agriculture, vol 187, 2021. DOI: https://doi.org/10.1016/j.compag.2021.106279
M. Agarwal, A. Singh, S. Arjaria, A. Sinha, and S. Gupta, ToLeD: Tomato Leaf Disease Detection Using Convolutional Neural Network, Procedia Computer Science, vol 167, pp 293-301, 2020. DOI: https://doi.org/10.1016/j.procs.2020.03.225
T. Anandhakrishnan, and S.M. Jaisakthi, Deep Convolutional Neural Networks for Image Based Tomato Leaf Disease Detection, Sustainable Chemistry and Pharmacy, vol 30, 2022. DOI: https://doi.org/10.1016/j.scp.2022.100793
V. Gonzalez-Huitron, J.A. León-Borges, A.E. Rodriguez-Mata, L.E. Amabilis-Sosa, B. Ramírez-Pereda, and H. Rodriguez, Disease Detection in Tomato Leaves via CNN with Lightweight Architectures Implemented in Raspberry Pi 4, Computers and Electronics in Agriculture, vol 181, 2021. DOI: https://doi.org/10.1016/j.compag.2020.105951
P. Kaur, S. Harnal, V. Gautam, M.P. Singh, and S.P. Singh, An Approach for Characterization of Infected Area in Tomato Leaf Disease Based on Deep Learning and Object Detection Technique, Engineering Applications of Artificial Intelligence, vol 115, 2022. DOI: https://doi.org/10.1016/j.engappai.2022.105210
R. Karthik, M. Hariharan, S. Anand, P. Mathikshara, A. Johnson, and R. Menaka, Attention embedded residual CNN for disease detection in tomato leaves, Applied Soft Computing, vol 86, 2020. DOI: https://doi.org/10.1016/j.asoc.2019.105933
A.K. Rangarajan, R. Purushothaman, and A. Ramesh, Tomato Crop Disease Classification Using Pre-trained Deep Learning Algorithm, Procedia Computer Science, vol 133, pp 1040-1047, 2018. DOI: https://doi.org/10.1016/j.procs.2018.07.070
S. Mishra, R. Sachan, and D. Rajpal, Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition, Procedia Computer Science, vol 167, 2020. DOI: https://doi.org/10.1016/j.procs.2020.03.236
D. J. M. Bonifacio, A. M. I. E. Pascual, M. V. C. Caya and J. C. Fausto, Determination of Common Maize (Zea mays) Disease Detection using Gray-Level Segmentation and Edge-Detection Technique, 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), pp. 1-6, 2020. DOI: https://doi.org/10.1109/HNICEM51456.2020.9399998
A. Yadav and M. K. Dutta, An Automated Image Processing Method for Segmentation and Quantification of Rust Disease in Maize Leaves, 2018 4th International Conference on Computational Intelligence & Communication Technology (CICT), pp. 1-5, 2018. DOI: https://doi.org/10.1109/CIACT.2018.8480122
M. V. Overbeek, Y. R. Kaesmetan, and F. A. T. Tobing, "Identification of Maize Leaf Diseases Cause by Fungus with Digital Image Processing (Case Study: Bismarak Village Kupang District - East Nusa Tenggara)" 2019 5th International Conference on New Media Studies (CONMEDIA), pp. 125-128, 2019. DOI: https://doi.org/10.1109/CONMEDIA46929.2019.8981843
M. J. Hasan, M. S. Alom, U. F. Dina and M. H. Moon, Maize Diseases Image Identification and Classification by Combining CNN with Bi-Directional Long Short-Term Memory Model, 2020 IEEE Region 10 Symposium (TENSYMP), pp. 1804-1807, 2020. DOI: https://doi.org/10.1109/TENSYMP50017.2020.9230796
F. Chollet, Xception: Deep Learning with Depthwise Separable Convolutions. CoRR, 2016. DOI: https://doi.org/10.1109/CVPR.2017.195
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