Towards detecting crop diseases and pest by supervised learning
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Jul 15, 2015
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Abstract
The climate change has caused threats to agricultural production; the extremes of temperature and humidity, and other abiotic stresses are contributing factors to the etiology of disease and pest on crops. About the matter, recent research efforts have focused on predicting disease and pest crops using techniques such as supervised learning algorithms. Therefore in this paper, we present an overview of supervised learning algorithms commonly used in agriculture for the detection of pests and diseases in crops such as corn, rice, coffee, mango, peanut, and tomato, among others, with the aim of selecting the algorithms that give the best performance for the agricultural sector.
Keywords
supervised learning, classifier, crop, disease, pest, agricultureaprendizaje supervisado, clasificador, cultivo, enfermedad, peste, agricultura
References
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[33] R. Jain, S. Minz, and V. Ramasubramanian, “Machine learning for forewarning crop diseases”, J Ind Soc Agricult Stat., vol. 63, pp. 97-107, 2009.
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[36] C. A. A. Meira and L. H. A. Rodrigues, “Árvore de decisão na análise de epidemias da ferrugem do cafeeiro”, en VI Simpósio de Pesquisa dos Cafés do Brasil, 2009.
[37] C. A. A. Meira, L. H. A. Rodrigues, and S. A. d. Moraes, “Modelos de alerta para o controle da ferrugem-do-cafeeiro em lavouras com alta carga pendente”, Pesq Agrop Bras., vol. 44, pp. 233-242, 2009.
[38] O. Luaces, L. H. A. Rodrigues, C. A. A. Meira, R. Quevedo, and A. Bahamonde, Viability of an alarm predictor for coffee rust disease using interval regression. Paper presented at the Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems, Part II. Cordoba, Spain, 2010.
[39] O. Luaces, L. H. A. Rodrigues, C. A. Alves Meira, and A. Bahamonde, “Using nondeterministic learners to alert on coffee rust disease”, Expert Syst Appl., vol. 38, no. 11, pp. 14276-14283, 2011.
[40] C. B. Pérez-Ariza, A. E. Nicholson, and M. J. Flores, “Prediction of coffee rust disease using bayesian networks”, en The Sixth European Workshop on Probabilistic Graphical Models, Granada, España, 2012.
[41] D. C. Corrales, A. J. Peña, C. Leon, A. Figueroa, and J. C. Corrales, “Early warning system for coffee rust disease based on Error Correcting Output Codes: a proposal”, Ingen Univ Med., vol. 13, no. 5, 2014.
[42] R. Ghaffari, Z. Fu, D. Iliescu, E. Hines, M. Leeson, Napier, R. et al., “Early detection of diseases in tomato crops: An Electronic Nose and intelligent systems approach”, en Neural Networks (IJCNN), The 2010 International Joint Conference on., 18-23 julio, 2010.
[43] G. Pokharel and R. Deardon, “Supervised learning and prediction of spatial epidemics”, Spatial Spatio-temporal Epidemiol., vol. 1, no. 0, pp. 59-77, 2014.
[44] A. K. Tripathy, J. Adinarayana, D. Sudharsan, S. N. Merchant, U. B. Desai, K. Vijayalakshmi et al., “Data mining and wireless sensor network for agriculture pest/disease predictions”, en Information and Communication Technologies (WICT), 2011 World Congress on, 11-14 dic. 2011.
[45] A. K. Tripathy, J. Adinarayana, K. Vijayalakshmi, S. N. Merchant, U. B. Desai, S. Ninomiya et al., “Knowledge discovery and Leaf Spot dynamics of groundnut crop through wireless sensor network and data mining techniques”, Comp Electr Agric., vol. 107, no. 0, pp. 104-114, 2014.
[46] M. Watts and S. Worner, “Predicting the distribution of fungal crop diseases from abiotic and biotic factors using multi-layer perceptrons”, en M. Köppen, N. Kasabov & G. Coghill (Eds.), Advances in Neuro-Information Processing. Berlin Heidelberg: Springer, 2009, pp. 901-908.
[47] W. Haiguang and M. Zhanhong, “Prediction of wheat stripe rust based on support vector machine”, en Natural Computation (ICNC), 2011 Seventh International Conference on, 26-28 julio 2011.
[48] S. Sannakki, V. S. Rajpurohit, F. Sumira, and H. Venkatesh, “A neural network approach for disease forecasting in grapes using weather parameters”, en Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, 4-6 julio 2013.
