All Issue

2022 Vol.34, Issue 2

Review Article

31 July 2022. pp. 97-117
Abstract
References
1
Abdulridha, J., Ampatzidis, Y., Qureshi, J., Roberts, P. (2020) Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning. Remote Sens 12:2732. 10.3390/rs12172732
2
Ahila Priyadharshini, R., Arivazhagan, S., Arun, M., Mirnalini, A. (2019) Maize leaf disease classification using deep convolutional neural networks. Neural Comput Appl 31:8887-8895. 10.1007/s00521-019-04228-3
3
Ahuja, S., Payak, M. (1982) Symptoms and signs of banded leaf and sheath blight of maize. Phytoparasitica 10:41-49. 10.1007/BF02981891
4
Akanksha, E., Sharma, N., Gulati, K. (2021) OPNN: Optimized probabilistic neural network based automatic detection of maize plant disease detection. ICICT. IEEE. pp.1322-1328. 10.1109/ICICT50816.2021.9358763
5
Alisaac, E., Behmann, J., Kuska, M. T., Dehne, H.-W., Mahlein, A.-K. (2018) Hyperspectral quantification of wheat resistance to Fusarium head blight: Comparison of two Fusarium species. Eur J Plant Pathol 152:869-884. 10.1007/s10658-018-1505-9
6
Anonymous (2021) World Food and Agriculture. Statistical Yearbook.
7
Aoki, T., O'Donnell, K., Homma, Y., Lattanzi, A. R. (2003) Sudden-death syndrome of soybean is caused by two morphologically and phylogenetically distinct species within the Fusarium solani species complex-F. virguliforme in North America and F. tucumaniae in South America. Mycologia 95:660-684. 10.1080/15572536.2004.1183307021148975
8
Arinichev, I. V., Polyanskikh, S. V., Volkova, G. V., Arinicheva, I. V. (2021) Rice Fungal Diseases Recognition Using Modern Computer Vision Techniques. Int J Fuzzy Log Intel 21:1-11. 10.5391/IJFIS.2021.21.1.1
9
Arnal Barbedo, J. G. (2013) Digital image processing techniques for detecting, quantifying and classifying plant diseases. SpringerPlus 2:660. 10.1186/2193-1801-2-66024349961PMC3863396
10
Ashwini, C., Sellam, V. (2022) Corn Disease Detection based on Deep Neural Network for Substantiating the Crop Yield. Appl Math 16:423-433. 10.18576/amis/160304
11
Asibi, A. E., Chai, Q., Coulter, J. A. (2019) Rice Blast: A Disease with Implications for Global Food Security. Agronomy 9. 10.3390/agronomy9080451
12
Awad, Y. M., Abdullah, A. A., Bayoumi, T. Y., Abd-Elsalam, K., Hassanien, A. E. (2014) Early Detection of Powdery Mildew Disease in Wheat (Triticum aestivum L.) Using Thermal Imaging Technique, in: Intelligent Systems'2014, eds. D. Filev, J. Jabłkowski, J. Kacprzyk, M. Krawczak, I. Popchev, L. Rutkowski, V. Sgurev, E. Sotirova, P. Szynkarczyk, S. Zadrozny: Springer International Publishing. pp.755-765. 10.1007/978-3-319-11310-4_66
13
Awaludin, N., Abdullah, J., Salam, F., Ramachandran, K., Yusof, N. A., Wasoh, H. (2020) Fluorescence-based immunoassay for the detection of Xanthomonas oryzae pv. oryzae in rice leaf. Anal biochem 610:113876. 10.1016/j.ab.2020.11387632750357
14
Bandara, A. Y., Weerasooriya, D. K., Bradley, C. A., Allen, T. W., Esker, P. D. (2020) Dissecting the economic impact of soybean diseases in the United States over two decades. PloS ONE 15:e0231141-e0231141. 10.1371/journal.pone.023114132240251PMC7117771
15
Barbedo, J. G. A., Godoy, C. V. (2015) Automatic classification of soybean diseases based on digital images of leaf symptoms, in: Embrapa Informática Agropecuária-Artigo em anais de congresso ALICE.
