REVIEW
Application of machine learning: a recent advancement in plant diseases detection
1
Department of Plant Pathology, M.S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Paralakhemundi, Odisha, India
2
Division of Plant Pathology, ICAR – Indian Agricultural Research Institute, New Delhi, India
3
Department of Plant Pathology, College of Agriculture, Odisha University of Agriculture and Technology, Bhubaneswar, India
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
Submission date: 2022-01-02
Acceptance date: 2022-04-06
Online publication date: 2022-06-29
Corresponding author
Siddhartha Das
Department of Plant Pathology, M. S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Village Alluri Nagar, R Sitapur, Via- Uppalada, Pa, 761211, Paralakhemundi, India
Department of Plant Pathology, M. S. Swaminathan School of Agriculture, Centurion University of Technology and Management, Village Alluri Nagar, R Sitapur, Via- Uppalada, Pa, 761211, Paralakhemundi, India
Journal of Plant Protection Research 2022;62(2):122-135
HIGHLIGHTS
- Machine learning is an advance tool kit for plant disease detection.
- Machine based deep learning has the features of fast, high precision, high training efficiency.
- Hyperspectral/multispectral imaging technology is robust and effective in the detection of different diseases under smart agricultural technology.
KEYWORDS
TOPICS
ABSTRACT
The world population, and thus the need for food, is increasing every day. This leads to the
ultimate question of how to increase food production with limited time and scarce land.
Another obstacle to meet the food demand includes the stresses a plant goes through. These
may be abiotic or biotic, but the majority are biotic, i.e., plant diseases. The major challenge
is to mitigate plant diseases efficiently, more quickly and with less manpower. Recently,
artificial intelligence has turned to new frontiers in smart agricultural science. One novel
approach in plant science is to detect and diagnose plant disease through deep learning and
hyperspectral imaging. This smart technique is very advantageous for monitoring large
acres of field where the availability of manpower is a major drawback. Early identification
of plant diseases can be achieved through machine learning approaches. Advanced machine
learning not only detects diseases but also helps to discover gene regulatory networks
and select the genomic sequence to develop resistance in crop species and to mark pathogen
effectors. In this review, new advancements in plant science through machine learning
approaches have been discussed.
RESPONSIBLE EDITOR
Przemysław Kardasz
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (94)
1.
Amara J., Bouaziz B., Algergawy A. 2017. A Deep Learningbased approach for banana leaf diseases classification. p. 79–88. In: Proceedings of the BTW (Workshops), Stuttgart, Germany.
2.
Angermueller C., Parnamaa T., Parts L., Stegle O. 2016. Deep learning for computational biology. Molecular Systems Biology 12 (7): 878. DOI: https://doi.org/10.15252/msb.2....
3.
Arsenovic M., Karanovic M., Sladojevic S., Anderla A., Stefanovic D. 2019. Solving current limitations of deep learning based approaches for plant disease detection. Symmetry 11: 939. DOI: https://doi.org/10.3390/sym110....
4.
Baranowski P., Jedryczka M., Mazurek W., Babula-Skowronska D., Siedliska A., Kaczmarek J. 2015. Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS ONE 10 (3): e0122913. DOI: https://doi.org/10.1371/journa....
5.
Barbedo J.G.A. 2019. Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering 180: 96–107. DOI: https://doi.org/10.1016/j.bios....
6.
Barré P., Stöver B.C., Müller K.F., Steinhage V. 2017. Leaf-Net: A computer vision system for automatic plant species identification. Ecological Informatics 40: 50–56. DOI: https://doi.org/10.1016/j.ecoi....
7.
Brahimi M., Boukhalfa K., Moussaoui A. 2017. Deep learning for tomato diseases: Classification and symptoms visualization. Applied Artificial Intelligence 31 (4): 299–315. DOI: https://doi.org/10.1080/088395....
8.