[49] M. G. Hill, P. G. Connolly, P. Reutemann, and D. Fletcher, “The use of data mining to assist crop protection decisions on kiwifruit in New Zealand”, Comp Electr Agricult., vol. 108, no. 0, pp. 250-257, 2014.
[50] B. Kitchenham and S. Charters, Guidelines for performing Systematic Literature Reviews in Software Engineering. UK: Keele University and Durham University Joint Report, 2007.
[51] H. Bhavsar and A. Ganatra, “A comparative study of training algorithms for supervised machine learning”, Int J Soft Comput Engineer., vol. 2, no. 4, pp. 74-81, 2012.
[52] S. B. Kotsiantis, “Supervised machine learning: a review of classification techniques”, en Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, 2007.
[53] J. Ghosh, “Multiclassifier systems: back to the future”, en Proceedings of the Third International Workshop on Multiple Classifier Systems, 2002.
[54] L. Li, B. Zou, Q. Hu, X. Wu, and D. Yu, “Dynamic classifier ensemble using classification confidence”, Neurocomputing, vol. 99, no. 0, pp. 581-591, 2013.
[55] R. Ranawana and V. Palade, “Multi-classifier systems: review and a roadmap for developers”, Int. J. Hybrid Intell. Syst., vol. 3, no. 1, pp. 35-61, 2006.
[2] H. R. Schultz, “¿Cómo puede afectar el clima a la viticultura en Europa?”, Rev Enol., no. 93, s. p. 2007.
[3] D. C. Corrales, I. D. López, F. Campo, S. A. Ordóñez, J. C. Corrales, A. F. Casas et al., “Plataforma para el seguimiento de variables meteorológicas y ambientales para el sector agropecuario”, en VII Congreso Ibérico de AgroIngeniería y Ciencias Hortícolas, Madrid, 2013.
[4] D. Sauri, “Potential effects of global change at Catalonian’s socio– economic and cultural levels”, Proc. of Adaptation to climate change: bridging science and decision making Seminary, 2007.
[5] R. Savé, “Potential effects of global change on Catalonian’s agriculture”, Proc. of Adaptation to climate change: bridging science and decision making Seminary ETC/LUSI/GenCat/UAB, 2007.
[6] E. Lowry, E. J. Rollinson, A. J. Laybourn, T. E. Scott, M. E. Aiello-Lammens, S. M. Gray, et al., “Biological invasions: a field synopsis, systematic review, and database of the literature”, Ecol Evol., vol. 3, no. 1, pp. 182-196, 2013.
[7] B. S. Araujo, Aprendizaje automático: conceptos básicos y avanzados: aspectos prácticos utilizando el software Weka. España: Pearson Prentice Hall, 2006.
[8] A. Mucherino, P. Papajorgji, and P. Pardalos, Data Mining in Agriculture. Springer, 2009.
[9] D. C. Corrales, A. Ledezma, A. Peña, J. Hoyos, A. Figueroa, and J. C. Corrales, “A new dataset for coffee rust detection in Colombian crops base on classifiers”, Sist Telemát., vol. 12, no. 29, pp. 9-22, 2014.
[10] R. Kaundal, A. Kapoor, and G. Raghava, “Machine learning techniques in disease forecasting: a case study on rice blast prediction”, BMC Bioinformatics, vol. 7, p. 485, 2006.
[11] A. Ng, (Producer). CS 229 Machine Learning Course Materials. Supervised learning. 2003. [Online]. Available: http://cs229.stanford.edu/materials.html
[12] S. Becker, La propagación de la roya del cafeto. Sociedad alemana de cooperación técnica (GTZ), 1979. pp. 70.
[13] T. Mitchell, Machine learning. Maidenhead, U.K.: McGraw-Hill, 1997.
[14] G. Meyfroidt, F. Güiza, J. Ramon, and M. Bruynooghe, “Machine learning techniques to examine large patient databases”, Best Pract Res Clin Anaesthesiol., vol. 23, no. 1, pp. 127-143, 2009.
[15] J. R. Quinlan, “Induction of decision trees”, Mach. Learn., vol. 1, no. 1, pp. 81-106, 1986.
[16] J. R. Quinlan, C4.5: programs for machine learning. Burlington, MA: Morgan Kaufmann Publishers, 1993.
