Login Register Cart 0
Generic placeholder image

Current Chinese Computer Science

Editor-in-Chief

ISSN (Print): 2665-9972
ISSN (Online): 2665-9964

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Systematic Review Article

Towards Highly Intelligent Image Processing Techniques for Rice Diseases Identification: A Review

Author(s): R. Manavalan*

Volume 2, Issue 1, 2022

Published on: 09 September, 2022

Article ID: e080622205724 Pages: 26

DOI: 10.2174/2665997202666220608125036

Price: $65

Abstract

Background: Rice is cultivated worldwide as one of the primary food crops. The responsible factors that rigorously affect rice crops' production are pests and various rice plant diseases, leading to considerable reduction in the agrarian and global economy. More sustainable farming methods for determining disease levels and the quality of paddy plants will be essential in the future.

Objective: The disease discovery in rice crops by naked eyes may result in erroneous pesticide measurements. Therefore, early diagnosis of rice diseases can expedite disease control by properly selecting pest management methods to maximize the rice yield to cope with the demand of the world's growing population. A literature search is conducted and identifies 68 peer-reviewed research studies published in the period between 2007 and 2021, focusing on early disease detection of rice crops to maximize productivity.

Conclusion: This study has identified several key issues that must be resolved at each step of the computer-assisted diagnostic system to recognize diseases in paddy crops. Study results show that automated disease diagnosing techniques are still immature for rice plants. Hence, the ingenious design and evolution of a novel fully-automated farming system are widely essential as innovative methods for addressing and resolving diseases in the paddy crop to offer sustainability and productivity benefits to the agrarian sector.

Keywords: Rice crop, paddy, plant disease, computational approaches, image processing, plant pathology, classification.

