Modelos de predição da ferrugem do cafeeiro (Hemileia vastatrix Berkeley & Broome) por técnicas de mineração de dados

Coffee rust can cause severe yield losses if control measures are not taken. Warning models are capable of generating useful information regarding to the application of fungicides, decreasing economic losses and environmental impacts. The aim of this study was to develop, compare and select warning models developed by data mining techniques in order to predict the coffee rust in years of high and low fruit load. For 13 years (1998-2011), data was collected from an automatic weather station. The independent variables were 23, obtained from the weather station, and the dependent variable was the monthly progress rate for the coffee rust, which was generated by the values of disease incidence. The most important features were refined by feature selection techniques, and the modeling was performed using four data mining techniques: support vector machines, artificial neural networks, decision trees and random forests. For high fruit load years the best accuracy was 85.3% and for low fruit load years it was 88.9%. Other performance measures like recall and specificity also had high and balanced values. The warning models developed on this study provide further information for monitoring the disease on high fruit load years than other models previously developed, and also provide a possibility for the monitoring on years of low fruit load.9340841

Warning Models For Coffee Rust (hemileia Vastatrix Berkeley & Broome) By Data Mining Techniques [modelos De Predição Da Ferrugem Do Cafeeiro (hemileia Vastatrix Berkeley & Broome) Por Técnicas De Mineração De Dados]

Coffee rust can cause severe yield losses if control measures are not taken. Warning models are capable of generating useful information regarding to the application of fungicides, decreasing economic losses and environmental impacts. The aim of this study was to develop, compare and select warning models developed by data mining techniques in order to predict the coffee rust in years of high and low fruit load. For 13 years (1998-2011), data was collected from an automatic weather station. The independent variables were 23, obtained from the weather station, and the dependent variable was the monthly progress rate for the coffee rust, which was generated by the values of disease incidence. The most important features were refined by feature selection techniques, and the modeling was performed using four data mining techniques: support vector machines, artificial neural networks, decision trees and random forests. For high fruit load years the best accuracy was 85.3% and for low fruit load years it was 88.9%. Other performance measures like recall and specificity also had high and balanced values. The warning models developed on this study provide further information for monitoring the disease on high fruit load years than other models previously developed, and also provide a possibility for the monitoring on years of low fruit load.93408418Alves, M.C., A Soft computing approach for epidemiological studies of coffee and soybean rusts (2010) International Journal of Digital Content Technology and Its Applications, 4 (1), pp. 149-154. , Sandy Bay, FebBatchelor, W.D., Yang, X.B., Tschanz, A.T., Development of a neural network for soybean rust epidemics (1997) Transactions of the American Society of Agricultural Engineers, 40 (1), pp. 247-252. , Saint JosephBatista, G.E.A.P.A., Prati, R.C., Monard, M.C., A study of the behavior of several methods for balancing machine learning training data (2004) SIGKDD Explorations, 