[17] L. Breiman, J. H. Friedman, R. A. Olshen, C. J. Stone, and R. Trees, Classification and Regression Trees. Wads-worth & Brooks/Cole Advanced Books & Software, 1984.
[18] G. V. Kass, “An exploratory technique for investigating large quantities of categorical data”, J Royal Statist Soc., vol. 29, no. 2, pp. 119-127, 1980.
[19] W.-Y. Loh and Y.-S. Shih, “Split selection methods for classification trees”, Statist Sinica, vol. 7, pp. 815-840, 1997.
[20] M. E. Cintra, C. A. A. Meira, M. C. Monard, H. A. Camargo, and L. H. A. Rodríguez, “The use of fuzzy decision trees for coffee rust warning in Brazilian crops”, en Intell Syst Design Applic. (ISDA), 11th International Conference, 22-24 Nov. 2011.
[21] C. S. Cinca and B. M. Brío, “Predicción de la quiebra bancaria mediante el empleo de redes neuronales artificiales”, Rev esp financ contab., vol. 74, pp. 153-176, 1993.
[22] J. Hilera and V. Martínez, “Redes neuronales artificiales”, Fund, Mod Aplic., 1995.
[23] C. Bishop, Neural Networks for Pattern Recognition. Oxford: Clarendon, 1996.
[24] R. Erb, “Introduction to backpropagation neural network computation”, Biomed Life Sci., vol. 2, pp. 165-170, 1993.
[25] C.-S. Cheng, “A multi-layerneuralnetwork model for detecting changes in the process mean”, Comp Ind Engine., vol. 28, pp. 51-61, 2000.
[26] V. N. Vapnik, Statistical learning theory. Wiley, 1998.
[27] T. O. Ayodele, “Types of machine learning algorithms”, en Y. Zhang (Ed.), New advances in machine learning. India: In-Tech, 2010, pp. 20-48.
[28] J. Hernandez, and S. Salazar, “Implementación de una máquina de vectores soporte empleando FPGA”, Sci Tech., vol. 31, pp. 47-52, 2006.
[29] A. G. Morales and G. Hernández, “Utilización de las maquinas con vectores de soporte para regresión: m² de construcción en Bogotá”, Rev Av Sist Inf., vol. 6, pp. 21-28, 2009.
[30] E. Fix and J. L. Hodges, “Discriminatory analysis: nonparametric discrimination: consistency properties”, USAF School of Aviation Medicine, 1951.
[31] P. A. Paul and G. M. Munkvold, “A Model-based approach to preplanting risk assessment for gray leaf spot of maize”, Am Phytopatholog Soc: Ecol Epidemiol., P-2004-1011-04R.
[32] K. Liu and Z. Wang, “Rice blast prediction based on gray ant colony and RBF neural network combination model”, en Comput Intell Design (ISCID), 2012 Fifth International Symposium, 28-29 oct., 2012.
[33] R. Jain, S. Minz, and V. Ramasubramanian, “Machine learning for forewarning crop diseases”, J Ind Soc Agricult Stat., vol. 63, pp. 97-107, 2009.
[34] L. Japiassu, A. García, A. Miguel, C. Carvalho, R. Ferreira, L. Padilha et al., “Effect of crop load, tree density and weather conditions on the development of the coffee leaf rust”, en Simposio de pesquisa dos cafes do Brasil, 5, 2007.
[35] C. Meira, L. Rodrigues, and S. Moraes, “Análise da epidemia da ferrugem do cafeeiro com árvore de decisão”, Tropical Plant Pathol., vol. 33, no. 2, pp. 114-124, 2008.
[36] C. A. A. Meira and L. H. A. Rodrigues, “Árvore de decisão na análise de epidemias da ferrugem do cafeeiro”, en VI Simpósio de Pesquisa dos Cafés do Brasil, 2009.
[37] C. A. A. Meira, L. H. A. Rodrigues, and S. A. d. Moraes, “Modelos de alerta para o controle da ferrugem-do-cafeeiro em lavouras com alta carga pendente”, Pesq Agrop Bras., vol. 44, pp. 233-242, 2009.
[38] O. Luaces, L. H. A. Rodrigues, C. A. A. Meira, R. Quevedo, and A. Bahamonde, Viability of an alarm predictor for coffee rust disease using interval regression. Paper presented at the Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems, Part II. Cordoba, Spain, 2010.