Graphical Abstract

[1]
N. Kurniawati, S. Abdullah, S. Abdullah, and S. Abdullah, "Investigation on Image processing techniques for diagnosing paddy diseases", International Conference of Soft Computing and Pattern Recognition, 4-7 Dec. 2009, Malacca, Malaysia, pp. 272-277, 2009.
[http://dx.doi.org/10.1109/SoCPaR.2009.62]
[2]
"Ricepedia, The Global Staple - Ricepedia", Available from: ricepedia.org/rice-as-food/the-global-staple-rice-consumers
[3]
"How to Start Rice Farming Business (how is rice grown)", Available from: www.roysfarm.com/rice-farming/
[4]
"Statista, “Rice Consumption by Country 2019.”", Available from: www.statista.com/statistics/255971/top-countries-based-on-rice-consumption-2012-2013/
[5]
"Statista, “Top Paddy Rice Producers Worldwide,”", Available from: www.statista.com/statistics/255937/leading-rice-producers-worldwide/
[6]
J.W. Orillo, I. Valenzuela, and J. Cruz, "Identification of diseases in rice plant (oryza sativa) using back propagation artificial neural network", International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 12-16 Nov. , 2014.
[7]
"International Rice Research, “Rice in the Philippines,” IRRI", Available from: www.irri.org/ourwork/locations/philippines
[8]
Z. Iqbal, M.A. Khan, M. Sharif, and J.H. Shah, "M. H. ur Rehman, and K. Javed, “An automated detection and classification of citrus plant diseases using image processing techniques: A review,”", Comput. Electron. Agric., vol. 153, pp. 12-32, 2018.
[http://dx.doi.org/10.1016/j.compag.2018.07.032]
[9]
P. Mishra, "MSM Asaari mohd, A. Herrero-Langreo, S. Lohumi, B. Diezma, and P. Scheunders, “Close range hyperspectral imaging of plants: A review”", Biosyst. Eng., vol. 164, pp. 49-67, 2017.
[http://dx.doi.org/10.1016/j.biosystemseng.2017.09.009]
[10]
V. Gutte, and M. Gitte, "Survey on recognition of plant disease with help of algorithm", Int. J. Eng. Sci. Comput., vol. 6, no. 6, pp. 7100-7102, 2018.
[http://dx.doi.org/10.13140/RG.2.2.13919.36004]
[11]
N. Pelka, O. Musshoff, and R. Weber, "Does weather matter? How rainfall affects credit risk in agricultural microfinance", Agr. Financ. Rev., vol. 75, no. 2, pp. 194-212, 2015.
[http://dx.doi.org/10.1108/AFR-10-2014-0030]
[12]
F. Van Winsen, Y. De Mey, L. Lauwers, S. Passel, M. Vancauteren, and E. Wauters, "Cognitive mapping: A method to elucidate and present farmers’ risk perception", Agric. Syst., vol. 122, pp. 42-52, 2013.
[http://dx.doi.org/10.1016/j.agsy.2013.08.003]
[13]
R.B.M. Huirne, "Strategy and risk in farming", NJAS Wagening. J. Life Sci., vol. 50, no. 2, pp. 249-259, 2003.
[http://dx.doi.org/10.1016/S1573-5214(03)80010-6]
[14]
A. Komarek, A. De Pinto, and V. Smith, "A review of types of risks in agriculture: What we know and what we need to know", Agric. Syst., vol. 178, p. 102738, 2019.
[http://dx.doi.org/10.1016/j.agsy.2019.102738]
[15]
B.S. Khatkar, N. Chaudhary, and P. Malik, "Production and consumption of grains: India", Encycl. Food Grains, no. December, pp. 367-373, 2016.
[http://dx.doi.org/10.1016/B978-0-12-394437-5.00044-9]
[16]
V. Prabha, and J.A. Moses, "Machine vision system for food grain quality evaluation: A review", Trends Food Sci. Technol., vol. 56, pp. 13-20, 2016.
[http://dx.doi.org/10.1016/j.tifs.2016.07.011]
[17]
A. Alves, W. Souza, and D. Borges, "Cotton pests classification in field-based images using deep residual networks", Comput. Electron. Agric., vol. 174, p. 105488, 2020.
[http://dx.doi.org/10.1016/j.compag.2020.105488]
[18]
W. Abdulkhair, and M. Alghuthaymi, "Plant Pathogens", In: Intechopen, 2016.
[http://dx.doi.org/10.5772/65325]
[19]