Brahimi M., Arsenovic M., Laraba S., Sladojevic S., Boukhalfa K., Moussaoui A. 2018. Deep learning for plant diseases: Detection and saliency map visualisation. p. 93–117. In: “Human and Machine Learning”. Springer, Berlin, Germany.
9.
Bruce L.M., Koger C.H., Jiang L. 2002. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Transactions on Geoscience and Remote Sensing 40 (10): 2331–2338. DOI: 10.1109/TGRS.2002.804721.
10.
Chen J., Liu Q., Gao L. 2019a. Visual tea leaf disease recognition using a convolutional neural network model. Symmetry 11 (3): DOI: https://doi.org/10.3390/sym110....
11.
Chen T., Zhang J., Chen Y., Wan S., Zhang L. 2019b. Detection of peanut leaf spots disease using canopy hyperspectral reflectance. Computers and Electronics in Agriculture 156: 677–683. DOI: https://doi.org/10.1016/j.comp....
12.
Chen Y., Jiang H., Li C., Jia X., Ghamisi P. 2016. Deep feature extraction, and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing 54 (10): 6232–6251. DOI: 10.1109/TGRS.2016.2584107.
13.
Ciresan D.C., Meier U., Masci J., Gambardella L.M., Schmidhuber J. 2011. Flexible, high performance convolutional neural networks for image classification. p. 1237–1242. In: Proceedings of the Twenty-second International Joint Conference on Artificial Intelligence.
14.
Crossa J., Pérez-Rodríguez P., Cuevas J., Montesinos-López O., Jarquín D., De Los Campos G., Burgueño J., González-Camacho J.M., Pérez-Elizalde S., Beyene Y., Dreisigacker S. 2017. Genomic selection in plant breeding: methods, models, and perspectives. Trends in Plant Science 22 (11): 961–975. DOI: https://doi.org/10.1016/j.tpla....
15.
Cruz A.C., Luvisi A., De Bellis L., Ampatzidis Y. 2017. Vision-based plant disease detection system using transfer and deep learning. In: Proceedings of the ASABE Annual International Meeting. Spokane, WA, USA.
16.
De Chant C., Wiesner-Hanks T., Chen S., Stewart E.L., Yosinski J., Gore M.A., Nelson R.J., Lipson H. 2017. Automated identification of northern leaf blight-infected maize plants from field imagery using deep learning. Phytopathology 107 (11): 1426–1432. DOI: https://doi.org/10.1094/PHYTO-....
17.
Deng J., Dong W., Socher R., Li L.J., Li K., Fei-Fei L. 2009. Imagenet: A large-scale hierarchical image database. p. 248–255. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition.
18.
Durmus H., Kırcı M. 2017. Disease detection on the leaves of the tomato plants by using deep learning. p. 1–5. In: Proceedings of the 6th International Conference on Agro-Geoinformatics. Fairfax, VA, USA.
19.
Ehler L.E. 2006. Integrated pest management (IPM): definition, historical development and implementation, and the other IPM. Pest Management Science 62 (9): 787–789. DOI: 10.1002/ps.1247.
20.
Ferentinos K.P. 2018. Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture 145: 311–318. DOI: https://doi.org/10.1016/j.comp....
21.
Fuentes A., Yoon S., Kim S.C., Park D.S. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pest recognition. Sensors 17 (9): 2022. DOI: https://doi.org/10.3390/s17092....
22.
Fujita E., Kawasaki Y., Uga H., Kagiwada S., Iyatomi H. 2016. Basic investigation on a robust and practical plant diagnostic system. p. 989–992. In: Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Anaheim, CA, USA.
23.
Ghosal S., Blystone D., Singh A.K., Ganapathy Subramanian B., Singh A., Sarkar S. 2018. An explainable deep machine vision framework for plant stress phenotyping. Proceedings of the National Academy of Sciences 115 (18): 4613–4618.
24.
Gonzalez-Camacho J.M., Ornella L., Perez-Rodriguez P., Gianola D., Dreisigacker S., Crossa J. 2018. Applications of machine learning methods to genomic selection in breeding wheat for rust resistance. Plant Genome 11: 170104.
