Luiz Henrique Antunes Rodrigues

Análise da epidemia da ferrugem do cafeeiro com árvore de decisão

Uma arvore de decisao foi desenvolvida com o objetivo de auxiliar na compreensao de manifestacoes epidemicas da ferrugem do cafeeiro causada por Hemileia vastatrix. Taxas de infeccao calculadas a partir de avaliacoes mensais de incidencia da ferrugem foram agrupadas em tres classes: reducao ou estagnacao - TX1; crescimento moderado (ate 5p.p.) - TX2; e crescimento acelerado (acima de 5p.p.)- TX3. Dados meteorologicos, carga pendente de frutos do cafeeiro (Coffea arabica) e espacamento entre plantas foram usados como variaveis explicativas das classes de taxa de infeccao. A arvore de decisao foi treinada com 364 exemplos preparados a partir de dados coletados em lavouras de cafe em producao, de outubro de 1998 a outubro de 2006. Ela classificou corretamente 78% do conjunto de treinamento e a sua acuracia foi estimada em 73% para a classificacao de novos exemplos. O acerto do modelo foi de 88%, 57% e 79% dos exemplos, respectivamente, para as classes de taxa de infeccao TX1, TX2 e TX3. As variaveis explicativas mais importantes foram a temperatura media nos periodos de molhamento foliar, a carga pendente de frutos, a media das temperaturas maximas diarias no periodo de incubacao e a umidade relativa do ar. A arvore de decisao demonstrou seu potencial como modelo de representacao simbolica e interpretavel, permitindo a identificacao das fronteiras de decisao existentes nos dados e da logica contida neles, auxiliando na compreensao de quais variaveis e como as interacoes dessas variaveis conduziram as epidemias da ferrugem do cafeeiro no campo.

Índice fuzzy de conforto térmico para frangos de corte

Estimating thermal comfort in modern poultry production is important that acclimatization systems can be triggered at appropriate time reducing losses and increasing yield. Although current literature presents some thermal comfort indexes which are applied for this estimation those are based just on ambient thermal conditions and do not consider important factors inherent to the animals such as genetics and capability of adaptation, generally providing an inadequate estimation of the birds’ thermal comfort. This research developed the Fuzzy thermal comfort index (FTCI) aiming to estimate broilers’ thermal comfort considering that the mechanism used by the birds for losing heat in environments outside the thermoneutral zone is the peripheral vasodilatation, which increases the surface temperature. Measurements of surface feathers and skin temperature of birds were used. The FTCI was developed using the data of two experiments which provided 108 distinct environmental scenarios. Infrared thermal images were used for registering surface temperature of feathers and skin, as well as the birds’ feathering degree. For the same scenarios of thermal environment both FTCI and the temperature and humidity index (THI) were compared. Results validated the FTCI for estimating broilers’ thermal comfort, being specific for the estimation of danger conditions usually found in housing in tropical climate countries. This characteristic is advantageous in models which estimate broiler thermal welfare, as occurrence classified as dangerous may lead to economical downward in avoiding productive losses.

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.

Modelos de alerta para o controle da ferrugem-do-cafeeiro em lavouras com alta carga pendente

The objective of this work was to develop decision trees as warning models of coffee (Coffea arabica L.) rust in growing areas with large fruit load. Monthly data of disease incidence in the field collected during eight years were transformed into binary values considering limits of 5 and 10 percentage points in the infection rate. Models were generated from meteorological data and space between plants for each binary infection rate. The warning is indicated when the infection rate is expected to reach or exceed the respective limit in a month. The accuracy obtained by cross-validating the model to the limit of 5 percentage points was 81%, reaching up to 89% according to an optimistic estimate. This model showed good results for other important evaluation measures, such as sensitivity (80%), specificity (83%), positive reliability (79%), and negative reliability (84%). The model for the limit of 10 percentage points had a 79% accuracy and did not show the same balance among the other evaluation measures. Together, these two models may support the decisions about coffee rust control in the field. The decision tree induction is a viable alternative to conventional modeling techniques, thus facilitating the comprehension of the models.

Mineração de dados e estimativa da mortalidade alta de frangos quando expostos a onda de calor

Heat waves usually result in losses of animal production since they are exposed to thermal stress inducing an increase in mortality and consequent economical losses. Animal science and meteorological databases from the last years contain enough data in the poultry production business to allow the modeling of mortality losses due to heat wave incidence. This research analyzes a database of broiler production associated to climatic data, using data mining techniques such as attribute selection and data classification (decision tree) to model the impact of heat wave incidence on broiler mortality. The temperature and humidity index (THI) was used for screening environmental data. The data mining techniques allowed the development of three comprehensible models for estimating specifically high mortality during broiler production. Two models yielded a classification accuracy of 89.3% by using Principal Component Analysis (PCA) and Wrapper feature selection approaches. Both models obtained a class precision of 0.83 for classifying high mortality. When the feature selection was made by the domain experts, the model accuracy reached 85.7%, while the class precision of high mortality was 0.76. Meteorological data and the calculated THI from meteorological stations were helpful to select the range of harmful environmental conditions for broilers 29 and 42 days old. The data mining techniques were useful for building animal production models.

Análise dos atributos do solo e da produtividade da cultura de cana-de-açúcar com o uso da geoestatística e árvore de decisão

One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane.

Using nondeterministic learners to alert on coffee rust disease

Motivated by an agriculture case study, we discuss how to learn functions able to predict whether the value of a continuous target variable will be greater than a given threshold. In the application studied, the aim was to alert on high incidences of coffee rust, the main coffee crop disease in the world. The objective is to use chemical prevention of the disease only when necessary in order to obtain healthier quality products and reductions in costs and environmental impact. In this context, the costs of misclassifications are not symmetrical: false negative predictions may lead to the loss of coffee crops. The baseline approach for this problem is to learn a regressor from the variables that records the factors affecting the appearance and growth of the disease. However, the number of errors is too high to obtain a reliable alarm system. The approaches explored here try to learn hypotheses whose predictions are allowed to return intervals rather than single points. Thus, in addition to alarms and non-alarms, these predictors identify situations with uncertain classification, which we call warnings. We present three different implementations: one based on regression, and two more based on classifiers. These methods are compared using a framework where the costs of false negatives are higher than that of false positives, and both are higher than the cost of warning predictions.

Viability of an alarm predictor for coffee rust disease using interval regression

We present a method to formulate predictions regarding continuous variables using regressors able to predict intervals rather than single points. They can be learned explicitly using the so-called insensitive zone of regression Support Vector Machines (SVM). The motivation for this research is the study of a real case; we discuss the feasibility of an alarm system for coffee rust, the main coffee crop disease in the world. The objective is to predict whether the percentage of infected coffee leaves (the incidence of the disease) will be above a given threshold. The requirements of such a system include avoiding false negatives, seeing as these would lead to not preventing the disease. The aim of reliable predictions, on the other hand, is to use chemical prevention of the disease only when necessary in order to obtain healthier products and reductions in costs and environmental impact. Although the breadth of the predicted intervals improves the reliability of predictions, it also increases the number of uncertain situations, i.e. those whose predictions include incidences both below and above the threshold. These cases would require deeper analysis. Our conclusion is that it is possible to reach a trade-off that makes the implementation of an alarm system for coffee rust disease feasible.

Sistema de suporte à decisão para recomendação de uso e manejo da terra

The objective of this work was to develop a decision support system to recommend land use and practices for soil conservation and management, which integrates an Expert System, Geographic Information System (GIS), a database and an interface to monitor input and output data and the messages passed between the subsystems. Using soil and slope maps, the developed system determines land capability from information stored in the database and also supplied by SIG; it recommends adequate uses for land capability class as well as practices for soil conservation and management and identifies conflict areas comparing the maps of land use with the land capability. All results can be visualized by the user through windows of the program, recorded or printed in form of report. In order to test the system, the municipality of Santo Antonio do Jardim, in the State of Sao Paulo, Brazil, was selected. The system is a powerful and efficient tool, permitting the evaluation of a region and thereby offering support for adequate decision making.

The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling

Data-mining techniques were applied to data from sugarcane production.The impact of different approaches to include weather data was evaluated.The RReliefF algorithm is used to evaluate feature engineering.We evaluated the impact of tuning, feature selection, and feature engineering in error.Sixty-six combinations were evaluated to quantify the impacts on model performance. Crop yield models can assist decision makers within any agro-industrial supply chain, even with regard to decisions that are unrelated to the crop production. Considering the characteristics of the mechanisms and data related to yield, data mining techniques are suitable candidates for modelling. The use of these techniques within a context with feature engineering, feature selection, and proper tuning can further improve performance beyond a simple replacement of multiple linear regression. To evaluate the impact of the different steps in the mentioned context, we evaluated sugarcane (Saccharum spp.) yield modelling with data obtained from a sugarcane mill. For a combination of six techniques, tuning, feature selection, and feature engineering, leading to 66 combinations, we assessed final model performance. Average performance across combinations resulted in a mean absolute error (MAE) of 6.42Mgha-1. Using different techniques led to a range of MAE from 4.57 to 8.80Mgha-1 on average. The best and worst performances for an individual model were MAEs of 4.11 and 9.00Mgha-1. Models with lower performance were close to simply predicting yield from the average yield for each number of cuts (MAE of 9.86Mgha-1). Tuning and feature engineering reduced the MAE on average by 1.17 and 0.64Mgha-1, respectively. Feature selection removed nearly 40% of the features but increased the MAE by 0.19Mgha-1. The performance of models was improved by simple strategies such as decomposing weather attributes and detailing fertilisation. Evaluation of feature importance provided by the RReliefF feature selection algorithm was used to explain the performance gains. If empirical models are needed, they will rely on using advanced techniques, but they will need proper algorithm tuning and feature engineering to extract most of the information from datasets. Based on the results, we recommend following the presented workflow for the development of yield models.