6 (1), pp. 20-29. , JuneBreiman, L., Random forests (2001) Machine Learning Journal, 45, pp. 5-32. , Hingham, JanChalfoun, S.M., (1997) Doenças Do Cafeeiro: Importância, Identificação E Métodos De Controle, , Lavras: UFLA/ FAEPEChang, C.C., Lin, C.J., LIBSVM: A library for support vector machines (2011) ACM Transactions On Intelligent Systems and Technology, 2 (3), pp. 1-27. , New York, AprCintra, M.E., The use of fuzzy decision trees for coffee rust warning in Brazilian crops (2011) International Conference On Intelligent Systems Design and Applications, pp. 1347-1352. , 11., 2011, Córdoba. Proceedings... Córdoba: IEEEFawcett, T., An introduction to ROC analysis (2006) Pattern Recognition Letters, 27 (8), pp. 861-874. , New York, JuneFayyad, U., Piatetsky-Shapiro, G., Smyth, P., From data mining to knowledge discovery in databases (1996) AI Magazine, Palo Alto, 17 (3), pp. 37-54. , JulyHall, M.A., The WEKA data mining software: An update. SIGKDD Explorations (2009) New York, 11 (1), pp. 10-18. , JuneHan, J., Kamber, M., Pei, J., (2011) Data Mining: Concepts and Techniques, p. 703. , 3rd ed. San Francisco: M. KaufmannHardwick, N.V., Disease forecasting (2006) The Epidemiology of Plant Diseases, pp. 239-267. , In: COOKE, B. M.JONES, D. G.KAYE, B. (Ed.), 2nd ed. Wageningen: SpringerHaykin, S., (2009) Neural Networks and Learning Machines, p. 936. , 3rd ed. Englewood Cliffs: Prentice-HallKushalappa, A.C., Akutsu, M., Ludwig, A., Application of survival ratio for monocyclic process of Hemileia vastatrix in predicting coffee rust infection rates (1983) Phytopathology, 73 (1), pp. 96-103. , Saint PaulKushalappa, A.C., Eskes, A.B., Advances in coffee rust research (1989) Annual Review of Phytopathology, 27, pp. 503-531. , Palo Alto, SeptLee, M.C., To, C., Comparison of support vector machine and back propagation neural network in evaluating the enterprise financial distress (2010) International Journal of Artificial Intelligence & Applications, 1, pp. 31-43. , NiskayunaLuaces, O., Using nondeterministic learners to alert on coffee rust disease (2011) Expert Systems With Applications, 38 (11), pp. 14276-14283. , New York, JanMeira, C.A.A., Rodrigues, L.H.A., Moraes, S.A., Análise da epidemia da ferrugem do cafeeiro com árvore de decisão (2008) Tropical Plant Pathology, 33, pp. 114-124. , Brasília, mar./abrMeira, C.A.A., Rodrigues, L.H.A., Moraes, S.A., (2009) Modelos De Alerta Para O Controle Da Ferrugem-docafeeiro Em Lavouras Com Alta Carga Pendente. Pesquisa Agropecuária Brasileira, 44 (3), pp. 233-242. , Brasília, marMolineros, J.E., Modeling epidemics of fusarium head blight: Trials and tribulations (2005) Phytopathology, Saint Paul, 95 (6), pp. S71Moraes, S.A., Período de incubação de Hemileia vastatrix Berk. e Br. em três regiões do Estado de SP (1976) Summa Phytopathologica, 2 (1), pp. 32-38. , PiracicabaPaul, P.A., Munkvold, G.P., A model-based approach to preplanting risk assessment for gray leaf spot of maize (2004) Phytopathology, 94 (12), pp. 1350-1357. , Saint PaulPaul, P.A., Munkvold, G.P., Regression and artificial neural network modeling for the prediction of gray leaf spot of maize (2005) Phytopathology, 95 (4), pp. 388-396. , Saint PaulPérez-Ariza, C.B., Nicholson, A.E., Flores, M.J., Prediction of coffee rust disease using bayesian networks (2012) European Workshop On Probabilistic Graphical Models, 6, pp. 259-266. , Granada. Proceedings... Granada: PGMPinto, A.C.S., Descrição da epidemia da ferrugem do cafeeiro com redes neuronais (2002) Fitopatologia Brasileira, 27 (5), pp. 517-524. , Brasília, set./outPrati, R.C., Batista, G.E.A.P.A., Monard, M.C., Curvas ROC