16
Barnwal, M. K., Kotasthane, A., Magculia, N., Mukherjee, P. K., Savary, S., Sharma, A. K. (2013) A review on crop losses, epidemiology and disease management of rice brown spot to identify research priorities and knowledge gaps. Eur J Plant Pathol 136:443-457. 10.1007/s10658-013-0195-6
17
Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., Herppich, W. (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Compute Electron Agric 75:304-312. 10.1016/j.compag.2010.12.006
18
Bernardo, R., Bourrier, M., Olivier, J. (1992) Generation means analysis of resistance to head smut in maize. Agronomie 12:303-306. 10.1051/agro:19920403
19
Boland, G., Melzer, M., Hopkin, A., Higgins, V., Nassuth, A. (2004) Climate change and plant diseases in Ontario. Can J Plant Pathol 26:335-350. 10.1080/07060660409507151
20
Bonifacio, D. J. M., Pascual, A. M. I. E., Caya, M. V. C., Fausto, J. C. (2020) Determination of Common Maize (Zea mays) Disease Detection using Gray-Level Segmentation and Edge-Detection Technique. HNICEM. IEEE. pp.1-6. 10.1109/HNICEM51456.2020.9399998
21
Boufleur, T. R., Ciampi‐Guillardi, M., Tikami, Í., Rogério, F., Thon, M. R., Sukno, S. A. (2021) Soybean anthracnose caused by Colletotrichum species: Current status and future prospects. Mol Plant Pathol 22:393-409. 10.1111/mpp.1303633609073PMC7938629
22
Bravo, C., Moshou, D., West, J., McCartney, A., Ramon, H. (2003) Early disease detection in wheat fields using spectral reflectance. Biosyst Eng 84:137-145. 10.1016/S1537-5110(02)00269-6
23
Bregaglio, S., Willocquet, L., Kersebaum, K. C., Ferrise, R., Stella, T., Ferreira, T. B. (2021) Comparing process-based wheat growth models in their simulation of yield losses caused by plant diseases. Field Crops Res 265:108108. 10.1016/j.fcr.2021.108108
24
Bressan, W. (2003) Biological control of maize seed pathogenic fungi by use of actinomycetes. BioControl 48:233-240. 10.1023/A:1022673226324
25
Browder, L., Eversmeyer, M. (1980) Sorting of Puccinia recondita: Triticum Infection-Type Data Sets. Phytopathology 70:666-670. 10.1094/Phyto-70-666
26
Buja, I., Sabella, E., Monteduro, A. G., Chiriacò, M. S., De Bellis, L., Luvisi, A., Maruccio, G. (2021) Advances in plant disease detection and monitoring: from traditional assays to in-field diagnostics. Sensors. 21(6). 2129. doi.org/10.3390/s21062129. 10.3390/s2106212933803614PMC8003093
27
Cai, G., Schneider, R. (2008) Population structure of Cercospora kikuchii, the causal agent of Cercospora leaf blight and purple seed stain in soybean. Phytopathology 98:823-829. 10.1094/PHYTO-98-7-082318943259
28
Cao, X., Luo, Y., Zhou, Y., Fan, J., Xu, X., West, J. S., Duan, X., Cheng, D. (2015) Detection of Powdery Mildew in Two Winter Wheat Plant Densities and Prediction of Grain Yield Using Canopy Hyperspectral Reflectance. PLoS ONE 10:0121462. 10.1371/journal.pone.012146225815468PMC4376796
29
Castroagudín, V. L., Moreira, S. I., Pereira, D. A., Moreira, S. S., Brunner, P. C., Maciel, J. L. (2016) Pyricularia graminis-tritici, a new Pyricularia species causing wheat blast. Pers Mol Phylogeny Evol Fungi 37:199-216. 10.3767/003158516X69214928232765PMC5315288
30
Ceresini, P. C., Castroagudín, V. L., Rodrigues, F. Á., Rios, J. A., Eduardo Aucique-Pérez, C., Moreira, S. I. (2018) Wheat blast: past, present, and future. Annu Rev Phytopathol 56:427-456. 10.1146/annurev-phyto-080417-05003629975608
31
Chaudhary, P., Chaudhari, A. K., Cheeran, A., Godara, S. (2012) Color transform based approach for disease spot detection on plant leaf. Int J Comput Sci Telecom 3:65-70.
32
Chauhan, M. D. (2021) Detection of maize disease using random forest classification algorithm. Turk J Comput Math Educ 12:715-720.
33
Chen, F., Zhang, Y., Zhang, J., Liu, L., Wu, K. (2022) Rice False Smut Detection and Prescription Map Generation in a Complex Planting Environment, with Mixed Methods, Based on Near Earth Remote Sensing. Remote Sens 14:945. 10.3390/rs14040945
34
Chen, W.-L., Lin, Y.-B., Ng, F.-L., Liu, C.-Y., Lin, Y.-W. (2019) RiceTalk: Rice blast detection using Internet of Things and artificial intelligence technologies. IEEE Internet of Things J 7:1001-1010. 10.1109/JIOT.2019.2947624
35
Chung, C.-L., Huang, K.-J., Chen, S.-Y., Lai, M.-H., Chen, Y.-C., Kuo, Y.-F. (2016) Detecting Bakanae disease in rice seedlings by machine vision. Compute Electron Agric 121:404-411. 10.1016/j.compag.2016.01.008
36
Cottyn, B., Mew, T. (2004) Bacterial blight of rice. Encyclopedia of Plant and Crop Science. New York, Marcel Dekker. pp.79-83. 10.1081/E-EPCS-120010586
37
Cowger, C., Miranda, L., Griffey, C., Hall, M., Murphy, J., Maxwell, J. (2012) Wheat powdery mildew. Disease resistance in wheat. CABI, Oxfordshire. pp.84-119. 10.1079/9781845938185.0084
38
Cui, D., Zhang, Q., Li, M., Hartman, G. L., Zhao, Y. (2010) Image processing methods for quantitatively detecting soybean rust from multispectral images. Biosyst Eng 107:186-193. 10.1016/j.biosystemseng.2010.06.004
39
Dean, R., Van Kan, J. A. L., Pretorius, Z. A., Hammond-Kosack, K. E., Di Pietro, A., Spanu, P. D. (2012) The Top 10 fungal pathogens in molecular plant pathology. Mol Plant Patho 13:414-430. 10.1111/j.1364-3703.2011.00783.x22471698PMC6638784
40
Del Fiore, A., Reverberi, M., Ricelli, A., Pinzari, F., Serranti, S., Fabbri, A. (2010) Early detection of toxigenic fungi on maize by hyperspectral imaging analysis. Int J Food Microbiol 144:64-71. 10.1016/j.ijfoodmicro.2010.08.00120869132
41
Deng, R., Tao, M., Xing, H., Yang, X., Liu, C., Liao, K. (2021) Automatic Diagnosis of Rice Diseases Using Deep Learning. Front Plant Sci 12. 10.3389/fpls.2021.70103834490004PMC8416767
42
Derbyshire, M. C., Newman, T. E., Khentry, Y., Owolabi Taiwo, A. (2022) The evolutionary and molecular features of the broad-host-range plant pathogen Sclerotinia sclerotiorum. Mol Plant Pathol. 10.1111/mpp.1322135411696PMC9276942
43
Deshapande, A. S., Giraddi, S. G., Karibasappa, K., Desai, S. D. (2019) "Fungal disease detection in maize leaves using haar wavelet features," in Information and Communication Technology for Intelligent Systems. Springer. pp.275-286. 10.1007/978-981-13-1742-2_27
44
Dey, U., Harlapur, S., Dhutraj, D., Suryawanshi, A., Badgujar, S., Jagtap, G., Kuldhar, D. P. (2012) Spatiotemporal yield loss assessment in corn due to common rust caused by Puccinia sorghi Schw. Afr J Agric Res 7:5265-5269. 10.5897/AJAR12.1103
45
Duveiller, E. (1994) Bacterial leaf streak or black chaff of cereals. EppO Bulletin 24:135-157. 10.1111/j.1365-2338.1994.tb01057.x
46
Editors, T. (1992) Diseases of Wheat Concepts and Management Methods.