25.
Grinblat G.L., Uzal L.C., Larese M.G., Granitto P.M. 2016. Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture 127: 418–424. DOI: https://doi.org/10.1016/j.comp....
26.
Guo L., Zhao G., Xu J.R., Kistler H.C., Gao L., Ma L.J. 2016. Compartmentalized gene regulatory network of the pathogenic fungus Fusarium graminearum. New Phytologist 211 (2): 527–541. DOI: 10.1111/nph.13912.
27.
Ha J.G., Moon H., Kwak J.T., Hassan S.I., Dang M., Lee O.N., Park H.Y. 2017. Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles. Journal of Applied Remote Sensing 11 (4): 042621. DOI: https://doi.org/10.1117/1.JRS.....
28.
Harvey C.A., Rakotobe Z.L., Rao N.S., Dave R., Razafimahatratra H., Rabarijohn R.H., Rajaofara H., MacKinnon J.L. 2014. Extreme vulnerability of small holder farmers to agricultural risks and climate change in Madagascar. Philosophical Transactions of the Royal Society B: Biological Sciences 369 (1639): 20130089. DOI: https://doi.org/10.1098/rstb.2....
29.
Hruska J., Adão T., Pádua L., Marques P., Cunha A., Peres E., Sousa A., Morais R., Sousa J.J. 2018. Machine learning classification methods in hyperspectral data processing for agricultural applications. p. 137–141. In: Proceedings of the International Conference on Geoinformatics and Data Analysis. Prague, Czech Republic.
30.
Hughes D.P., Salathe M. 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060. DOI: https://doi.org/10.48550/arXiv....
31.
ITU. 2015. ICTF acts and Figures – the World in 2015. Geneva: International Telecommunication Union.
32.
Jia Y., Shelhamer E., Donahue J., Karayev S., Long J., Girshick R., Guadarrama S., Darrell T. 2014. Caffe: Convolutional architecture for fast feature embedding. p. 675–678. In: Proceedings of the 22nd ACM International Conference on Multimedia. DOI: https://doi.org/10.1145/264786....
33.
Jiang P., Chen Y., Liu B., He D., Liang C. 2019. Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks. IEEE Access 7: 59069–59080. DOI: 10.1109/ACCESS.2019.2914929.
34.
Jin X., Jie L., Wang S., Qi H., Li S. 2018. Classifying wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in the wild field. Remote Sensing 10 (3): 395. DOI: https://doi.org/10.3390/rs1003....
35.
Johannes A., Picon A., Alvarez-Gila A., Echazarra J., Rodriguez-Vaamonde S., Navajas A.D., Ortiz-Barredo A. 2017. Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture 138: 200–209. DOI: https://doi.org/10.1016/j.comp....
36.
Kamal K., Yin Z., Wu M., Wu Z. 2019. Depthwise separable convolution architectures for plant disease classification. Computers and Electronics in Agriculture 165: 104948. DOI: https://doi.org/10.1016/j.comp....
37.
Kerkech M., Hafiane A., Canals R. 2018. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. Computers and Electronics in Agriculture 155: 237–243. DOI: https://doi.org/10.1016/j.comp....
38.
Khan M.A., Akram T., Sharif M., Awais M., Javed K., Ali H., Saba T. 2018. CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coeffcient and deep CNN features. Computers and Electronics in Agriculture 155: 220–236. DOI: https://doi.org/10.1016/j.comp....
39.
Krizhevsky A., Sutskever I., Hinton G.E. 2012. Imagenet classification with deep convolutional neural networks. p. 1097–1105. In: “Advances in Neural Information Processing Systems” (F. Pereira, C.J.C. Burges, L. Bottou, K.Q. Weinberger, eds.). Curran Associates, Inc.
40.
Landgrebe D.A. 2003. Signal Theory Methods in Multispectral Remote Sensing. John Wiley & Sons, Hoboken, NJ, USA.
41.
