Abstract
Malaysia's rice production, consumption and area harvested are relatively small compared to its neighboring countries. Farmers lose an estimated average of 37% of their rice crop to pests and diseases every year due to poor pests and disease monitoring for the paddy rice. The research aims to find the optimal range of hyperparameters to assess their performance. The application of deep learning in detection of diseases and pests in rice plants can help farmers in applying timely treatment on the plants. Therefore, we can reduce the economic losses substantially. Most research conducted previously required large number datasets to get the best accuracy. In addition to that, most deep learning approaches have outperformed the other machine learning approaches in performing the classification tasks. However, deep learning approaches have many hyperparameters that require optimization. Thus, in this research, we will determine the optimal value of all hyperparameters used in the classification tasks. In this work, we also investigating the effects of varying the values of all the hyperparameters used in the CNN architecture by comparing the accuracy performance.
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References
DOA (Department of Agriculture Sabah) (2017) Accessible online at www.doa.sabah
FAO (Food and Agriculture Organization of the United Nations) (2014) FAO statistical yearbook 2014: Asia and the Pacific. Food and Agriculture; FAO Regional Office for Asia and the Pacific, Bangkok, Thailand
OECD-FAO Agricultural Outlook, Accessed on 31 January 2020, http://www.agri-outlook.org/data/
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Rahman CR, Arko PS, Ali ME, Iqbal Khan MA, Apon SH, Nowrin F, Wasif A (2020) Identification and recognition of rice diseases and pests using convolutional neural networks. Biosyst Eng 194:112–120. https://doi.org/10.1016/j.biosystemseng.2020.03.020. ISSN 1537-5110
Anami BS, Malvade NN, Palaiah S (2020) Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images. Artif Intell Agric 4:12–20. https://doi.org/10.1016/j.aiia.2020.03.001. ISSN 2589-7217
Anami BS, Malvade NN, Palaiah S (2020) Classification of yield affecting biotic and abiotic paddy crop stresses using field images. Inf Process Agric 7(2):272–285. https://doi.org/10.1016/j.inpa.2019.08.005. ISSN 2214-3173
Wang Y, Wang H, Peng Z (2021) Rice diseases detection and classification using attention based neural network and bayesian optimization. Expert Syst Appl 178:114770. https://doi.org/10.1016/j.eswa.2021.114770. ISSN 0957-4174
Mohanty SP, Hughes DP, Salathé M (2016) Using deep learning for image-based plant disease detection. Frontiers Plant Sci 7:1–10
Nettleton DF, Katsantonis D, Kalaitzidis A et al (2019) Predicting rice blast disease: machine learning versus process-based models. BMC Bioinf 20:514. https://doi.org/10.1186/s12859-019-3065-1
Katsantonis D, Kadoglidou K, Dramalis C, Puigdollers P (2017) Rice blast forecasting models and their practical value: a review. Phytopathol Mediterr 56(2):187–216. https://doi.org/10.14601/Phytopathol_Mediterr-18706
Temniranrat P, Kiratiratanapruk K, Kitvimonrat A, Sinthupinyo W, Patarapuwadol S (2021) A system for automatic rice disease detection from rice paddy images serviced via a Chatbot. Comput Electron Agric 185:106156. https://doi.org/10.1016/j.compag.2021.106156. ISSN 0168-1699
Sethy PK, Barpanda NK, Rath AK, Behera SK (2020) Deep feature based rice leaf disease identification using support vector machine. Comput Electron Agric 175:105527. https://doi.org/10.1016/j.compag.2020.105527. ISSN 0168-1699
Nigam A, Tiwari AK, Pandey A (2020) Paddy leaf diseases recognition and classification using PCA and BFO-DNN algorithm by image processing. Mater Today Proc 33(Part 7):4856–4862. https://doi.org/10.1016/j.matpr.2020.08.397. ISSN 2214-7853
Tian L, Xue B, Wang Z, Li D, Yao X, Cao Q, Zhu Y, Cao W, Cheng T (2021) Spectroscopic detection of rice leaf blast infection from asymptomatic to mild stages with integrated machine learning and feature selection. Remote Sens Environ 257:112350. https://doi.org/10.1016/j.rse.2021.112350. ISSN 0034-4257
