Evaluating late blight severity in potato crops using unmanned aerial vehicles and machine learning algorithms

Remote Sensing

Volumn 10, Issue 10, 2018, Pages

  Duarte Carvajalino, Julio M ^a   Alzate, Diego F ^a   Ramirez, Andrés A ^b   Santa Sepulveda, Juan D ^a   Fajardo Rojas, Alexandra E ^a   Soto Suárez, Mauricio ^a  

^a CENTRO DE INVESTIGACIÓN TIBAITATÁ   (Colombia)

^b MONSANTO COMPANY   (United States)

Author keywords

Deep learning; Multispectral; Neural networks; Phytophthora infestans; Remote sensing; UAV

Indexed keywords

ANTENNAS; CONVOLUTION; CROPS; DECISION TREES; DEEP LEARNING; DEEP NEURAL NETWORKS; FORECASTING; MULTILAYER NEURAL NETWORKS; MULTILAYERS; NEURAL NETWORKS; PLANTS (BOTANY); REMOTE SENSING; UNMANNED AERIAL VEHICLES (UAV);

CANOPY LEVEL; CONVOLUTIONAL NEURAL NETWORK; MULTI-SPECTRAL; MULTI-SPECTRAL DATA; PHYTOPHTHORA INFESTANS; QUANTITATIVE PREDICTION; RANDOM FORESTS; SUPPORT VECTOR REGRESSION (SVR);

LEARNING ALGORITHMS;

EID: 85055457969     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10101513     Document Type: Article

References (41)