N.T. Keen, "A century of plant pathology: A retrospective view on understanding host-parasite interactions", Annu. Rev. Phytopathol., vol. 38, no. 1, pp. 31-48, 2000.
[http://dx.doi.org/10.1146/annurev.phyto.38.1.31] [PMID: 11701835]
[20]
J.F.I. Nturambirwe, and U. Opara, "Machine learning applications to non-destructive defect detection in horticultural products", Biosyst. Eng., vol. 189, pp. 60-83, 2019.
[http://dx.doi.org/10.1016/j.biosystemseng.2019.11.011]
[21]
E. Britannica, Plant Disease - Symptoms and Signs, Available from: www.britannica.com/science/plant-disease/Symptoms-and-signs
[22]
"Krishisewa.com, 5 major diseases of rice", Available from: www.krishisewa.com/articles/disease-management/444-diseases-rice.html
[23]
"B. IRRI Rice Knowledge, Bacterial Blight - IRRI Rice Knowledge Bank", Available from: www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/bacterial-blight?category_id=326
[24]
"B. IRRI Rice Knowledge, Sheath Blight", Available from: www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/sheath-blight
[25]
"Ito and Kuribayashi, Brown Spot of Rice. ITO", Available from: https://www. lsuagcenter. com/~/media/system/4/6/2/f/462fd54617eaff51c0fdeb50d5feccdd/pub3108brownspotlowres.pdf
[26]
"Www.Agritech.Tnau.Ac.In, Crop Protection", Available from: www.agritech.tnau.ac.in/expert_system/paddy/cpdissrsb.html
[27]
"B. IRRI Rice Knowledge, False Smut", Available from: www.knowledgebank.irri.org/training/fact-sheets/pest-management/diseases/item/false-smut
[28]
"Eagri.org, “RICE :: FUNGAL DISEASES :: FOOT ROT OR BAKANAE DISEASE,” Eagri", Available from: eagri.org/eagri50/PATH272/lecture01/0010.html
[29]
"Agritech, Bakanae Disease or Foot Rot", Available from: http://www.agritech.tnau.ac.in/expert_system/paddy/cpdisbakanae.html
[30]
J.G.A. Barbedo, "A review on the main challenges in automatic plant disease identification based on visible range images", Biosyst. Eng., vol. 144, pp. 52-60, 2016.
[http://dx.doi.org/10.1016/j.biosystemseng.2016.01.017]
[31]
R. Manavalan, "Automatic identification of diseases in grains crops through computational approaches: A review", Comput. Electron. Agric., vol. 178, no. September, p. 105802, 2020.
[http://dx.doi.org/10.1016/j.compag.2020.105802]
[32]
N.N. Kurniawati, S.N.H.S. Abdullah, S. Abdullah, and S. Abdullah, "Texture analysis for diagnosing paddy disease", Proc. 2009 Int. Conf. Electr. Eng. Informatics, ICEEI, vol. 1, pp. 23-27, 2009.
[http://dx.doi.org/10.1109/ICEEI.2009.5254824]
[33]
P. Sethy, S. Dash, N. Barpanda, and A. Rath, "A novel approach for quantification of population density of Rice Brown Plant Hopper (RBPH) using on-field images based on image processing", J. Emerg. Technol. Innov. Res., vol. 6, no. 5, pp. 252-256, 2019.
[34]
U. Prabu, "Smart paddy crop disease identification and management using deep convolution neural network and SVM classifier", Int. J. Pure Appl. Math., vol. 118, no. 15, pp. 255-264, 2018.
[35]
M. Mukherjee, T. Pal, and D. Samanta, "Damaged paddy leaf detection using image processing", J. Glob. Res. Comput. Sci., vol. 3, no. 10, pp. 2010-2013, 2012.
[http://dx.doi.org/10.1016/j.proeng.2012.06.377]
[36]
A. Singh, and M. Singh, Automated color prediction of paddy crop leaf using image processing., IEEE Technological Innovation in ICT for Agriculture and Rural Development. TIAR, 2015, pp. 24-32.
[http://dx.doi.org/10.1109/TIAR.2015.7358526]
[37]
Devi and Neelamangalam, "Paddy leaf Disease detection Using Svm With RBFN classifier", Int. J. Pure Appl. Math., vol. 117, p. 699-15-710, 2017.
[38]
S. Das, D. Roy, and P. Das, "Disease feature extraction and disease detection from paddy crops using image processing and deep learning technique", In: Computational Intelligence in Pattern Recognition, 2020, pp. 443-449.