[39] O. Luaces, L. H. A. Rodrigues, C. A. Alves Meira, and A. Bahamonde, “Using nondeterministic learners to alert on coffee rust disease”, Expert Syst Appl., vol. 38, no. 11, pp. 14276-14283, 2011.
[40] C. B. Pérez-Ariza, A. E. Nicholson, and M. J. Flores, “Prediction of coffee rust disease using bayesian networks”, en The Sixth European Workshop on Probabilistic Graphical Models, Granada, España, 2012.
[41] D. C. Corrales, A. J. Peña, C. Leon, A. Figueroa, and J. C. Corrales, “Early warning system for coffee rust disease based on Error Correcting Output Codes: a proposal”, Ingen Univ Med., vol. 13, no. 5, 2014.
[42] R. Ghaffari, Z. Fu, D. Iliescu, E. Hines, M. Leeson, Napier, R. et al., “Early detection of diseases in tomato crops: An Electronic Nose and intelligent systems approach”, en Neural Networks (IJCNN), The 2010 International Joint Conference on., 18-23 julio, 2010.
[43] G. Pokharel and R. Deardon, “Supervised learning and prediction of spatial epidemics”, Spatial Spatio-temporal Epidemiol., vol. 1, no. 0, pp. 59-77, 2014.
[44] A. K. Tripathy, J. Adinarayana, D. Sudharsan, S. N. Merchant, U. B. Desai, K. Vijayalakshmi et al., “Data mining and wireless sensor network for agriculture pest/disease predictions”, en Information and Communication Technologies (WICT), 2011 World Congress on, 11-14 dic. 2011.
[45] A. K. Tripathy, J. Adinarayana, K. Vijayalakshmi, S. N. Merchant, U. B. Desai, S. Ninomiya et al., “Knowledge discovery and Leaf Spot dynamics of groundnut crop through wireless sensor network and data mining techniques”, Comp Electr Agric., vol. 107, no. 0, pp. 104-114, 2014.
[46] M. Watts and S. Worner, “Predicting the distribution of fungal crop diseases from abiotic and biotic factors using multi-layer perceptrons”, en M. Köppen, N. Kasabov & G. Coghill (Eds.), Advances in Neuro-Information Processing. Berlin Heidelberg: Springer, 2009, pp. 901-908.
[47] W. Haiguang and M. Zhanhong, “Prediction of wheat stripe rust based on support vector machine”, en Natural Computation (ICNC), 2011 Seventh International Conference on, 26-28 julio 2011.
[48] S. Sannakki, V. S. Rajpurohit, F. Sumira, and H. Venkatesh, “A neural network approach for disease forecasting in grapes using weather parameters”, en Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, 4-6 julio 2013.
[49] M. G. Hill, P. G. Connolly, P. Reutemann, and D. Fletcher, “The use of data mining to assist crop protection decisions on kiwifruit in New Zealand”, Comp Electr Agricult., vol. 108, no. 0, pp. 250-257, 2014.
[50] B. Kitchenham and S. Charters, Guidelines for performing Systematic Literature Reviews in Software Engineering. UK: Keele University and Durham University Joint Report, 2007.
[51] H. Bhavsar and A. Ganatra, “A comparative study of training algorithms for supervised machine learning”, Int J Soft Comput Engineer., vol. 2, no. 4, pp. 74-81, 2012.
[52] S. B. Kotsiantis, “Supervised machine learning: a review of classification techniques”, en Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies, 2007.
[53] J. Ghosh, “Multiclassifier systems: back to the future”, en Proceedings of the Third International Workshop on Multiple Classifier Systems, 2002.
[54] L. Li, B. Zou, Q. Hu, X. Wu, and D. Yu, “Dynamic classifier ensemble using classification confidence”, Neurocomputing, vol. 99, no. 0, pp. 581-591, 2013.
[55] R. Ranawana and V. Palade, “Multi-classifier systems: review and a roadmap for developers”, Int. J. Hybrid Intell. Syst., vol. 3, no. 1, pp. 35-61, 2006.
How to Cite
Corrales, D. C., Corrales-Muñoz, J. C., & Figueroa-Casas, A. (2015). Towards detecting crop diseases and pest by supervised learning. Ingenieria Y Universidad, 19(1), 207–228. https://doi.org/10.11144/Javeriana.iyu19-1.tdcd
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Articles