LeCun Y., Bengio Y., Hinton G. 2015. Deep learning. Nature 521: 436–444. DOI:10.1038/nature14539.
42.
LeCun Y., Boser B., Denker J.S., Henderson D., Howard R.E., Hubbard W., et al. 1989. Back propagation applied to handwritten zip code recognition. Neural Computation 1: 541–551. DOI: 10.1162/neco.1989.1.4.541.
43.
LeCun Y., Bengio Y. 1995. Convolutional networks for images, speech, and time series. p. 10. In: “The Handbook of Brain Theory and Neural Networks”.
44.
Libbrecht M.W., Noble W.S. 2015. Machine learning applications in genetics and genomics. Nature Reviews Genetics 6 (6): 321–332. DOI: https://doi.org/10.1038/nrg392....
46.
Liu B., Zhang Y., He D., Li Y. 2017a. Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10 (1): 11. DOI: https://doi.org/10.3390/sym100....
47.
Liu S., Liu Y., Zhao J., Cai S., Qian H., Zuo K., Zhao L., Zhang L. 2017b. A computational interactome for prioritizing genes associated with complex agronomic traits in rice (Oryza sativa). The Plant Journal 90 (1): 177–188. DOI: https://doi.org/10.1111/tpj.13....
48.
Lowe A., Harrison N., French A.P. 2017. Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress. Plant Methods 13 (1): 1–12. DOI: https://doi.org/10.1186/s13007....
49.
Lu J., Hu J., Zhao G., Mei F., Zhang C. 2017. An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture 142: 369–379. DOI: https://doi.org/10.1016/j.comp....
50.
Ma X., Geng J., Wang H. 2015. Hyperspectral image classification via contextual deep learning. EURASIP Journal on Image and Video Processing 2015: 1–12. DOI: https://doi.org/10.1186/s13640....
51.
Mahlein A.K., Alisaac E., Al Masri A., Behmann J., Dehne H.W., Oerke E.C. 2019. Comparison and combination of thermal, fluorescence, and hyperspectral imaging for monitoring Fusarium head blight of wheat on spikelet scale. Sensors 19 (10): 2281. DOI: https://doi.org/10.3390/s19102....
52.
Mehra L.K., Cowger C., Gross K., Ojiambo P.S. 2016. Predicting pre-planting risk of Stagonospora nodorum blotch in winter wheat using machine learning models. Frontiers in Plant Science 7: 390. DOI: https://doi.org/10.3389/fpls.2....
53.
Moghadam P., Ward D., Goan E., Jayawardena S., Sikka P., Hernandez E. 2017. Plant disease detection using hyperspectral imaging. p. 1–8. In: Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA). Sydney, Australia.
54.
Mohanty S.P., Hughes D.P., Salathe M. 2016. Using deep learning for image-based plant disease detection. Frontiers in Plant Science 7: 1419. DOI: https://doi.org/10.3389/fpls.2....
55.
Moshou D., Bravo C., West J., Wahlen S., McCartney A., Ramon H. 2004. Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture 44 (3): 173–188. DOI: https://doi.org/10.1016/j.comp....
56.
Odilbekov F., Armoniene R., Henriksson T., Chawade A. 2018. Proximal phenotyping and machine learning methods to identify Septoria tritici blotch disease symptoms in wheat. Frontiers in Plant Science 9: 685. DOI: https://doi.org/10.3389/fpls.2....
57.
Ornella L., Gonzalez-Camacho J.M., Dreisigacker S., Crossa J. 2017. Applications of genomic selection in breeding wheat for rust resistance. Methods in Molecular Biology 1659: 173–182. DOI: https://doi.org/10.1007/978-1-....
58.
Paoletti M., Haut J., Plaza J., Plaza A. 2018. A new deep convolutional neural network for fast hyperspectral image classification. ISPRS Journal of Photogrammetry and Remote Sensing 145: 120–147. DOI: https://doi.org/10.1016/j.ispr....
59.