para avaliação de classificadores (2008) Revista IEEE América Latina, 6, pp. 215-222. , São Paulo, junSouza, V.C.O., Técnicas de extração de conhecimento aplicadas a modelagem de ocorrência da cercosporiose (Cercospora coffeicola Berkeley & Cooke) em cafeeiros na região sul de minas gerais (2013) Coffee Science, 8 (1), pp. 91-100. , Lavras, jan./marCoffee: World Markets and Trade, , http://www.fas.usda.gov/psdonline/circulars/coffee.pdf, United States Department Of Agriculture, Disponível em, Acesso em: 15 fev. 2013Witten, I.H., Frank, E., Hall, M.A., Data Mining: Practical Machine Learning Tools and Techniques, p. 629. , 3rd ed. San Francisco: M. KaufmannZambolim, L., Epidemiologia e controle integrado da ferrugem-do-cafeeiro (2002) O Estado Da Arte De Tecnologias Na Produção De Café, pp. 369-449. , ZAMBOLIM, L. (Ed.), Viçosa, MG: UF

Analysis Of Coffee Leaf Rust Epidemics With Decision Tree [análise Da Epidemia Da Ferrugem Do Cafeeiro Com árvore De Decisão]

A decision tree was developed to aid the understanding of coffee rust epidemics caused by Hemileia vastatrix. Infection rates calculated from monthly assessments of rust incidence were grouped into three classes: reduction or stagnation - TX1; moderate growth (up to 5pp) - TX2; and accelerated growth (above 5pp) - TX3. Meteorological data, expected yield and space between plants were used as explanatory variables for the infection rate classes. The decision tree was trained using 364 examples prepared from data collected in coffee-growing areas between October 1998 and October 2006. The model correctly classified 78% of the training data set and its accuracy was estimated at 73% for the classification of new examples. The success rates of the model were 88%, 57% and 79%, respectively, for the infection rate classes TX1, TX2 and TX3. The most important explanatory variables were mean temperature during leaf wetness periods, expected yield, mean of maximum temperatures during the incubation period and relative air humidity. The decision tree demonstrated its potential as a symbolic and interpretable model. Its model representation identified the existing decision boundaries in the data and the logic underlying them, helping to understand which variables, and interactions between these variables, led to coffee rust epidemics in the field. Copyright by the Brazilian Phytopathological Society.332114124Apte, C., Weiss, S., Data mining with decision trees and decision rules (1997) Future Generation Computer Systems, 13, pp. 197-210Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., (1984) Classification and regression trees, , Boca Raton FL. CRC PressButt, D.J., Royle, D.J., Multiple regression analysis in the epidemiology of plant diseases (1974) Epidemics of plant diseases: Mathematical analysis and modeling, pp. 78-114. , Kranz J Ed, New York NY. Springer Verlag. ppChalfoun SM (1997) Doenças do cafeeiro: importância, identificação e métodos de controle. Lavras MG. FAEPE, Universidade Federal de LavrasChapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, Wirth R (2000) CRISP-DM 1.0: step-by-step data mining guide. Chicago IL. SPSSFayyad, U., Piatetsky-Shapiro, G., Smyth, P., From data mining to knowledge discovery: An overview (1996) Advances in knowledge discovery and data mining, pp. 1-34. , Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurasamy R Eds, Menlo Park CA. AAAI Press. ppGleason, M.L., Taylor, S.E., Loughin, T.M., Koehler, K.J., Development and validation of an empirical model to estimate the duration of dew periods (1994) Plant Disease, 78, pp. 1011-1016Han, J., Kamber, M., (2001) Data mining: Concepts and techniques, , San Francisco CA. Morgan KaufmannHand, D., Mannila, H., Smyth, P., (2001) Principles of data mining, , Cambridge. MIT PressJapiassú LB, Garcia AWR, Miguel AE, Carvalho CHS, Ferreira RA, Padilha L, Matiello JB (2007) Influência da carga pendente, do espaçamento e de fatores climáticos no desenvolvimento da ferragem do cafeeiro. Anais, 5°. Simpósio de Pesquisa dos Cafés do Brasil, Águas de Lindóia SP. CD-ROMKim, K.S., Taylor, S.E., Gleason, M.L., Koehler, K.J., Model to enhance site-specific estimation of leaf wetness duration (2002) Plant Disease, 86, pp. 179-185Kushalappa, A.C., Akutsu, M., Ludwig, A., Application of survival ratio monocyclic process of Hemileia vastatrix in predicting coffee rust infection rates (1983) Phytopathology, 73, pp. 96-103Kushalappa, A.C., Eskes, A.B., (1989) Coffee rust: Epidemiology, resistance, and management, , Boca Raton FL. CRC PressMolineros, J.E., Madden, L., Lipps, P., Shaner, G., Osborne, L., Shaukat, A., Francl, L., de Wolf, E.D., Comparison of methods for developing fusarium head blight forecasting models (2004) Proceedings, 2nd International Symposium on Fusarium Head Blight, p. 475. , Canty SM, Boring T, Wardwell J, Ward RW Eds, Orlando FL. pMolineros, J.E., de Wolf, E.D., Francl, L., Madden, L., Lipps, P., Modeling epidemics of fusarium head blight: Trials and tribulations (2005) Phytopathology, 95 (SUPPL.), p. 71Monard, M.C., Baranauskas, J.A., Indução de regras e árvores de decisão (2002) Rezende SO (Org.) Sistemas inteligentes: Fundamentos e aplicações. Barueri SP, pp. 115-139. , Editora Manole. ppMonard, M.C., Baranauskas, J.A., Conceitos sobre aprendizado de máquina (2002) Rezende SO (Org.) Sistemas inteligentes: Fundamentos e aplicações. Barueri SP, pp. 89-114. , Editora Manole. ppMontoya, R.H., Chaves, G.M., Influência da temperatura e da luz na germinação, infectividade e período de geração de Hemileia vastatrix Berk. & Br. (1974) Experientiae, 18, pp. 239-266Moraes, S.A., (1983) A ferrugem do cafeeiro: Importância, condições predisponentes, evolução e situação no Brasil, , Campinas SP. Instituto AgronômicoMoraes, S.A., Sugimori, M.H., Ribeiro, I.J.A., Ortolani, A.A., Pedro Junior, M.J., Período de incubação de Hemileia vastatrix Berk. et Br. em três regiões do Estado de São Paulo. (1976) Summa Phytopathologica, 2, pp. 32-38Paul, P.A., Munkvold, G.P., A model-based approach to preplanting risk assessment for gray leaf spot of maize (2004) Phytopathology, 94, pp. 1350-1357Pinto, A.C.S., Pozza, E.A., Souza, P.E., Pozza, A.A.A., Talamini, V., Boldini, J.M., Santos, F.S., Descrição da epidemia da ferrugem do cafeeiro com redes neuronais. (2002) Fitopatologia Brasileira, 27, pp. 517-524Quinlan JR (1993) C4.5: programs for machine learning. San Francisco CA. Morgan KaufmannSilva-Acuña, R., Zambolim, L., Cruz, C.D., Vale, F.X.R., Estudo epidemiológico da ferragem do cafeeiro (Hemileia vastatrix) utilizando a análise de trilha. (1998) Fitopatologia Brasileira, 23, pp. 425-430Sutton, J.C., Gillespie, T.J., Hildebrand, P.D., Monitoring weather factors in relation to plant disease (1984) Plant Disease, 68, pp. 78-84Witten, I.H., Frank, E., (2005) Data mining: Practical machine learning tools and techniques, , 2nd Ed. San Francisco CA. Morgan KaufmannZambolim L, Vale FXR, Pereira AA, Chaves GM (1997) Café (Coffea arabica L.): controle de doenças - doenças causadas por fungos, bactérias e vírus. In: Vale FXR, Zambolim L (Eds.) Controle de doenças de plantas: grandes culturas. 1. Viçosa MG. UFV. pp. 83-140Zambolim, L., Vale, F.X.R., Costa, H., Pereira, A.A., Chaves, G.M., (2002) Epidemiologia e controle integrado da ferrugem-do-cafeeiro, pp. 369-450. , Ed, O estado da arte de tecnologias na produção de café. Viçosa MG. UFV. p

Decision Tree For Classification Of Soybean Rust Occurence In Commercial Crops Based On Weather Variables [Árvore De Decisão Para Classificação De Ocorrências De Ferrugem Asiáticaem Lavouras Comerciais Com Base Em Variáveis Meteorológicas]

Soybean rust is the most aggressive soybean disease in Brazil. Despite its epidemiology is known, there are few studies about factors that cause it based on field data. This paper aimed to report influence of weather variables on rust occurrence using the decision tree technique. The models were developed based on disease detection dataset during harvests (2007/08 to 2010/11), temperature and rainfall variables at varied time windows prior to disease detection. For each disease "occurrence" record, a corresponding "non-occurrence" was generated based on the assumption that disease was not present at the thirtieth day prior to the report date, due to unfavorable weather conditions. The training set for modeling consisted of 45 rainfall and temperature variables and 12,591 records. The chosen predictive model resulted in a decision tree with approximately 78% of accuracy and 108 rules, determined by cross-validation. The interpreted model, with 28 rules, considered the temperature variables as more important, of which temperatures below 15°C and above 30 °C were related to events of non-occurrence, while temperatures within the favorable range have been associated with events of occurrence, showing consistency with the literature.343590599Alves, S.A.M., Furtado, G.Q., Bergamin Filho, A., Influência das condições climáticas sobre a ferrugem da soja (2006) Ferrugem asiática da soja, pp. 37-59. , ZAMBOLIM, L. (ed.).Viçosa: Universidade Federal de Viçosa, Departamento de FitopatologiaBonde, M.R., Berner, D.K., Nester, S.E., Frederick, R.D., Effects of temperature on urediniospore germination, germ tube growth, and initiation of infection in soybean by Phakopsora isolates (2007) Phytopathology, 97 (8), pp. 997-1003. , Saint PaulCollischonn, B., Allasia, D., Collischonn, W., Tucci, C.E.M., Desempenho do satélite TRMM na estimativa de precipitação sobre a bacia do Paraguai superior (2007) Revista Brasileira de Cartografia, 59 (1), pp. 93-99. , Rio de JaneiroAntiferrugem, C., (2012) Custo ferrugem asiática da soja, , http://www.consorcioantiferrugem.net/portal/?page_id=1347, Disponível em:Acesso em: setAntiferrugem, C., (2013) Ferrugem da soja, , http://www.consorcioantiferrugem.net/portal/, Disponível emDel Ponte, E.M., Esker, P.D., Meteorological factors and Asian soybean rust epidemics: A systems approach and implications for risk assessment (2008) Scientia Agricola, 65, pp. 88-97. , Piracicaba, spe, dezDel Ponte, E.M., Godoy, C.V., Canteri, M.G., Reis, E.M., Yang, X.B., Models and applications for risk assessment and prediction of Asian soybean rust epidemics (2006) Fitopatologia Brasileira, 31 (6), pp. 533-544. , BrasíliaDel Ponte, E.M., Godoy, C.V., Li, X., Yang, X.B., Predicting severity of asian soybean rust epidemics with empirical rainfall models (2006) Phytopathology, 96 (7), pp. 797-803. , Saint PaulDelgado, R.C., Sediyama, G.C., Costa, M.H., Soares, V.P., Andrade, R.G., Classificação espectral de área plantada com a cultura da cana-de-açúcar por meio da árvore de decisão (2012) Engenharia Agrícola, 32 (2), pp. p369-380. , JaboticabalDufault, N.S., Isard, S.A., Marois, J.J., Wright, D.L., Removal of wet deposited Phakopsora pachyrhizi urediniospores from soybean leaves by subsequent rainfall (2010) Plant Disease, 94 (11), pp. p1336-1340. , St. PaulGodoy, C.V., Flausino, A.M., Santos, L.C.M., Del Ponte, E.M., Eficiência do controle da ferrugem asiática da soja em função do momento de aplicação sob condições de epidemia em Londrina - PR. (2009) Tropical Plant Pathology, 34 (1), pp. p56-61. , BrasíliaHan, J., Kamber, M., Pei, J., (2011) Data mining: concepts and techniques, , 3rded. San Francisco: Morgan Kaufmann PublishersKochman, J.K., The effect of temperature on development of soybean rust (Phakopsora pachyrhizi) (1979) Australian Journal of Agricultural Research, 30, pp. 273-277. , VictoriaMarchetti, M.A., Melching, J.S., Bromfield, K.R., The effects of temperature and dew period on germination and infection by uredospores of Phakopsora pachyrhizi (1976) Phytopathology, 66, pp. 461-463. , Saint PaulMeira, C.A.A., Rodrigues, L.H.A., Moraes, S.A., Análise da epidemia da ferrugem do cafeeiro com árvore de decisão (2008) Tropical Plant Pathology, 33 (2), pp. 114-124. , BrasíliaMeira, C.A.A., Rodrigues, L.H.A., Moraes, S.A.D., Modelos de alerta para o controle da ferrugem-do-cafeeiro em lavouras com alta carga pendente (2009) Pesquisa Agropecuária Brasileira, Brasília, 44 (3), pp. 233-242. , marPinto, L.I.C., Costa, M.H., De Lima, F.Z., Comparação de produtos de precipitação para a América do Sul. Revista Brasileira de Meteorologia (2009) São José dos Campos, 24 (4), pp. 461-472Quinlan, J.R., C4 5: Programs for machine learning (1993), San Francisco: Morgan KaufmannRomani, L.A.S., Otavian, A.F., Evangelista, S.R.M., Assad, E.D., Modelo de estações virtuais com estimativa de precipitação e temperatura para aprimoramento dos mapas no Agritempo (2007) CONGRESSO BRASILEIRO DE AGROMETEOROLOGIA, p. 15. , Aracaju. AnaisTan, P.N., Steinbach, M., Kumar, V., (2009) Introdução ao data mining, p. 932. , Mineração de dados. Rio de Janeiro: Ciência ModernaYorinori, J.T., Paiva, W.M., Frederick, R.D., Costamilan, L.M., Epidemics of soybean rust (Phakopsora pachyrhizi) in Brazil and Paraguay from 2001 to 2003 (2005) Plant Disease, 89 (6), pp. 675-677. , jun. Saint PaulWitten, I.H., Frank, E., Hall, M.A., (2011) Data mining: practical machine learning tools and techniques, , 3rded. San Francisco: Morgan Kaufman

The Use Of Fuzzy Decision Trees For Coffee Rust Warning In Brazilian Crops

This paper proposes the use of fuzzy decision trees for coffee rust warning, the most economically important coffee disease in the world. The models were induced using field data collected during 8 years. Using different subsets of attributes from the original data, three distinct datasets were constructed. The class attribute, representing the monthly infection rate, was used to construct six datasets according to two distinct infection rates. Induced models can be used to trigger alerts when estimated monthly disease infection rates reach one of the two thresholds. The first threshold allows applying preventive actions, whereas the second one requires a curative action. The fuzzy decision tree models were compared to the ones induced by a classic decision tree algorithm, taking into account the accuracy and the syntactic complexity of the models, as well as its quality according to an expert opinion. The fuzzy models showed better accuracy power and interpretability. © 2011 IEEE.13471352Machine Intelligence Research Labs (MIR Labs),University of Cordoba,Ministry of Science and Innovation of SpainZambolim, L., Vale, F.X.R., Pereira, A.A., Chaves, G.M., Coffee (Coffea arabica L.): Diseases control - Diseases caused by fungi, bacterium, viruses (1997) Plant Diseases Control: Large Cultures, 1. , F. X. R. Vale and L. Zambolim, Eds. Federal University of Varginha - UFV, in PortugueseZambolim, L., Vale, F.X.R., Costa, H., Pereira, A.A., Chaves, G.M., The state-of-the-art of the coffee production technologies (2002) Epidemiology and Integrated Control of the Coffee Rust, , L. Zambolim, Ed. Suprema, in PortugueseKushalappa, A.C., Akutsu, M., Ludwig, A., Application of survival ratio for monocyclic process of Hemileia vastatrix in predicting coffee rust infection rates (1983) Phytopathology, 73, pp. 96-103Kushalappa, A.C., Akutsu, M., Oseguera, S.H., Chaves, G.M., Melles, C., Equations for predicting the rate of coffee rust development based on net survival ratio for monocyclic process of Hemileia vastatrix (1984) Fitopatologia Brasileira, 9, pp. 255-271Pinto, A.C.S., Pozza, E.A., Souza, P.E., Pozza, A.A.A., Talamini, V., Boldini, J.M., Santos, F.S., Description of epidemics of coffee rust with neural network (2002) Fitopatologia Brasileira, 27, pp. 517-524. , in PortugueseGarcon, C.L.P., Zambolim, L., Mizubuti, E.S.G., Vale, F.X.R., Costa, H., Coffee leaf rust control based on rust severity values (2004) Fitopatologia Brasileira, 29, pp. 486-491. , in PortugueseButt, D.J., Royle, D.J., Multiple regression analysis in the epidemiology of plant diseases (1990) Epidemics of Plant Diseases: Mathematical Analysis and Modeling, , 2nd ed., J. Kranz, Ed. Springer-VerlagPaul, P.A., Munkvold, G.P., A model-based approach to preplanting risk assessment for gray leaf spot of maize (2004) Phytopathology, 94, pp. 1350-1357Baker, F.A., Verbyla, D.L., Hodges, C.S., Ross, E.W., Classification and regression tree analysis for assessing hazard of pine mortality caused by Heterobasidion annosum (1993) Plant Disease, 77, pp. 136-139Meira, C.A.A., Rodrigues, L.H.A., Moraes, S.A., Analysis of coffee leaf rust epidemics with decision tree (2008) Tropical Plant Pathology, 33, pp. 114-124. , in PortugueseMeira, C.A.A., Rodrigues, L.H.A., Moraes, S.A., Warning models for coffee rust control in growing areas with large fruit load (2009) Pesquisa Agropecuaria Brasileira, 44, pp. 233-242. , in PortugueseZadeh, L., Fuzzy sets (1965) Information and Control, 8, pp. 338-353Cintra, M.E., Monard, M.C., Camargo, H.A., An evaluation of rule-based classification models induced by a fuzzy method and two classic learning algorithms (2010) IEEE - The Brazilian Symposium on Artificial Neural Network (SBRN), 1, pp. 188-193Quinlan, J.R., C4.5: Programs for Machine Learning (1993) Morgan Kaufmann Series in Machine Learning, , 1st ed. Morgan Kaufmann, JanuaryJapiassu, L.B., Garcia, A.W.R., Miguel, A.E., Carvalho, C.H.S., Ferreira, R.A., Padilha, L., Matiello, J.B., Effect of crop load, tree density and weather conditions on the development of the coffee leaf rust Simpósio de Pesquisa Dos Cafés Do Brasil, 2007, , 5° editionChalfoun, S.M., (1997) Coffee Tree Diseases: Importance, Identification and Control Methods, , Federal University of Lavras, Brazil, FAEPE (Foundation for the Support of Teaching, Researching and Extension), in PortugueseMeira, C.A.A., (2008) Process of Knowledge Discovery in Databases for Analysis and Warning of Crop Diseases and Its Application on Coffee Rust, , Ph.D. dissertation, State University of Campinas, Brazil, in PortugueseWitten, I.H., Frank, E., Hall, M.A., (2011) Data Mining: Practical Machine Learning Tools and Techniques, , 3rd ed. Morgan KaufmannPrati, R.C., Monard, M.C., Carvallho, A.C.P.L.F., Looking for exceptions on knowledge rules induced from HIV cleavage data set (2004) Genetics and Molecular Biology, 27 (4), pp. 637-643Demsar, J., Statistical comparisons of classifiers over multiple data sets (2006) Journal of Machine Learning Research, 7, pp. 1-30. , http://jmlr.csail.mit.edu/papers/volume7/demsar06a/demsar06a.pdfWilcoxon, F., Individual comparisons by ranking methods (1945) Biometrics Bulletin, 1, pp. 80-8

Progresso da ferrugem do cafeeiro irrigado em diferentes densidades de plantio pós-poda Progress of rust in coffee plants in various densities of cultivation in irrigated planting after pruning

Objetivou-se, no presente trabalho, avaliar o efeito de diferentes critérios para manejo da irrigação em quatro densidades de plantio, sob sistema de gotejamento na incidência e severidade da ferrugem do cafeeiro e avaliar a influência do enfolhamento na curva de progresso dessa doença. Conduziu-se, o experimento, em área experimental da Universidade Federal de Lavras MG, utilizando a cultivar Rubi MG-1192 com seis anos. O delineamento experimental foi em blocos ao acaso com quatro repetições. Os tratamentos foram constituídos por quatro parcelas representadas pelas densidades de plantio (convencionais e adensados): 2500 (4,0x1,0 m), 3333 (3,0x1,0 m), 5000 (2,0x1,0 m), 10000 (2,0x0,5 m) plantas ha-1, quatro subparcelas sendo: irrigações quando a tensão da água no solo atingiu valores de 20 e 60kPa; irrigações utilizando o manejo do balanço hídrico (calculado através do software IRRIPLUS), com turnos de irrigação fixos de três dias por semana e uma testemunha sem irrigação, perfazendo um total de 16 tratamentos. Cada subparcela foi constituída por 10 plantas, sendo consideradas como plantas úteis as seis centrais. Foram avaliadas a incidência e severidade da ferrugem e a porcentagem de enfolhamento das plantas de cafeeiros. Após análise estatística, os dados foram convertidos em área abaixo da curva de progresso da doença e do crescimento. Verificou-se que os critérios para manejo da irrigação influenciaram a curva de progresso do crescimento, porém, não interferiu na curva de progresso da incidência e da severidade da ferrugem. Os sistemas de plantios adensados favoreceram a incidência da ferrugem. Mas as densidades de plantio não interferiram no enfolhamento.<br>The objective of this study was to evaluate the effect of different irrigation controls implemented in four planting densities on a system of drip on the incidence and severity of rust and to assess the influence of leaf growth on the progress curve of this disease. The experiment was conducted in the experimental area of the Federal University of Lavras - MG, using the cultivar Rubi MG-1192 with 6 years. The experimental design was randomized blocks with four replicates. The treatments consisted of four tranches represented by planting densities (conventional and non-conventional): 2500 (4.0 x1.0 m), 3333 (3.0 x 1.0 m), 5000 (2.0 x 1.0 m), 10,000 (2.0 x 0.5 m) plants / ha, with four subplots: the tension when irrigation water in the soil reaches values of 20 and 60kPa; using water balance management of irrigation (calculated by the software IRRIPLUS) with rounds of irrigation fixed three days per week and a control without irrigation, making a total of 16 treatments. Each subplot line consisted of 10 plants with the six central plants considered as useful. The incidence and severity of rust and the percentage of grown of coffee plants were evaluated. After statistical analyses the data were converted to area under the curve of disease and growth progress. It was verified that the management of irrigation influenced the progress curve of growth, but it did not interfere in the progress curve of the incidence and severity of coffee rust. The dense system of plantation favored the incidence of rust. However, the planting densities did not affect growth