1. Hwang, Y.T.;Wijekoon, C.; Kalischuk, M.; Johnson, D.; Howard, R.; Prüfer, D.; Kawchuk, L. Evolution and Management of the Irish Potato Famine Pathogen Phytophthora Infestans in Canada and the United States. Am. J. Potato Res. 2014, 91, 579-593
2. Vargas, A.M.; Ocampo, L.M.Q.; Céspedes, M.C.; Carreño, N.; González, A.; Rojas, A.; Zuluaga, A.P.; Myers, K.; Fry, W.E.; Jiménez, P.; et al. Characterization of Phytophthora infestans populations in Colombia: First report of the A2 mating type. Phytopathology 2009, 99, 82-88
3. Fry, W.E. Phytophthora infestans: New Tools (and Old Ones) Lead to New Understanding and Precision Management. Annu. Rev. Phytopathol. 2016, 54, 529-547
4. European and Mediterranean Plant Protection Organization. Phytophthora infestans on potato. EPPO 2008, 38, 268-271
5. Forbes, G.; Perez, W.; Piedra, J.A. Evaluacion de la Resistencia en Genotipos de Papa a Phytophthora infestans Bajo Condiciones de Campo: Guia Para Colaboradores Internacionales; International Potato Center: Lima, Peru, 2014
6. Henfling, J.A. El tizón tardío de la papa: Phytophthora infestans. In Boletin de Informacion Tecnica; Instituto de Censores Jurados de Cuentas de España: Madrid, Spain, 1987; p. 25
7. Ray, S.S.; Jain, N.; Arora, R.K.; Chavan, S.; Panigrahy, S. Utility of Hyperspectral Data for Potato Late Blight Disease Detection. J. Indian Soc. Remote Sens. 2011, 39, 161-169
8. Franceschini, M.H.D.; Bartholomeus, H.; van Apeldoorn, D.; Suomalainen, J.; Kooistra, L. Assessing changes in potato canopy caused by late blight in organic production systems through UAV-based pushbroom imaging spectrometer. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2017, 42, 109-112
9. Biswas, S.; Jagyasi, B.; Singh, B.P.; Lal, M. Severity Identification of Potato Late Blight Disease from Crop Images Captured under Uncontrolled Environment. In Proceedings of the 2014 IEEE Canada International Humanitarian Technology Conference-(IHTC), Montreal, QC, Canada, 1-4 June 2014; pp. 1-5
10. Sugiura, R.; Tsuda, S.; Tamiya, S.; Itoh, A. ScienceDirect Field phenotyping system for the assessment of potato late blight resistance using RGB imagery from an unmanned aerial vehicle. Biosyst. Eng. 2016, 148, 1-10
11. Liakos, K.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, D. Machine Learning in Agriculture: A Review. Sensors 2018, 18, 2674
12. Balducci, F.; Impedovo, D.; Pirlo, G. Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement. Machines 2018, 6, 38
13. Durgabai, R.P.L.; Bhargavi, P. Pest Management using Machine Learning Algorithms: A Review. Int. J. Comput. Sci. Eng. Inf. Technol. Res. 2018, 8, 13-22
14. Corrales, D.C. Toward detecting crop diseases and pest by supervised learning. Ing. Univ. 2015, 19, 207-228
15. Revathi, P.; Revathi, R.; Hemalatha, M. Comparative Study of Knowledge in Crop Diseases Using Machine Learning Techniques. Int. J. Comput. Sci. Inf. Technol. 2011, 2, 2180-2182
16. Tripathi, M.K.; Maktedar, D.D. Recent machine learning based approaches for disease detection and classification of agricultural products. In Proceedings of the 2016 International Conference on Computing Communication Control and automation (ICCUBEA), Pune, India, 12-13 August 2016; pp. 1-6
17. Alves, D.P.; Tomaz, R.S.; Laurindo, B.S.; Laurindo, R.D.F.; Cruz, C.D.; Nick, C.; Silva, D.J.H.D. Artificial neural network for prediction of the area under the disease progress curve of tomato late blight. Sci. Agric. 2017, 74, 51-59
18. Moshou, D.; Bravo, C.;West, J.;Wahlen, S.; McCartney, A.; Ramon, H. Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Comput. Electron. Agric. 2004, 44, 173-188
19. Wang, X.; Zhang, M.; Zhu, J.; Geng, S. Spectral prediction of Phytophthora infestans infection on tomatoes using artificial neural network (ANN). Int. J. Remote Sens. 2008, 29, 1693-1706
20. Wasserman, P.D.; Schwartz, T. Neural networks. II. What are they and why is everybody so interested in them now? IEEE Expert 1988, 3, 10-15
21. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Process. Syst. 2012, 1097-1105
22. Firdaus, P.; Arkeman, Y.; Buono, A.; Hermadi, I. Satellite image processing for precision agriculture and agroindustry using convolutional neural network and genetic algorithm. In IOP Earth and Environmental Science; IOP Publishing: Bristol, UK, 2017; p. 7
23. Sladojevic, S.; Arsenovic, M.; Anderla, A.; Culibrk, D.; Stefanovic, D. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification. Comput. Intell. Neurosci. 2016, 2016, 11
24. Smola, A.J.; Sch, B.; Schölkopf, B. A Tutorial on Support Vector Regression. Stat. Comput. 2004, 14, 199-222
25. Ho, T.K. Random decision forests. In Proceedings of the IEEE Third International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 14-16 August 1995; pp. 278-282
26. O'Shea, K.; Nash, R. An Introduction to Convolutional Neural Networks. 2015. Available online: https://arxiv.org/pdf/1511.08458.pdf (accessed on 7 July 2018)
27. Haghighattalab, A.; Pérez, L.G.; Mondal, S.; Singh, D.; Schinstock, D.; Rutkoski, J.; Ortiz-Monasterio, I.; Singh, R.P.; Goodin, D.; Poland, J. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant. Methods 2016, 12, 35
28. Smith, G.M.; Milton, E.J. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sens. 1999, 20, 2653-2662
29. Raudys, S.; Jain, A. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13, 252-264
30. Kohavi, R. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, Montreal, QC, Canada, 20-25 August 1995; pp. 1137-1143
31. Kingma, D.P.; Ba, J. Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference on Learning Representations, Banff, AB, Canada, 14-16 April 2014; pp. 1-15
32. Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 6-11 July 2015; pp. 81-87
33. Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; Salakhutdinov, R. Dropout: A SimpleWay to Prevent Neural Networks from Overfitting. J. Mach. Learn. Res. 2014, 15, 1929-1958
34. Willmott, C.J.; Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 2005, 30, 79-82
35. Willmott, C.J.; Matsuura, K.; Robeson, S.M. Ambiguities inherent in sums-of-squares-based error statistics. Atmos. Environ. 2009, 43, 749-752
36. Fry, W.E. Phytophthora infestans: The plant (and R gene) destroyer. Mol. Plant Pathol. 2008, 9, 385-402
37. Ali, A.; Alexandersson, E.; Sandin, M.; Resjö, S.; Lenman, M.; Hedley, P.; Levander, F.; Andreasson, E. Quantitative proteomics and transcriptomics of potato in response to Phytophthora infestans in compatible and incompatible interactions. BMC Genom. 2014, 15, 497
38. Zhu, H.; Chu, B.; Zhang, C.; Liu, F.; Jiang, L.; He, Y. Hyperspectral Imaging for Presymptomatic Detection of Tobacco Disease with Successive Projections Algorithm and Machine-learning Classifiers. Sci. Rep. 2017, 7, 4125
39. Majeed, A.; Muhammad, Z.; Ullah, Z.; Ullah, R.; Ahmad, H. Late Blight of Potato (Phytophthora infestans) I: Fungicides Application and Associated Challenges. Turk. J. Agric. Food Sci. Technol. 2017, 5, 261-266
40. Van Evert, F.K.; Fountas, S.; Jakovetic, D.; Crnojevic, V.; Travlos, I.; Kempenaar, C. Big Data for weed control and crop protection. Weed Res. 2017, 57, 218-233
41. Szegedy, C. Deep Neural Networks for Object Detection. In Advances in Neural Information Processing Systems; The MIT Press: Cambridge, MA, USA, 2013; pp. 2553-2561