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. Computers and Electronics in Agriculture 161: 280–290. DOI: https://doi.org/10.1016/j.comp....
60.
Polder G., Blok P.M., de Villiers H.A.C., van der Wolf J.M., Kamp J. 2019. Potato virus Y detection in seed potatoes using deep learning on hyperspectral images. Frontiers in Plant Science 10: 209. DOI: https://doi.org/10.3389/fpls.2....
61.
Popp J., Peto K., Nagy J. 2013. Pesticide productivity and food security – a review. Agronomy for Sustainable Development 33: 243–255. DOI: https://doi.org/10.1007/s13593....
62.
Ramcharan A., Baranowski K., McCloskey P., Ahmed B., Legg J., Hughes D.P. 2017. Deep learning for image-based cassava disease detection. Frontiers in Plant Science 8: 1852. DOI: https://doi.org/10.3389/fpls.2....
63.
Rangarajan A.K., Purushothaman R., Ramesh A. 2018. Tomato crop disease classification using a pre-trained deep learning algorithm. Procedia Computer Science 133: 1040–1047. DOI: https://doi.org/10.1016/j.proc....
64.
Raza S.E., Prince G., Clarkson J.P., Rajpoot N.M. 2015. Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE 10: e0123262. DOI: https://doi.org/10.1371/journa....
65.
Rumpf T., Mahlein A.K., Steiner U., Oerke E.C., Dehne H.W., Plümer L. 2010. Early detection, and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and Electronics in Agriculture 74: 91–99. DOI: https://doi.org/10.1016/j.comp....
66.
Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A.C., Fei-Fei L. 2015. Image net large-scale visual recognition challenge. International Journal of Computer Vision 115: 211–252. DOI: 10.1007/s11263-015-0816-y.
67.
Sanchez P.A., Swaminathan M.S. 2005. Cutting world hunger in half. Science 307: 357–359.
68.
Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Networks 61: 85–117. DOI: 10.1016/j.neunet.2014.09.003.
69.
Selvaraj M.G., Vergara A., Ruiz H., Safari N., Elayabalan S., Ocimati W., Blomme G. 2019. AI-powered banana diseases and pest detection. Plant Methods 15: 92. DOI: https://doi.org/10.1186/s13007....
70.
Shaik R., Ramakrishna W. 2014. Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice. Plant Physiology 164: 481–495. DOI: https://doi.org/10.1104/pp.113....
71.
Shuaibu M., Lee W.S., Schueller J., Gader P., Hong Y.K., Kim S. 2018. Unsupervised hyperspectral band selection for apple Marssonina blotch detection. Computers and Electronics in Agriculture 148: 45–53. DOI: https://doi.org/10.1016/j.comp....
72.
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. DOI: https://doi.org/10.3390/agrien....
73.
Signoroni A., Savardi M., Baronio A., Benini S. 2019. Deep learning meets hyperspectral image analysis: a multi-disciplinary review. Journal of Imaging 5 (5): 52. DOI: https://doi.org/10.3390/jimagi....
74.
Sladojevic S., Arsenovic M., Anderla A., Culibrk D., Stefanovic D. 2016. Deep neural networks-based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience Article ID: 3289801. DOI: https://doi.org/10.1155/2016/3....
75.
Sperschneider J., Dodds P.N., Singh K.B., Taylor J.M. 2018. Apoplast P: prediction of effectors and plant proteins in the apoplast using machine learning. New Phytologist 217: 1764–1778. DOI: https://doi.org/10.1111/nph.14....
76.
Su J., Liu C., Coombes M., Hu X., Wang C., Xu X., Li Q., Guo L., Chen W.H. 2018. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Computers and Electronics in Agriculture 155: 157–166. DOI: https://doi.org/10.1016/j.comp....
77.
Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. 2015. Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer vision and Pattern Recognition. Boston, MA, USA.
78.
Too E.C., Yujian L., Njuki S., Yingchun L. 2019. A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture 161: 272–279. DOI: https://doi.org/10.1016/j.comp....
