Detection and prediction of rice plant diseases using convolutional neural network (CNN) method
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Abstract
Rice is a basic staple food in many Asian countries and is generally irreplaceable. Rice accounts for almost half of Asia food expenditure. Rice is too a crop that is prone to plant disease. It can appear and cause a decline in the quality of rice. However, constant monitoring of the rice fields can prevent the infection of the disease. Therefore, detection and prediction of rice plant diseases is one of the topics that will be discussed in this research. The purpose of this research is to help farmers to quickly pinpoint the disease of rice plants and take care of it properly. The methods used in this paper is researching and redesigning the previous attempt to hopefully make it better and more accurate. We will be using Convolutional Neural Network (CNN) models VGG16 as our algorithm. The results are that our proposed method has more accuracy than previous research using a similar dataset. The novelty of this paper is the increased accuracy of rice plant disease detection.
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References
Z. Deng, H. Hung, M. T. Carson, A. A. Oktaviana, B. Hakim, and T. Simanjuntak, “Validating earliest rice farming in the Indonesian Archipelago,” Sci. Rep., vol. 10, no. 1, p. 10984, Jul. 2020, doi: 10.1038/s41598-020-67747-3.
E. Acar, O. F. Ertugrul, E. Aldemir, and A. Oztekin, “Automatic identification of cassava leaf diseases utilizing morphological hidden patterns and multi-feature textures with a distributed structure-based classification approach,” J. Plant Dis. Prot., vol. 129, no. 3, pp. 605–621, Jun. 2022, doi: 10.1007/s41348-022-00583-x.
G. S. Laha et al., “Importance and Management of Rice Diseases: A Global Perspective,” in Rice Production Worldwide, Cham: Springer International Publishing, 2017, pp. 303–360. doi: 10.1007/978-3-319-47516-5_13.
H. B. Prajapati, J. P. Shah, and V. K. Dabhi, “Detection and classification of rice plant diseases,” Intell. Decis. Technol., vol. 11, no. 3, pp. 357–373, Aug. 2017, doi: 10.3233/IDT-170301.
R. Rismiyati and A. Luthfiarta, “VGG16 Transfer Learning Architecture for Salak Fruit Quality Classification,” Telematika, vol. 18, no. 1, p. 37, Mar. 2021, doi: 10.31315/telematika.v18i1.4025.
H. Al-Hiary, B. S. Ahmad, M. Reyalat, M. Braik, and Z. ALRahamneh, “Fast and Accurate Detection and Classification of Plant Diseases,” Int. J. Comput. Appl., vol. 17, no. 1, pp. 31–38, 2011, doi: 10.5120/2183-2754.
S. Phadikar, J. Sil, and A. K. Das, “Rice diseases classification using feature selection and rule generation techniques,” Comput. Electron. Agric., vol. 90, pp. 76–85, Jan. 2013, doi: 10.1016/j.compag.2012.11.001.
M. M. H. Matin, A. Khatun, M. G. Moazzam, and M. S. Uddin, “An Efficient Disease Detection Technique of Rice Leaf Using AlexNet,” J. Comput. Commun., vol. 08, no. 12, pp. 49–57, 2020, doi: 10.4236/jcc.2020.812005.
H. Durmus, E. O. Gunes, and M. Kirci, “Disease detection on the leaves of the tomato plants by using deep learning,” in 2017 6th International Conference on Agro-Geoinformatics, Aug. 2017, pp. 1–5. doi: 10.1109/Agro-Geoinformatics.2017.8047016.
Z. Fan, J. Lu, M. Gong, H. Xie, and E. D. Goodman, “Automatic Tobacco Plant Detection in UAV Images via Deep Neural Networks,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 11, no. 3, pp. 876–887, Mar. 2018, doi: 10.1109/JSTARS.2018.2793849.
Z. Song, L. Fu, J. Wu, Z. Liu, R. Li, and Y. Cui, “Kiwifruit detection in field images using Faster R-CNN with VGG16,” IFAC-PapersOnLine, vol. 52, no. 30, pp. 76–81, 2019, doi: 10.1016/j.ifacol.2019.12.500.
D. M. S. Arsa and A. A. N. H. Susila, “VGG16 in Batik Classification based on Random Forest,” in 2019 International Conference on Information Management and Technology (ICIMTech), Aug. 2019, pp. 295–299. doi: 10.1109/ICIMTech.2019.8843844.
Z. Qu, J. Mei, L. Liu, and D.-Y. Zhou, “Crack Detection of Concrete Pavement With Cross-Entropy Loss Function and Improved VGG16 Network Model,” IEEE Access, vol. 8, pp. 54564–54573, 2020, doi: 10.1109/ACCESS.2020.2981561.
S. Mishra, R. Sachan, and D. Rajpal, “Deep Convolutional Neural Network based Detection System for Real-time Corn Plant Disease Recognition,” Procedia Comput. Sci., vol. 167, pp. 2003–2010, 2020, doi: 10.1016/j.procs.2020.03.236.
T. Islam, M. Sah, S. Baral, and R. Roy Choudhury, “A Faster Technique on Rice Disease Detectionusing Image Processing of Affected Area in Agro-Field,” in 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Apr. 2018, pp. 62–66. doi: 10.1109/ICICCT.2018.8473322.
A. Bashir and S. Yuliana, “Identifying factors influencing rice production and consumption in Indonesia,” J. Ekon. Pembang. Kaji. Masal. Ekon. dan Pembang., vol. 19, no. 2, Jan. 2019, doi: 10.23917/jep.v19i2.5939.
J. Pardede, B. Sitohang, S. Akbar, and M. L. Khodra, “Implementation of Transfer Learning Using VGG16 on Fruit Ripeness Detection,” Int. J. Intell. Syst. Appl., vol. 13, no. 2, pp. 52–61, Apr. 2021, doi: 10.5815/ijisa.2021.02.04.
T. Daniya and S. Vigneshwari, “A review on machine learning techniques for rice plant disease detection in agricultural research,” Int. J. Adv. Sci. Technol., vol. 28, no. 13, pp. 49–62, 2019.
VBOOKSHELF, “Rice Leaf Diseases Dataset,” Kaggle, 2019. https://www.kaggle.com/datasets/vbookshelf/rice-leaf-diseases
S. A. Sanchez, H. J. Romero, and A. D. Morales, “A review: Comparison of performance metrics of pretrained models for object detection using the TensorFlow framework,” IOP Conf. Ser. Mater. Sci. Eng., vol. 844, p. 012024, Jun. 2020, doi: 10.1088/1757-899X/844/1/012024.
N. K. Manaswi, “Understanding and Working with Keras,” in Deep Learning with Applications Using Python, Berkeley, CA: Apress, 2018, pp. 31–43. doi: 10.1007/978-1-4842-3516-4_2.
J. Lu, L. Tan, and H. Jiang, “Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification,” Agriculture, vol. 11, no. 8, p. 707, Jul. 2021, doi: 10.3390/agriculture11080707.
H.-C. Shin et al., “Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning,” IEEE Trans. Med. Imaging, vol. 35, no. 5, pp. 1285–1298, May 2016, doi: 10.1109/TMI.2016.2528162.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015, doi: https://doi.org/10.48550/arXiv.1409.1556.
S. Tammina, “Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” Int. J. Sci. Res. Publ., vol. 9, no. 10, p. p9420, Oct. 2019, doi: 10.29322/IJSRP.9.10.2019.p9420.