47
Ennadifi, E., Laraba, S., Vincke, D., Mercatoris, B., Gosselin, B. (2020) Wheat Diseases Classification and Localization Using Convolutional Neural Networks and GradCAM Visualization (ISCV): IEEE. pp.1-5. 10.1109/ISCV49265.2020.9204258
48
Fattah, F. (1988) Effects of inoculation methods on the incidence of ear-cockle and 'tundu'on wheat under field conditions. Plant and soil 109:195-198. 10.1007/BF02202084
49
Gao, Z., Khot, L. R., Naidu, R.A., Zhang, Q. (2020) Early detection of grapevine leafroll disease in a red-berried wine grape cultivar using hyperspectral imaging. Compute Electron Agri 179:105807. 10.1016/j.compag.2020.105807
50
Gharge, S., Singh, P. (2016) Image processing for soybean disease classification and severity estimation. Springer. pp.493-500. 10.1007/978-81-322-2553-9_44
51
Ghosal, S., Blystone, D., Singh Asheesh, K., Ganapathysubramanian, B., Singh, A., Sarkar, S. (2018) An explainable deep machine vision framework for plant stress phenotyping. Proc Natl Acad Sci 115:4613-4618. 10.1073/pnas.171699911529666265PMC5939070
52
Ghyar, B. S., Birajdar, G. K. (2017) Computer vision based approach to detect rice leaf diseases using texture and color descriptors (ICICI): IEEE. pp.1074-1078. 10.1109/ICICI.2017.8365305
53
Gitz, V., Meybeck, A., Lipper, L., Young, C., Braatz, S. (2016) Climate change and food security: Risks and responses.
54
Godoy, C. V., Seixas, C. D. S., Soares, R. M., Marcelino-Guimarães, F. C., Meyer, M. C., Costamilan, L. M. (2016) Asian soybean rust in Brazil: past, present, and future. Pesqui Agropecu Bra 51:407-421. 10.1590/S0100-204X2016000500002
55
Gorman, Z., Christensen, S. A., Yan, Y., He, Y., Borrego, E., Kolomiets, M. V. (2020) Green leaf volatiles and jasmonic acid enhance susceptibility to anthracnose diseases caused by Colletotrichum graminicola in maize. Mol Plant Pathol 21:702-715. 10.1111/mpp.1292432105380PMC7170777
56
Gu, D., Andreev, K., Dupre, M. E. (2021) Major trends in population growth around the world. China CDC Wkly. 3(28):604-613. 10.46234/ccdcw2021.160. 10.46234/ccdcw2021.16034594946PMC8393076
57
Gui, J., Hao, L., Zhang, Q., Bao, X. (2015) A new method for soybean leaf disease detection based on modified salient regions. Int J Multimedia Ubiquitous eng 10:45-52. 10.14257/ijmue.2015.10.6.06
58
Guo, X., Li, Y., Fan, J., Li, L., Huang, F., Wang, W. (2012) Progress in the study of false smut disease in rice. J Agric Sci Technol A 2(11A):1211.