79.
Türkoğlu M., Hanbay D. 2019. Plant disease and pest detection using deep learning-based features. Turkish Journal of Electrical Engineering and Computer Sciences 27 (3): 1636–1651.
80.
Veys C., Chatziavgerinos F., Al Suwaidi A., Hibbert J., Hansen M., Bernotas G., Smith M., Yin H., Rolfe S., Grieve B. 2019. Multispectral imaging for pre-symptomatic analysis of light leaf spot in oilseed rape. Plant Methods 15: 4. DOI: https://doi.org/10.1186/s13007....
81.
Wallelign S., Polceanu M., Buche C. 2018. Soybean Plant Disease Identification using convolutional neural network. In: Proceedings of the 31st International Flairs Conference. Melbourne, FL, USA.
82.
Wang D., Vinson R., Holmes M., Seibel G., Bechar A., Nof S., Tao Y. 2019. Early detection of tomato spotted wilt virus by hyperspectral imaging and outlier removal auxiliary classifier generative adversarial nets (OR-AC-GAN). Scientific Reports 9: 4377. DOI: https://doi.org/10.1038/s41598....
83.
Wang Y., Wei X., Bao H., Liu S.L. 2014. Prediction of bacterial type IV secreted effectors by C-terminal features. BMC Genomics 15: 50. DOI: https://doi.org/10.1186/1471-2....
84.
Wang X., Zhang M., Zhu J., Geng S. 2008. Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN). International Journal of Remote Sensing 29: 1693–1706. DOI: https://doi.org/10.1080/014311....
85.
Xie C., Yang C., He Y. 2017. Hyperspectral imaging for classification of healthy and gray mold diseased tomato leaves with different infection severities. Computers and Electronics in Agriculture 135: 154–162. DOI: https://doi.org/10.1016/j.comp....
86.
Xue L., Tang B., Chen W., Luo J. 2018. DeepT3: deep convolutional neural networks accurately identify gram-negative bacterial type III secreted effectors using the N terminal sequence. Bioinformatics 35 (12): 2051–2057. DOI: https://doi.org/10.1093/bioinf....
87.
Yamamoto K., Togami T., Yamaguchi N. 2017. Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors 17: 2557. DOI: https://doi.org/10.3390/s17112....
88.
Yip K.Y., Cheng C., Gerstein M. 2013. Machine learning and genome annotation: a match meant to be? Genome Biology 14: 205. DOI: https://doi.org/10.1186/gb-201....
89.
Yue J., Zhao W., Mao S., Liu H. 2015. Spectral-spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sensing Letters 6: 468–477. DOI: https://doi.org/10.1080/215070....
90.
Zhang K., Wu Q., Liu A., Meng X. 2018a. Can Deep Learning Identify Tomato Leaf Disease? Advances in Multimedia: 1–10. DOI: https://doi.org/10.1155/2018/6....
91.
Zhang X., Qiao Y., Meng F., Fan C., Zhang M. 2018b. Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6: 30370–30377. DOI: 10.1109/ACCESS.2018.2844405.
92.
Zhang X., Han L., Dong Y., Shi Y., Huang W., Han L., González-Moreno P., Ma H., Ye H., Sobeih T. 2019a. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral uav images. Remote Sensing 11: 1554. DOI: https://doi.org/10.3390/rs1113....
93.
Zhang S., Zhang S., Zhang C., Wang X., Shi Y. 2019b. Cucumber leaf disease identification with global pooling dilated convolutional neural network. Computers and Electronics in Agriculture 162: 422–430. DOI: https://doi.org/10.1016/j.comp....
94.
Zou J., Huss M., Abid A., Mohammadi P., Torkamani A., Telenti P. 2019. A primer on deep learning in genomics. Nature Genetics 51: 12–18. DOI: https://doi.org/10.1038/s41588....
Share
RELATED ARTICLE
| eISSN: | 1899-007X |
| ISSN: | 1427-4345 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