[http://dx.doi.org/10.1007/978-981-15-2449-3_38]
[39]
B. Achour, M. Belkadi, I. Filali, M. Laghrouche, and M. Lahdir, "Image analysis for individual identification and feeding behaviour monitoring of dairy cows based on Convolutional Neural Networks (CNN)", Biosyst. Eng., vol. 198, pp. 31-49, 2020.
[http://dx.doi.org/10.1016/j.biosystemseng.2020.07.019]
[40]
G. Anthonys, and N. Wickramarachchi, "An image recognition system for crop disease identification of paddy fields in Sri lanka", In: 2009 International Conference on Industrial and Information Systems (ICIIS), 28-31 Dec. 2009, Peradeniya, Sri Lanka, IEEE, 2009..
[http://dx.doi.org/10.1109/ICIINFS.2009.5429828]
[41]
Z. Zhou, Y. Zang, Y. Li, Y. Zhang, P. Wang, and X. Luo, "Rice plant-hopper infestation detection and classification algorithms based on fractal dimension values and fuzzy C-means", Math. Comput. Model., vol. 58, no. 3-4, pp. 701-709, 2013.
[http://dx.doi.org/10.1016/j.mcm.2011.10.028]
[42]
R. A. D., "Pugoy and V. Y. Mariano, “Automated rice leaf disease detection using color image analysis", Third International Conference on Digital Image Processing (ICDIP 2011), vol. 8009, p. 80090F, 2011.
[http://dx.doi.org/10.1117/12.896494]
[43]
T. Verma, S.K. Satpathy, and L. Sharma, "A step towards precision farming of rice crop by estimating loss caused by leaf blast disease using digital image processing and fuzzy clustering", Int. J. Comput. Trends Tech., vol. 1, no. 1, pp. 152-157, 2011.
[44]
S. Phadikar, J. Sil, and A. Das, "Rice diseases classification using feature selection and rule generation techniques", Comput. Electron. Agric., vol. 90, pp. 76-85, 2013.
[http://dx.doi.org/10.1016/j.compag.2012.11.001]
[45]
D.A. Devi, and K. Muthukannan, "Analysis of segmentation scheme for diseased rice leaves", IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1374-1378, 2014.
[http://dx.doi.org/10.1109/ICACCCT.2014.7019325]
[46]
R. Singh, "Amit Kumar and B. S. Raja, “Classification of rice disease using digital image processing and SVM classifier”", Int. J. Electr. Electron. Eng., vol. 7, no. 1, pp. 294-299, 2015.
[47]
R. Islam, and M. Rafiqul, "An image processing technique to calculate percentage of disease affected pixels of paddy leaf", Int. J. Comput. Appl., vol. 123, no. 12, pp. 28-34, 2015.
[http://dx.doi.org/10.5120/ijca2015905495]
[48]
R. Sarkar, and A. Pramanik, "Segmentation of plant disease spots using automatic SRG algorithm: a look up table approach", In: International Conference on Advances in Computer Engineering and Applications (ICACEA), 19-20 March 2015, Ghaziabad, India, IEEE, 2015, pp. 1-5.
[http://dx.doi.org/10.1109/ICACEA.2015.7194375]
[49]
P. Xu, G. Wu, Y. Guo, X. Chen, H. Yang, and R. Zhang, "Automatic wheat leaf rust detection and grading diagnosis via embedded image processing system", Procedia Comput. Sci., vol. 107, pp. 836-841, 2017.
[http://dx.doi.org/10.1016/j.procs.2017.03.177]
[50]
P. Sanyal, U. Bhattacharya, S. Parui, S. Bandyopadhyay, and S. Patel, "Color texture analysis of rice leaves diagnosing deficiency in the balance of mineral levels towards improvement of crop productivity", In 10th International Conference on Information Technology (ICIT 2007), 17-20 Dec. 2007, Rourkela, India, IEEE, 2007.
[http://dx.doi.org/10.1109/ICIT.2007.40]
[51]
Q. Yao, Z. Guan, Y. Zhou, J. Tang, Y. Hu, and B. Yang, "Application of support vector machine for detecting rice diseases using shape and color texture features", Eng. Comput. Int. Conf., pp. 79-83, 2009.
[http://dx.doi.org/10.1109/ICEC.2009.73]
[52]