59
Ham, J. H., Melanson, R. A., Rush, M. C. (2011) Burkholderia glumae: next major pathogen of rice? Mol Plant Pathol 12:329-339. 10.1111/j.1364-3703.2010.00676.x21453428PMC6640401
60
Han, L., Haleem, M. S., Taylor, M. (2015) A novel computer vision-based approach to automatic detection and severity assessment of crop diseases. (SAI): IEEE. pp.638-644. 10.1109/SAI.2015.7237209
61
Hartman, G. L., Rupe, J. C., Sikora, E. J., Domier, L. L., Davis, J. A., Steffey, K. L. (2015) Compendium of soybean diseases and pests. Am Phytopath Society. 10.1094/9780890544754
62
Hasan, M. J., Mahbub, S., Alom, M. S., Nasim, M. A. (2019) Rice disease identification and classification by integrating support vector machine with deep convolutional neural network (ICASERT): IEEE. pp.1-6. 10.1109/ICASERT.2019.8934568
63
Hemmati, P., Zafari, D., Mahmoodi, S. B., Hashemi, M., Gholamhoseini, M., Dolatabadian, A. (2018) Histopathology of charcoal rot disease (Macrophomina phaseolina) in resistant and susceptible cultivars of soybean. Rhizosphere 7:27-34. 10.1016/j.rhisph.2018.06.009
64
Hong, J. K., Sung, C. H., Kim, D. K., Yun, H.-T., Jung, W., Kim, K. D. (2012) Differential effect of delayed planting on soybean cultivars varying in susceptibility to bacterial pustule and wildfire in Korea. Crop Protection 42:244-249. 10.1016/j.cropro.2012.07.014
65
Hossain, S. M., Tanjil, M., Morhsed, M., Ali, M. A. B., Islam, M. Z., Islam, M. (2020) Rice leaf diseases recognition using convolutional neural networks. Springer. pp.299-314. 10.1007/978-3-030-65390-3_23
66
Islam, T., Sah, M., Baral, S., Choudhury, R. R. (2018) A faster technique on rice disease detectionusing image processing of affected area in agro-field. (ICICCT). 10.1109/ICICCT.2018.847332230243917
67
Jadhav, S. B., Udup, V. R., Patil, S. B. (2019) Soybean leaf disease detection and severity measurement using multiclass SVM and KNN classifier. Int J Electr Compute Eng 9:4092. 10.11591/ijece.v9i5.pp4077-4091
68
Jadhav, S. B., Udupi, V. R., Patil, S. B. (2021) Identification of plant diseases using convolutional neural networks. Int J Inf Technol 13:2461-2470. 10.1007/s41870-020-00437-5
69
Jahan, N., Zhang, Z., Liu, Z., Friskop, A., Flores, P., Mathew, J. J. (2021) Using images from a handheld camera to detect wheat bacterial leaf streak disease severities. St. Joseph, MI: ASABE. 10.13031/aim.202100112
70
Jiang, F., Lu, Y., Chen, Y., Cai, D., Li, G. (2020) Image recognition of four rice leaf diseases based on deep learning and support vector machine. Compute Electron Agric 179:105824. 10.1016/j.compag.2020.105824
71
Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez-Vaamonde, S., Navajas, A. D. (2017) Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Compute Electron Agric 138:200-209. 10.1016/j.compag.2017.04.013
72
Kai, S., Zhikun, L., Hang, S., Chunhong, G. (2011) A research of maize disease image recognition of corn based on BP networks. 10.1109/ICMTMA.2011.6621617312
73
Kang, M., Zuber, M. (1988) Yellow- and white-endosperm effects on Stewart's wilt of maize. Phytopathology 78:909-911. 10.1094/Phyto-78-909
74
Karlekar, A., Seal, A. (2020) SoyNet: Soybean leaf diseases classification. Compute Electron Agric 172:105342. 10.1016/j.compag.2020.105342
75
Kazan, K., Gardiner, D. M., Manners, J. M. (2012) On the trail of a cereal killer: recent advances in Fusarium graminearum pathogenomics and host resistance. Mol Plant Pathol 13:399-413. 10.1111/j.1364-3703.2011.00762.x22098555PMC6638652
76
Kendler, S., Aharoni, R., Young, S., Sela, H., Kis-Papo, T., Fahima, T. (2022) Detection of crop diseases using enhanced variability imagery data and convolutional neural networks. Compute Electron Agric 193:106732. 10.1016/j.compag.2022.106732
77
Khalili, E., Kouchaki, S., Ramazi, S., Ghanati, F. (2020) Machine learning techniques for soybean charcoal rot disease prediction. Front in plant sci 11:2009. 10.3389/fpls.2020.59052933381132PMC7767839
78
Khan, M. S., Uandai, S. B., Srinivasan, H. (2019) Anthracnose disease diagnosis by image processing, support vector machine and correlation with pigments. J Plant Pathol 101:749-751. 10.1007/s42161-019-00268-9
79
Khirade, S. D., Patil, A. (2015). Plant disease detection using image processing. Paper presented at the 2015 International conference on computing communication control and automation. 10.1109/ICCUBEA.2015.153
80
Kim, H. C., Kim, K.-H., Song, K., Kim, J. Y., Lee, B.-M. (2020) Identification and validation of candidate genes conferring resistance to downy mildew in maize (Zea mays L.). Genes 11:191. 10.3390/genes1102019132053973PMC7074223
81
Kiruthika, U., Kanagasuba Raja, S., Jaichandran, R., Priyadharshini, C. (2019) Detection and classification of paddy crop disease using deep learning techniques. Int J Recent Technol Eng 8:4353-4359. 10.35940/ijrte.C5506.098319
82
Koch, E., Slusarenko, A. (1990) Arabidopsis is susceptible to infection by a downy mildew fungus. The Plant Cell 2:437-445. 10.1105/tpc.2.5.4372152169PMC159900
83