Chen J, Zhang D, Nanehkaran YA (2020) Identifying plant diseases using deep transfer learning and enhanced lightweight network. Multimed Tools Appl 79:31497–31515. https://doi.org/10.1007/s11042-020-09669-w
Desai SV, Balasubramanian VN, Fukatsu T et al (2019) Automatic estimation of heading date of paddy rice using deep learning. Plant Methods 15:76. https://doi.org/10.1186/s13007-019-0457-1
Kamrul MH, Paul P, Rahman M, Machine vision based rice disease recognition by deep learning. In: Proceedings 22nd international conference on computer and information technology (ICCIT), Dhaka, Bangladesh
Rawat A, Kumar A, Upadhyay P, Kumar S (2021) Deep learning-based models for temporal satellite data processing: classification of paddy transplanted fields. Ecol Inf 61:101214. https://doi.org/10.1016/j.ecoinf.2021.101214. ISSN 1574-9541
Joshi D, Butola A, Kanade SR, Prasad DK, Mithra SA, Singh NK, Bisht DS, Mehta DS (2021) Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network. Opt Laser Technol 137:106861. https://doi.org/10.1016/j.optlastec.2020.106861. ISSN 0030-3992
Deng R, Jiang Y, Tao M, Huang X, Bangura K, Liu C, Lin J, Qi L (2020) Deep learning-based automatic detection of productive tillers in rice. Comput Electron Agric 177:105703. https://doi.org/10.1016/j.compag.2020.105703. ISSN 0168-1699
Nguyen TT, Hoang TD, Pham MT, Vu TT, Nguyen TH, Huynh QT, Jo J (2020) Monitoring agriculture areas with satellite images and deep learning. Appl Soft Comput 95:106565. https://doi.org/10.1016/j.asoc.2020.106565. ISSN 1568-4946
Yang M-D, Boubin J-G, Tsai H-P, Tseng H-H, Hsu Y-C, Stewart C-C (2020) Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet. Comput Electron Agric 179:105817. https://doi.org/10.1016/j.compag.2020.105817. ISSN 0168-1699
Yan Y, Ryu Y (2021) Exploring google street view with deep learning for crop type mapping. ISPRS J Photogram Remote Sens 171:278–296. https://doi.org/10.1016/j.isprsjprs.2020.11.022. ISSN 0924-2716
Yang Q, Shi L, Han J, Yu J, Huang K (2020) A near real-time deep learning approach for detecting rice phenology based on UAV images. Agric For Meteorol 287:107938. https://doi.org/10.1016/j.agrformet.2020.107938. ISSN 0168-1923
Xiong X, Duan L, Liu L et al (2017) Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization. Plant Methods 13:104. https://doi.org/10.1186/s13007-017-0254-7
Sainin MS, Alfred R (2011) A genetic based wrapper feature selection approach using nearest neighbour distance matrix. In: Conference on data mining and optimization, art. no. 5976534, pp 237–242
Alfred R (2008) DARA: Data summarisation with feature construction. In: Proceedings—2nd Asia international conference on modelling and simulation, AMS 2008, art. no. 4530583, pp 830–835
Alfred R (2010) Feature transformation: a genetic-based feature construction method for data summarization. Comput Intell 26(3), 337–357
Anami BS, Malvade NN, Palaiah S (2019) Automated recognition and classification of adulteration levels from bulk paddy grain sample. Inf Process Agricult 6(1):47–60. https://doi.org/10.1016/j.inpa.2018.09.001
Bai X, Cao Z, Zhao L, Zhang J, Lv C, Li C, Xie J (2018) Rice heading stage automatic observation by multi-classifier cascade based rice spike detection method. Agric For Meteorol 259:260–270. https://doi.org/10.1016/j.agrformet.2018.05.001. ISSN 0168-1923
Chu Z, Yu J (2020) An end-to-end model for rice yield prediction using deep learning fusion. Comput Electron Agricult 174(Art. no. 105471). https://doi.org/10.1016/j.compag.2020.105471
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Jomin, L., Alfred, R. (2022). A Review on the Hyperparameters Used in Machine Learning Approaches for Classifying Paddy Rice Field. In: Alfred, R., Lim, Y. (eds) Proceedings of the 8th International Conference on Computational Science and Technology. Lecture Notes in Electrical Engineering, vol 835. Springer, Singapore. https://doi.org/10.1007/978-981-16-8515-6_4
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