B.S. Anami, J. Pujari, and R. Yakkundimath, "Identification and classification of normal and affected agriculture/horticulture produce based on combined color and texture feature extraction", Int. J. Comput. Appl. Eng. Sci., vol. 1, no. 3, pp. 356-360, 2011.
[53]
K. Majid, Y. Herdiyeni, and A. Rauf, "I-PEDIA: Mobile application for paddy disease identification using fuzzy entropy and probabilistic neural network", 2013 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2013, pp. 403-406, 2013.
[http://dx.doi.org/10.1109/ICACSIS.2013.6761609]
[54]
A. Asfarian, Y. Herdiyeni, A. Rauf, and K. Mutaqin, "Paddy Diseases Identification with Texture Analysis using Fractal Descriptors Based on Fourier Spectrum", In: International Conference on Computer, Control, Informatics and Its Applications (IC3INA), 19-21 Nov. 2013, Jakarta, Indonesia, IEEE, 2013.
[http://dx.doi.org/10.1109/IC3INA.2013.6819152]
[55]
K. Jagan, M. Balasubramanian, and S. Palanivel, "B. M., and S. Palanivel, “Detection and recognition of diseases from paddy plant leaf images”", Int. J. Comput. Appl., vol. 144, no. 12, pp. 34-41, 2016.
[http://dx.doi.org/10.5120/ijca2016910505]
[56]
K.J. Mohan, and M. Balasubramanian, "Recognition of Paddy Plant Diseases Based on Histogram Oriented Gradient Features", Int. J. Adv. Res. Comput. Commun. Eng., vol. 5, no. 3, pp. 1071-1074, 2016.
[57]
H. Prajapati, J. Shah, and V. Dabhi, "Detection and classification of rice plant diseases", Intell. Decision Technol., vol. 11, no. 3, pp. 357-373, 2017.
[http://dx.doi.org/10.3233/IDT-170301]
[58]
S. Das, and S. Sengupta, "Feature extraction and disease prediction from paddy crops using data mining techniques", Comput. Intell. Pattern Recogn, pp. 155-163, 2020.
[http://dx.doi.org/10.1007/978-981-15-2449-3_13]
[59]
D. Mallick, R. Ray, and S. Dash, "Detection and classification of crop diseases from its leaves using image processing", Smart Comput. Informatics, no. Dec, 2018.
[http://dx.doi.org/10.1007/978-981-13-9282-5_20]
[60]
T. Devi, and P. Neelamegam, "Image processing based rice plant leaves diseases in Thanjavur, Tamilnadu", Cluster Comput., vol. 22, no. S6, pp. 1-14, 2019.
[http://dx.doi.org/10.1007/s10586-018-1949-x]
[61]
M. Hasan, S. Mahbub, and M. Nasim, "Rice disease identification and classification by integrating support vector machine with deep convolutional neural network", In: 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 3-5 May 2019, Dhaka, Bangladesh, 2019.
[http://dx.doi.org/10.1109/ICASERT.2019.8934568]
[62]
W.J. Liang, H. Zhang, G.F. Zhang, and H.X. Cao, "Rice blast disease recognition using a deep convolutional neural network", Sci. Rep., vol. 9, no. 1, p. 2869, 2019.
[http://dx.doi.org/10.1038/s41598-019-38966-0] [PMID: 30814523]
[63]
V.K. Shrivastava, M.K. Pradhan, S. Minz, and M.P. Thakur, "Rice plant disease classification using transfer learning of deep convolution neural network", Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., vol. 42, no. 3/W6, pp. 631-635, 2019.
[http://dx.doi.org/10.5194/isprs-archives-XLII-3-W6-631-2019]
[64]
A. Chatterjee, S. Roy, and S. Das, "feature selection using rough set theory from infected rice plant images", In: Computational Intelligence in Pattern Recognition., 2020, pp. 417-427.
[http://dx.doi.org/10.1007/978-981-15-2449-3_36]
[65]
J. Chen, D. Zhang, Y.A. Nanehkaran, and D. Li, "Detection of rice plant diseases based on deep transfer learning", J. Sci. Food Agric., vol. 100, no. 7, pp. 3246-3256, 2020.
[http://dx.doi.org/10.1002/jsfa.10365] [PMID: 32124447]
[66]
A. Das, C. Mallick, and S. Dutta, "Deep learning-based automated feature engineering for rice leaf disease prediction", Sci. Rep., vol. 9, pp. 133-141, 2020.