Kumar, D., Kukreja, V. (2021) N-CNN based transfer learning method for classification of powdery mildew wheat disease (ESCI): IEEE. pp.707-710. 10.1109/ESCI50559.2021.9396972
84
Li, N., Lin, B., Wang, H., Li, X., Yang, F., Ding, X. (2019) Natural variation in ZmFBL41 confers banded leaf and sheath blight resistance in maize. Nat Genet 51:1540-1548. 10.1038/s41588-019-0503-y31570888
85
Liang, W. J., Zhang, H., Zhang, G. F., Cao, H. X. (2019) Rice blast disease recognition using a deep convolutional neural network. Scientific Reports. 9(1):2869. 10.1038/s41598-019-38966-030814523PMC6393546
86
Lim, S. (1978). Disease severity gradient of soybean downy mildew from a small focus of infection. Phytopathology 68:1774-1778. 10.1094/Phyto-68-1774
87
Lin, F., Guo, S., Tan, C., Zhou, X., Zhang, D. (2020) Identification of Rice sheath blight through spectral responses using hyperspectral images. Sensors 20:6243. 10.3390/s2021624333147714PMC7663646
88
Liu, W., Liu, J., Triplett, L., Leach, J. E., Wang, G.-L. (2014) Novel insights into rice innate immunity against bacterial and fungal pathogens. Annu Rev Phytopathol 52:213-241. 10.1146/annurev-phyto-102313-04592624906128
89
Lu, J., Hu, J., Zhao, G., Mei, F., Zhang, C. (2017a) An in-field automatic wheat disease diagnosis system. Compute Electron Agric 142:369-379. 10.1016/j.compag.2017.09.012
90
Lu, Y., Yi, S., Zeng, N., Liu, Y., Zhang, Y. (2017b) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267, 378-384. 10.1016/j.neucom.2017.06.023
91
Mendes, J., Pinho, T. M., Neves dos Santos, F., Sousa, J. J., Peres, E., Boaventura-Cunha, J., Cunha, M., Morais, R. (2020) Smartphone applications targeting precision agriculture practices?A systematic review. Agronomy. 10(6):855. 10.3390/agronomy10060855
92
Mew, T. W., Alvarez, A. M., Leach, J., Swings, J. (1993) Focus on bacterial blight of rice. Plant disease 77:5-12. 10.1094/PD-77-0005
93
Meyer, W., Sinclair, J., Khare, M. (1974) Factors affecting charcoal rot of soybean seedlings. Phytopathology 64:845-849. 10.1094/Phyto-64-845
94
Mian, M., Boerma, H., Phillips, D., Kenty, M., Shannon, G., Shipe, E., Soffes Blount, A. R., Weaver, D. B. (1998) Performance of frogeye leaf spot-resistant and-susceptible near-isolines of soybean. Plant disease 82:1017-1021. 10.1094/PDIS.1998.82.9.101730856828
95
Micheni, M. M., Kinyua, M., Too, B., Gakii, C. (2021) Maize Leaf Disease Detection using Convolutional Neural Networks. J. Appl. Comput. Sci. Math 15:15-20. 10.4316/JACSM.202101002
96
Minervini, M., Scharr, H., Tsaftaris, S. A. (2015) Image analysis: The new bottleneck in plant phenotyping [applications corner]. IEEE Signal Process Mag 32:126-131. 10.1109/MSP.2015.2405111
97
Mique Jr, E. L., Palaoag, T. D. (2018) Rice pest and disease detection using convolutional neural network. ACM pp.147-151. 10.1145/3209914.3209945
98
Mishra, S., Sachan, R., Rajpal, D. (2020) Deep convolutional neural network based detection system for real-time corn plant disease recognition. Procedia Comput Sci 167:2003-2010. 10.1016/j.procs.2020.03.236
99
Morco, R. C., Calanda, F. B., Bonilla, J. A., Corpuz, M. J. S., Avestro, J. E., Angeles, J. M. (2017) e-RICE: an expert system using rule-based algorithm to detect, diagnose, and prescribe control options for rice plant diseases in the Philippines. ResearchGate. pp.49-54. 10.1145/3168390.3168431
100
Mueller, D. S., Wise, K. A., Sisson, A. J., Allen, T. W., Bergstrom, G. C., Bosley, D. B., Bradley, C. A., Broders, K. D., Byamukama, E., Chilvers, M. I., Collins, A., Faske, T. R., Friskop, A. J., Heiniger, R. W., Hollier, C. A., Hooker, D. C., Isakeit, T., Jackson-Ziems, T. A., Jardine, D. J., Kelly, H. M., Kinzer, K., Koenning, S. R., Malvick, D. K., McMullen, M., Meyer, R. F., Paul, P. A., Robertson, A. E., Roth, G. W., Smith, D. L., Tande, C. A., Tenuta, A. U., Vincelli, P., Warner, F. (2016) Corn yield loss estimates due to diseases in the United States and Ontario, Canada from 2012 to 2015. Plant Health Prog 17:211-222. 10.1094/PHP-RS-16-0030
101
Muruganandam, P., Tandon, V., Baranidharan, B. (2022) Rice Crop Diseases and Pest Detection Using Edge Detection Techniques and Convolution Neural Network. Springer. pp.49-64. 10.1007/978-981-16-8225-4_5
102
Ngugi, L. C., MoatazAbelwahab, M. A.-Z. (2020) Recent advances in image processing techniques for automated leaf pest and disease recognition-A review. Inf Process Agric 8:27-51. 10.1016/j.inpa.2020.04.004
103
Nidhis, A., Pardhu, C. N. V., Reddy, K. C., Deepa, K. (2019) Cluster based paddy leaf disease detection, classification and diagnosis in crop health monitoring unit. Springer. pp.281-291. 10.1007/978-3-030-04061-1_29
104
Niu, X., Wang, M., Chen, X., Guo, S., Zhang, H., He, D. (2014) mage segmentation algorithm for disease detection of wheat leaves. IEEE. pp.270-273. 10.1109/ICAMechS.2014.6911663
105
Osunlaja, S. (1983) Effect of tillage on the control ofPhysoderma brown spot disease of maize in South-West Nigeria. Plant and Soil 72:73-76. 10.1007/BF02185095
106
Ou, S. H. (1985). Rice diseases. IRRI.
107
Panicker, S., Gangadharan, K. (1999) Controlling downy mildew of maize caused by Peronosclerospora sorghi by foliar sprays of phosphonic acid compounds. Crop Protection 18:115-118. 10.1016/S0261-2194(98)00101-X
108
Panigrahi, K. P., Sahoo, A. K., Das, H. (2020) A cnn approach for corn leaves disease detection to support digital agricultural system (ICOEI)(48184): IEEE. pp.678-683. 10.1109/ICOEI48184.2020.9142871
109
Parry, D., Jenkinson, P., McLeod, L. (1995) Fusarium ear blight (scab) in small grain cereals-a review. Plant pathology 44:207-238. 10.1111/j.1365-3059.1995.tb02773.x
110
Patidar, S., Pandey, A., Shirish, B. A., Sriram, A. (2020) Rice plant disease detection and classification using deep residual learning. Springer. pp.278-293. 10.1007/978-981-15-6315-7_23
111
Patil, B. V., Patil, P. S. (2021) Computational method for Cotton Plant disease detection of crop management using deep learning and internet of things platforms. Springer. pp.875-885 10.1007/978-981-15-5258-8_81
112
Pérez-Bueno, M. L., Pineda, M., Cabeza, F. M., Barón, M. (2016) Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping. Front Plant Sci 7. 10.3389/fpls.2016.01790
113
Phadikar, S., Sil, J. (2008) Rice disease identification using pattern recognition techniques. IEEE. pp.420-423. 10.1109/ICCITECHN.2008.4803079
114
Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., Johannes, A. (2019) Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Compute Electron Agric 16:280-290. 10.1016/j.compag.2018.04.002
115
Pires, R. D. L., Gonçalves, D. N., Oruê, J. P. M., Kanashiro, W. E. S., Rodrigues Jr, J. F., Machado, B. B., Goncalves, W. N. (2016) Local descriptors for soybean disease recognition. Compute Electron Agric 125:48-55. 10.1016/j.compag.2016.04.032
116
Potnis, N., Timilsina, S., Strayer, A., Shantharaj, D., Barak, J. D., Paret, M. L., Vallad, G. E., Jones, J. B. (2015) Bacterial spot of tomato and pepper: diverse Xanthomonas species with a wide variety of virulence factors posing a worldwide challenge. Mol Plant Pathol 16:907-920. 10.1111/mpp.1224425649754PMC6638463
117
Prajapati, H. B., Shah, J. P., Dabhi, V. K. (2017) Detection and classification of rice plant diseases. Intel Decis Technol 11:357-373. 10.3233/IDT-170301
118
Ramesh, S., Vydeki, D. (2020) Recognition and classification of paddy leaf diseases using Optimized Deep Neural network with Jaya algorithm. Inf Process Agric 7:249-260. 10.1016/j.inpa.2019.09.002
119
Ray, D. K., Mueller, N. D., West, P. C., Foley, J. A. (2013) Yield Trends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE 8:e66428. 10.1371/journal.pone.006642823840465PMC3686737
120
Reddy, C., Laha, G., Prasad, M., Krishnaveni, D., Castilla, N., Nelson, A., Savary, S. (2011) Characterizing multiple linkages between individual diseases, crop health syndromes, germplasm deployment, and rice production situations in India. Field Crops Res 120:241-253. 10.1016/j.fcr.2010.10.005
121
Ritchie, H., Roser, M. (2013) Crop yields. Our World in Data.
122
Roy, K., Hershman, D., Rupe, J., Abney, T. (1997) Sudden death syndrome of soybean. Plant disease 81:1100-1111. 10.1094/PDIS.1997.81.10.110030861702
123
Sankar, P., Sharma, R. (2001) Management of charcoal rot of maize with Trichoderma viride. Scientia Agricola 55:1-7.
124
Sankaran, S., Maja, J. M., Buchanon, S., Ehsani, R. (2013) Huanglongbing (Citrus Greening) Detection Using Visible, Near Infrared and Thermal Imaging Techniques. Sensors 13. 10.3390/s13020211723389343PMC3649375
125
Savary, S., Willocquet, L., Pethybridge, S. J., Esker, P., McRoberts, N., Nelson, A. (2019) The global burden of pathogens and pests on major food crops. Nat Ecol Evol 3:430-439. 10.1038/s41559-018-0793-y30718852
126
Schall, R., Nicholson, R., Warren, H. (1980) Influence of light on maize anthracnose in the greenhouse. Phytopathology 70:1023-1026. 10.1094/Phyto-70-1023
127
Scherff, R. (1973) Control of bacterial blight of soybean by. Bdellovibrio bacteriovorus 328:400-402. 10.1094/Phyto-63-400
128
Sethy, P. K., Barpanda, N. K., Rath, A. K., Behera, S. K. (2020) Deep feature based rice leaf disease identification using support vector machine. Compute Electron Agric 175:105527. 10.1016/j.compag.2020.105527
129
Shane, W., Baumer, J. (1987) Population dynamics of Pseudomonas syringae pv. syringae on spring wheat. Phytopathology 77:1399-1405. 10.1094/Phyto-77-1399
130
Sharma, M., Kumar, C. J., Deka, A. (2022) Early diagnosis of rice plant disease using machine learning techniques. Arch Phytopathol Pflanzenschutz 55:259-283. 10.1080/03235408.2021.2015866
131
Sharma, R., Das, S., Gourisaria, M. K., Rautaray, S. S., Pandey, M. (2020) A model for prediction of paddy crop disease using CNN. Springer. pp.533-543. 10.1007/978-981-15-2414-1_54
132
Shrivastava, S., Singh, S. K., Hooda, D. S. (2015) Color sensing and image processing-based automatic soybean plant foliar disease severity detection and estimation. Multimed Tools Appl 74:11467-11484. 10.1007/s11042-014-2239-0
133
Shrivastava, S., Singh, S. K., Hooda, D. S. (2017) Soybean plant foliar disease detection using image retrieval approaches. Multimed Tools Appl 76:26647-26674. 10.1007/s11042-016-4191-7
134
Shrivastava, V. K., Pradhan, M. K. (2021) Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103:17-26. 10.1007/s42161-020-00683-3
135
Shrivastava, V. K., Pradhan, M. K., Thakur, M. P. (2021) Application of pre-trained deep convolutional neural networks for rice plant disease classification. IEEE, pp.1023-1030. 10.1109/ICAIS50930.2021.9395813
136
Shrivastava, V. K., Pradhan, M. K., Minz, S., Thakur, M. P. (2019) Rice plant disease classification using transfer learning of deep convolution neural network. Int Arch of the PhotogrammRemote Sens Spat Inf Sci 42:W6. 10.5194/isprs-archives-XLII-3-W6-631-2019
137
Sibiya, M., Sumbwanyambe, M. (2019) A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering 1:119-131. 10.3390/agriengineering1010009
138
Silva, O., Santos, H., Dalla Pria, M., May-De Mio, L. (2011) Potassium phosphite for control of downy mildew of soybean. Crop Protection 30:598-604. 10.1016/j.cropro.2011.02.015
139
Singh, A., Ganapathysubramanian, B., Singh, A. K., Sarkar, S. (2016) Machine Learning for High-Throughput Stress Phenotyping in Plants. Trends Plant Sci 21:110-124. 10.1016/j.tplants.2015.10.01526651918
140
Singh, D., Park, R., McIntosh, R. (2001) Postulation of leaf (brown) rust resistance genes in 70 wheat cultivars grown in the United Kingdom. Euphytica 120:205-218. 10.1023/A:1017578217829
141
Singh, V., Sharma, N., Singh, S. (2020) A review of imaging techniques for plant disease detection. Art Intel Agric 4:229-242. 10.1016/j.aiia.2020.10.002
142
Sinha, S., Prasad, M. (1977) Bacterial stalk rot of maize, its symptoms and host-range. Zentralblatt für Bakteriologie, Parasitenkunde, Infektionskrankheiten und Hygiene. Zweite Naturwissenschaftliche Abteilung: Allgemeine, Landwirtschaftliche und Technische Mikrobiologie 132:81-88. 10.1016/S0044-4057(77)80037-3
143
Smart, M., Wicklow, D., Caldwell, R. (1990) Pathogenesis in Aspergillus ear rot of maize: light microscopy of fungal spread from wounds. Phytopathology 80:1287-1294. 10.1094/Phyto-80-1287
144
Srinivasachary, L. (2011) Resistance to rice sheath blight (Rhizoctonia solani Kuhn)[teleomorph: Thanatephorus cucumeris (AB Frank) Donk.] disease: current status and perspectives. Euphytica 178:1-22. 10.1007/s10681-010-0296-7
145
Su, W. H., Zhang, J., Yang, C., Page, R., Szinyei, T., Hirsch, C. D., Steffenson, B. J. (2020) Automatic evaluation of wheat resistance to fusarium head blight using dual mask-RCNN deep learning frameworks in computer vision. Remote sensing 13:26. 10.3390/rs13010026
146
Subedi, S. (2015) A review on important maize diseases and their management in Nepal. JMRD 1:28-52. 10.3126/jmrd.v1i1.14242
147
Sundin, G. W., Castiblanco, L. F., Yuan, X., Zeng, Q., Yang, C.-H. (2016) Bacterial disease management: challenges, experience, innovation and future prospects. Mol Plant Pathol 17:1506-1518. 10.1111/mpp.1243627238249PMC6638406
148
Tanaka, E., Ashizawa, T., Sonoda, R., Tanaka, C. (2008) Villosiclava virens gen. nov., comb. nov., the teleomorph of Ustilaginoidea virens, the causal agent of rice false smut. Mycotaxon 106:491-501.
149
Tian, Y., Zhao, C., Lu, S., Guo, X. (2011) Multiple classifier combination for recognition of wheat leaf diseases. Intel Autom Soft Compute 17:519-529. 10.1080/10798587.2011.10643166
150
Ullstrup, A. (1972) The impacts of the southern corn leaf blight epidemics of 1970-1971. Annu Rev phytopathol 10:37-50. 10.1146/annurev.py.10.090172.000345
151
Ullstrup, A. J. (1953) Several ear rots of corn. Plant Diseases.
152
Upadhyay, S. K., Kumar, A. (2022) A novel approach for rice plant diseases classification with deep convolutional neural network. Int J Inf Technol 14:185-199. 10.1007/s41870-021-00817-5
153
Urashima, A., Igarashi, S., Kato, H. (1993) Host range, mating type, and fertility of Pyricularia grisea from wheat in Brazil. Plant Disease 77:1211-1216. 10.1094/PD-77-1211
154
Vasantha, S. V., Kiranmai, B., Krishna, S. R. (2021) Techniques for Rice Leaf Disease Detection using Machine LearningAlgorithms. Int. J. Eng. Res. Technol 9:162-166.
155
Vázquez-Arellano, M., Griepentrog, H. W., Reiser, D., Paraforos, D. S. (2016) 3-D Imaging Systems for Agricultural Applications-A Review. Sensors 16. 10.3390/s1605061827136560PMC4883309
156
Velu, M., Abimannan, S. (2022) Computational Approaches for Detection and Classification of Crop Diseases. Springer. pp.89-117. 10.1007/978-3-030-78284-9_5
157
Verma, T., Dubey, S. (2020) Impact of Color Spaces and Feature Sets in Automated Plant Diseases Classifier: A Comprehensive Review Based on Rice Plant Images. Arch Compute Methods Eng 27:1611-1632. 10.1007/s11831-019-09364-6
158
Waheed, A., Goyal, M., Gupta, D., Khanna, A., Hassanien, A. E., Pandey, H. M. (2020) An optimized dense convolutional neural network model for disease recognition and classification in corn leaf. Compute Electron Agric 175:105456. 10.1016/j.compag.2020.105456
159
Wallelign, S., Polceanu, M., Buche, C. (2018) Soybean plant disease identification using convolutional neural network. The thirty-first international flairs conference.
160
Ward, J. M., Stromberg, E. L., Nowell, D. C., Nutter Jr, F. W. (1999) Gray leaf spot: a disease of global importance in maize production. Plant disease 83:884-895. 10.1094/PDIS.1999.83.10.88430841068
161
Wilkie, J. P. (1973) Basal glume rot of wheat in New Zealand. New Zealand Journal of Agricultural Research. 16(1):155-160. 10.1080/00288233.1973.10421176
162
Williams, D., Nyvall, R. (1980). Leaf infection and yield losses caused by brown spot and bacterial blight diseases of soybean. Phytopathology 70:900. 10.1094/Phyto-70-900
163
Williams, P. J., Geladi, P., Britz, T. J., Manley, M. (2012) Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis. J Cereal Sci 55:272-278. 10.1016/j.jcs.2011.12.003
164
Xie, Y., Plett, D., Liu, H. (2022) Detecting Crown Rot Disease in Wheat in Controlled Environment Conditions Using Digital Color Imaging and Machine Learning. AgriEngineering 4:141-155. 10.3390/agriengineering4010010
165
Xiong, Y., Liang, L., Wang, L., She, J., Wu, M. (2020) Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Compute Electron Agric 177:105712. 10.1016/j.compag.2020.105712
166
Xu, P., Wu, G., Guo, Y., Yang, H., Zhang, R. (2017) Automatic wheat leaf rust detection and grading diagnosis via embedded image processing system. Procedia Comput Sci 10:836-841. 10.1016/j.procs.2017.03.177
167
Yan, X., Talbot, N. J. (2016). Investigating the cell biology of plant infection by the rice blast fungus Magnaporthe oryzae. Curr Opin Microbiol 34:147-153. 10.1016/j.mib.2016.10.00127816794
168
Yao, Q., Zhang, C., Wang, Z., Yang, B., Tang, J. (2017) Design and experiment of agricultural diseases and pest image collection and diagnosis system with distributed and mobile device. Trans Chin Soc Agric Eng 33:184-191.
169
Yashitola, J., Krishnaveni, D., Reddy, A., Sonti, R. (1997) Genetic diversity within the population of Xanthomonas oryzae pv. oryzae in India. Phytopathology 87:760-765. 10.1094/PHYTO.1997.87.7.76018945099
170
Zhang, D., Daoyong, W., Shizhou, D., Huang, L., Haitao, Z., Liang, D. (2019) A rapidly diagnosis and application system of fusarium head blight based on smartphone. IEEE. pp.1-5. 10.1109/Agro-Geoinformatics.2019.8820529
171
Zhang, K., Wu, Q., Chen, Y. (2021) Detecting soybean leaf disease from synthetic image using multi-feature fusion faster R-CNN. Compute Electron Agric 183:106064. 10.1016/j.compag.2021.106064
172
Zhang, X., Qiao, Y., Meng, F., Fan, C., Zhang, M. (2018). Identification of maize leaf diseases using improved deep convolutional neural networks. Ieee Access 6:30370-30377. 10.1109/ACCESS.2018.2844405
Information
  • Publisher :Agriculture and Life Sciences Research Institute, Kangwon National University
  • Publisher(Ko) :강원대학교 농업생명과학대학 농업생명과학연구원
  • Journal Title :Journal of Agricultural, Life and Environmental Sciences
  • Journal Title(Ko) :농업생명환경연구
  • Volume : 34
  • No :2
  • Pages :97-117
  • Received Date :2022. 05. 13
  • Revised Date :2022. 05. 30
  • Accepted Date : 2022. 06. 13