[http://dx.doi.org/10.1007/978-981-15-2449-3_11]
[67]
P. Sanyal, and S. Patel, "Pattern recognition method to detect two diseases in rice plants", Imaging Sci. J., vol. 56, no. 6, pp. 319-325, 2008.
[http://dx.doi.org/10.1179/174313108X319397]
[68]
S. Phadikar, and J. Sil, "Rice disease identification using pattern recognition techniques", Inter. Conf. Comput. Inform. Technol., pp. 420-423, 2009.
[http://dx.doi.org/10.1109/ICCITECHN.2008.4803079]
[69]
L. Liu, and G. Zhou, "Extraction of the rice leaf disease image based on BP", Neural Netw., 2010.
[70]
Z-Y. Liu, H-F. Wu, and J. Huang, "Application of neural networks to discriminate fungal infection levels in rice panicles using hyperspectral reflectance and principal components analysis", Comput. Electron. Agric., vol. 72, no. 2, pp. 99-106, 2010.
[http://dx.doi.org/10.1016/j.compag.2010.03.003]
[71]
A. Nithya, and V. Sundaram, "Classification rules for Indian Rice diseases", Int. J. Comput. Sci. Issues, vol. 8, no. 1, pp. 444-448, 2011.
[72]
S. Phadikar, "Classification of rice leaf diseases based on morphological changes", Int. J. Inf. Electron. Eng., vol. 2, pp. 460-463, 2012.
[http://dx.doi.org/10.7763/IJIEE.2012.V2.137]
[73]
L. Kun, and W. Zhiqiang, "Rice blast prediction based on gray ant colony and rbf neural network combination model", In 2012 Fifth International Symposium on Computational Intelligence and Design, 28-29 Oct. 2012, Hangzhou, China, IEEE, 2012.
[http://dx.doi.org/10.1109/ISCID.2012.44]
[74]
Y. Yang, "Early detection of rice blast (Pyricularia) at seedling stage in Nipponbare rice variety using near-infrared hyper-spectral image", Afr. J. Biotechnol., vol. 11, no. 26, 2012.
[http://dx.doi.org/10.5897/AJB11.3269]
[75]
V. Surendrababu, "Detection of rice leaf diseases using chaos and fractal dimension in image processing", Int. J. Comput. Sci. Eng., vol. 6, pp. 69-74, 2014.
[76]
K.R. Kumar, and S.A. Ramesh Kumar, "A novel and high speed technique for paddy crops disease prediction in wireless tele-agriculture using data mining techniques", Middle East J. Sci. Res., vol. 22, no. 9, pp. 1430-1441, 2014.
[77]
R. Deshmukh, and D. Manjusha, "Detection of paddy leaf diseases", Inter. J. Comput. Appl., pp. 8-10, 2015.
[78]
A. Joshi, and B.D. Jadhav, "Monitoring and controlling rice diseases using Image processing techniques", In International Conference on Computing, Analytics and Security Trends (CAST), 19-21 Dec. 2016, Pune, India, IEEE, 2016, pp. 471-476
[http://dx.doi.org/10.1109/CAST.2016.7915015]
[79]
S. Ramesh, and B. Rajaram, "Iot based crop disease identification system using optimization techniques", ARPN J. Eng. Appl. Sci., vol. 13, pp. 1392-1395, 2018.
[80]
R. Shunmugam, and V. Dharmar, "Recognition and classification of paddy leaf diseases using optimized deep neural network with jaya algorithm", Inf. Process. Agric., no. Sep, 2019.
[http://dx.doi.org/10.1016/j.inpa.2019.09.002]
[81]
A. Rath, and J. Meher, "Disease detection in infected plant leaf by computational method", Arch. Phytopathol. Pflanzenschutz, vol. 52, no. 19-20, pp. 1-11, 2020.
[http://dx.doi.org/10.1080/03235408.2019.1708546]
[82]
S. Bhattacharya, A. Mukherjee, and S. Phadikar, "A deep learning approach for the classification of rice leaf diseases", In: Bhattacharyya S., Mitra S., Dutta P., Eds., Intelligence Enabled Research. Advances in Intelligent Systems and Computing., vol. 1109. Springer: Singapore, 2020.
[http://dx.doi.org/10.1007/978-981-15-2021-1_8]
[83]
P. Sethy, S. Gouda, N. Barpanda, and A. Rath, "Detection of white ear-head of rice crop using image processing and machine learning techniques", In: Elçi A., Sa P., Modi C., Olague G., Sahoo M., Bakshi S., Eds., Smart Computing Paradigms: New Progresses and Challenges. Advances in Intelligent Systems and Computing., vol. 766. Springer: Singapore, 2020.
[http://dx.doi.org/10.1007/978-981-13-9683-0_10]
[84]
B.S. Bari, M.N. Islam, M. Rashid, M.J. Hasan, M.A.M. Razman, R.M. Musa, A.F. Ab Nasir, and A.P.P. Abdul Majeed, "A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework", PeerJ Comput. Sci., vol. 7, p. e432, 2021.
[http://dx.doi.org/10.7717/peerj-cs.432] [PMID: 33954231]
[85]
S. Handayani, and G.W. Nurcahyo, "Accuracy in identifying rice plant diseases using method fuzzy", Smart Comput. Inform., vol. 13, no. 1, pp. 33-41, 2021.
[86]
I.V. Arinichev, S.V. Polyanskikh, I.V. Arinicheva, and I.O. Sergeeva, "Applications of convolutional neural networks for the detection and classification of fungal rice diseases", IOP Conf. Ser. Earth Environ. Sci., vol. 699, no. 1, p. 012020, 2021.
[http://dx.doi.org/10.1088/1755-1315/699/1/012020]
[87]
P.A. Gunawan, E.N. Kencana, and K. Sari, "Classification of rice leaf diseases using artificial neural network", J. Phys. Conf. Ser., vol. 1722, no. 1, p. 012013, 2021.
[http://dx.doi.org/10.1088/1742-6596/1722/1/012013]
[88]
J. Chen, D. Zhang, A. Zeb, and Y.A. Nanehkaran, "Identification of rice plant diseases using lightweight attention networks", Expert Syst. Appl., vol. 169, no. January, p. 114514, 2021.
[http://dx.doi.org/10.1016/j.eswa.2020.114514]
[89]
B. Tian, "Rice disease image recognition method based on support vector machine", Turkish J. F. Crop., vol. 26, no. 1, pp. 88-98, 2021.
[http://dx.doi.org/10.17557/tjfc.834510]
[90]
Y. Wang, H. Wang, and Z. Peng, "Rice diseases detection and classification using attention based neural network and bayesian optimization", Expert Syst. Appl., vol. 178, no. February, p. 114770, 2021.
[http://dx.doi.org/10.1016/j.eswa.2021.114770]
[91]
N. Krishnamoorthy, and V.R. Loga Parameswari, "Rice leaf disease detection via deep neural networks with transfer learning for early identification", Turkish J. Physiother. Rehabil., vol. 32, no. 2, pp. 1087-1097, 2021.
[92]
K. N, "L.V. Narasimha Prasad, C.S. Pavan Kumar, B. Subedi, H.B. Abraha, and V. E. Sathishkumar, “Rice leaf diseases prediction using deep neural networks with transfer learning”", Environ. Res., vol. 198, no. April, p. 111275, 2021.
[http://dx.doi.org/10.1016/j.envres.2021.111275]
[93]
N.V.R.R. Goluguri, K.S. Devi, and P. Srinivasan, "Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases", Neural Comput. Appl., vol. 33, no. 11, pp. 5869-5884, 2021.
[http://dx.doi.org/10.1007/s00521-020-05364-x]
[94]
S. Abdullah, A.A. Bakar, N. Mustafa, M. Yusuf, and A.R. Hamdan, "Fuzzy knowledge modelling for image-based paddy disease diagnosis expert system", Int. J. Eng. Sci. Res. Technol., pp. 978-980, 2007.
[95]
R. Kaura, S. Dina, and P. Pannub, "Expert system to detect and diagnose the leaf diseases of cereals", Int. J. Curr. Eng. Technol., vol. 3, no. 4, pp. 1480-1483, 2013.
[96]
H. Kalita, S.K. Sarma, and R.D. Choudhury, "Expert system for diagnosis of diseases of rice plants: Prototype design and implementation", Int. Conf. Autom. Control Dyn. Optim. Tech. ICACDOT 2016, pp. 723-730, 2017.
[http://dx.doi.org/10.1109/ICACDOT.2016.7877682]
[97]
B. Ji, T. Banhazi, K. Perano, A. Ghahramani, L. Bowtell, C. Wang, and B. Li, "A review of measuring, assessing and mitigating heat stress in dairy cattle", Biosyst. Eng., vol. 199, pp. 4-26, 2020.
[http://dx.doi.org/10.1016/j.biosystemseng.2020.07.009]

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy