Identification of patterns for increasing production with decision trees in sugarcane mill data

Sugarcane mills in Brazil collect a vast amount of data relating to production on an annual basis. The analysis of this type of database is complex, especially when factors relating to varieties, climate, detailed management techniques, and edaphic conditions are taken into account. The aim of this paper was to perform a decision tree analysis of a detailed database from a production unit and to evaluate the actionable patterns found in terms of their usefulness for increasing production. The decision tree revealed interpretable patterns relating to sugarcane yield (R2 = 0.617), certain of which were actionable and had been previously studied and reported in the literature. Based on two actionable patterns relating to soil chemistry, intervention which will increase production by almost 2 % were suitable for recommendation. The method was successful in reproducing the knowledge of experts of the factors which influence sugarcane yield, and the decision trees can support the decision-making process in the context of production and the formulation of hypotheses for specific experiments

When do I want to know and why? Different demands on sugarcane yield predictions

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOThe production planning processes of sugarcane mills require quantitative information to support decisions on sugarcane yield and the effects of decisions made during planning. An exploratory study was conducted at a sugarcane mill with the goals of identifying the main decisions influenced by the prospects of future yield and of evaluating the manner in which those forecasts affect planning. Key decisions and their characteristics were identified based on a series of interviews and activity monitoring. These decisions are presented and discussed in relation to various solutions proposed by the scientific community for planning, as well as within the concept of Advanced Planning Systems. The yield forecasts used to inform budgeting and harvesting plans are of critical importance because actions taken based on those forecasts affect the entire value chain, highlighting the need for a decision-making framework that assess the effects of decisions on subsequent processes. Advanced Planning Systems design to the sugar value chain should incorporate the use of yield forecasts for production and must address the uncertainties throughout the entire system. These improvements can enhance the performances of Advanced Planning Systems by producing an integrated planning approach that is based on a comprehensive assessment of the sugar value chain. (C) 2014 Elsevier Ltd. All rights reserved.The production planning processes of sugarcane mills require quantitative information to support decisions on sugarcane yield and the effects of decisions made during planning. An exploratory study was conducted at a sugarcane mill with the goals of ident1354856FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP [12/50049-3]12/50049-

Representation of harmonic cycles of Modis time series for the analysis of sugarcane cultivation

The objective of this work was to evaluate sugarcane cultivation, in a harmonic analysis applied to a time series of Modis vegetation indices, with the representation of harmonic terms. Daily rainfall data were obtained from Agritempo for the state of São Paulo, Brazil, and accumulated for a period of 16 days of Modis compositions, from the 2004/2005 to 2011/2012 crop seasons. The normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) were used in time-series decomposed in harmonic terms by the harmonic analysis. In order to visualize the growing conditions of vegetation in agricultural areas, specially the phase information, the HLS transformation was applied to the harmonic terms obtained by the Hants algorithm, using Envi software. Sugarcane cultivation in the state of São Paulo shows spatial patterns that are coherent with the sugarcane development cycle and consistent with the variability of seasonal rainfall that directly affect the maximum period of vegetation indices. The peak growth stage of sugarcane occurs in years of normal rainfall; however, in years with below normal rainfall, sugarcane maturation phase is anticipated, and, in years with above normal rainfall, the growth phase is anticipated, which causes maturation delay

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 3 different implementations: one based on regression, and 2 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 prediction

Warning models for coffee rust control in growing areas with large fruit load

O objetivo deste trabalho foi desenvolver árvores de decisão como modelos de alerta da ferrugem-do-cafeeiro em lavouras de café (Coffea arabica L.) com alta carga pendente de frutos. Dados de incidência mensal da doença no campo coletados durante oito anos foram transformados em valores binários considerando limites de 5 e 10 pontos percentuais na taxa de infecção. Foi gerado um modelo para cada taxa de infecção binária a partir de dados meteorológicos e do espaçamento entre plantas. O alerta é indicado quando a taxa de infecção, prevista para o prazo de um mês, atingir ou ultrapassar o respectivo limite. A acurácia do modelo para o limite de 5 pontos percentuais foi de 81%, por validação cruzada, chegando até 89% segundo estimativa otimista. Esse modelo apresentou bons resultados para outras medidas de avaliação importantes, como sensitividade (80%), especificidade (83%) e confiabilidades positiva (79%) e negativa (84%). O modelo para o limite de 10 pontos percentuais teve acurácia de 79%, e não apresentou o mesmo equilíbrio entre as demais medidas. Em conjunto, esses modelos podem auxiliar na tomada de decisão referente ao controle da ferrugem-do-cafeeiro no campo. A indução de árvores de decisão é alternativa viável às técnicas convencionais de modelagem e facilita a compreensão dos modelos.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 fi eld 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%), specifi city (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 fi eld. The decision tree induction is a viable alternative to conventional modeling techniques, thus facilitating the comprehension of the models

Warning models for coffee rust control in growing areas with large fruit load

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.O objetivo deste trabalho foi desenvolver árvores de decisão como modelos de alerta da ferrugem-do-cafeeiro em lavouras de café (Coffea arabica L.) com alta carga pendente de frutos. Dados de incidência mensal da doença no campo coletados durante oito anos foram transformados em valores binários considerando limites de 5 e 10 pontos percentuais na taxa de infecção. Foi gerado um modelo para cada taxa de infecção binária a partir de dados meteorológicos e do espaçamento entre plantas. O alerta é indicado quando a taxa de infecção, prevista para o prazo de um mês, atingir ou ultrapassar o respectivo limite. A acurácia do modelo para o limite de 5 pontos percentuais foi de 81%, por validação cruzada, chegando até 89% segundo estimativa otimista. Esse modelo apresentou bons resultados para outras medidas de avaliação importantes, como sensitividade (80%), especificidade (83%) e confiabilidades positiva (79%) e negativa (84%). O modelo para o limite de 10 pontos percentuais teve acurácia de 79%, e não apresentou o mesmo equilíbrio entre as demais medidas. Em conjunto, esses modelos podem auxiliar na tomada de decisão referente ao controle da ferrugem-do-cafeeiro no campo. A indução de árvores de decisão é alternativa viável às técnicas convencionais de modelagem e facilita a compreensão dos modelos.23324

Kalman Filters in crop models: old experiences in new contexts

Data assimilation has been widely used for improvement of crop models’ estimates, for example to incorporate the effects of external events or compensate calibration errors in large areas. The term describes multiple approaches for those who want to take advantage of satellite imagery to reduce uncertainty or improve accuracy of model estimates. Kalman Filters are among the most used methods for achieving these goals. But their use in new contexts, i.e., from open field to protected environments, requires untangling aspects of the pipeline that are often performed in many different ways without guidelines, such as which variables to assimilate or how to ascribe uncertainty to observations or model estimates. This study is then divided in two parts. In the first, we review details on how uncertainty is ascribed on crop model estimates and in observations for applications of the Kalman Filter and three variations of the method, i.e., the Extended, Unscented and Ensemble, as well as which state variables are often updated and the frequency with which assimilation may occur, as well as how these aspects are connected to each other. In the second part, we apply different approaches from the reviewed literature in a greenhouse tomato crop model. We use artificial data with controlled noise levels as well as artificial data generated by simulation using other tomato crop model. We assess the impacts of using different methods and different approaches for ascribing uncertainty in model estimates and in observations, by assimilating artificial observations of fruit and of mature fruit biomass. We note that covariances should not be fixed values, that there are trade-offs between ascribing model uncertainty to the state itself and to other elements of the process, that observation covariance may have been considered disproportionality higher when using some ensemble generation approaches in the EnKF, and that bias in model estimates may lead to worse outcomes even when observations are high-quality ones. While we discussed aspects that should be considered in a new environment, many of them are also important for field crops, and we concluded assimilation should follow an assessment of which variables could be useful for assimilation

ANALYSIS OF THE VEGETATION PHENOLOGY FROM THE ALTO PARAGUAI BASIN THROUGHT THE REPRESENTATION OF HARMONIC CYCLES OF EVI/MODIS TIME-SERIES

The Alto Paraguai Basin (BAP) is of strategic importance for Brazil, due to its ecological diversity of landscape, especially because it includes the Pantanal floodplain. The harmonic analysis can be used in remote sensing time-series data to study the cyclic behavior of vegetation indices. The visual representation of harmonic terms can help image interpretation through the combination of colors in the HLS (Hue, Lightness, Saturation) space which provides a soft visual transition effect between the cycles. The objective of this study was to analyze the vegetation phenology of the BAP using the harmonic analysis applied to an EVI (Enhanced Vegetation Index) time-series data from MODIS (Moderate Resolution Imaging Spectroradiometer) during 10 hydrologic years from October 2001 to September 2011, considering the HLS representation of the harmonic terms. The results show that the vegetation phenology of BAP presents spatial patterns coherent with the vegetation development and consistent with the variability of the seasonal inundations in Pantanal, which determines the hydrologic conditions of the region, directly affecting the moment of maximum EVI. The HLS representation of harmonic terms indicates that it is an effective tool for the visual interpretation of vegetation cycle

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

As ondas de calor provocam perdas na produção animal pela sua exposição ao estresse por calor aumentando a mortalidade, e consequentemente, perdas econômicas. Bancos de dados zootécnicos e meteorológicos históricos podem conter informações que permitem modelar a mortalidade de frangos devido à incidência de ondas de calor. O objetivo foi analisar bancos de dados de frangos de corte associados a dados meteorológicos utilizando técnicas de mineração de dados, seleção de atributos e classificação (árvore de decisão) para modelar o impacto da incidência de onda de calor na mortalidade de frangos de corte. O Índice de Temperatura e Umidade (ITU) foi utilizado para descrever parte dos dados ambientais. A técnica de Mineração de Dados permitiu a construção de três modelos compreensíveis para estimar a alta mortalidade em frangos de corte. Os modelos gerados pela abordagem de seleção de atributos por Análise dos Componentes Principais e Wrapper apresentaram igual desempenho com uma precisão total de 89,3% e a classificação para alta mortalidade foi de 83,3%. Quando a seleção foi feita por especialistas do domínio, a precisão do modelo foi de 85,7%, e a da classificação para alta mortalidade foi de 76,9%. Resultados meteorológicos e o ITU calculada a partir de estações meteorológicas permitiram identificar condições ambientais prejudiciais para frangos entre 29 e 42 dias de vida. A técnica de Mineração de Dados é aplicável para construir modelos preditivos para a produção animal.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

CRESCIMENTO DE PLANTAS JOVENS DE PEQUIZEIRO IRRIGADAS NA REGIÃO DO CERRADO

Dentre todas as espécies nativas apontadas como economicamente viáveis para o Cerrado, o pequizeiro apresenta um dos maiores potenciais. Entretanto, o cultivo desta planta em grande escala em sistema de monocultivo no Cerrado parece ainda arriscado e insustentável, devido a falta de informações técnicas de cultivo. Assim, estudos que buscam viabilidade técnica desta cultura, são importantes dada a grande variedade de usos do pequizeiro. Desta forma, o presente trabalho objetivou avaliar o crescimento de plantas de pequi, cultivadas sob a irrigação e sem irrigação, nos 2 primeiros anos de cultivo na região do Cerrado. O pomar foi estabelecido com 120 mudas de pequizeiro, espaçadas 5 x 5 m, transplantadas em covas no campo em janeiro de 2009, e o estudo foi conduzido até  Novembro de 2010. O delineamento experimental utilizado foi o de blocos ao acaso, com dois tratamentos (T1: irrigado e T2: não irrigado) e seis repetições. Utilizou-se o sistema de irrigação por microaspersão, sendo a quantidade de água aplicada, em cada irrigação, estimada com base na evapotranspiração de cultura obtida pelo método de Penman Monteith. O crescimento das plantas foi avaliado mensalmente, com base nas seguintes variáveis biométricas: altura de planta; comprimento do ramo principal; diâmetro do caule e número de ramos. As  plantas de pequi apresentam elevado grau de adaptação às condições edafoclimáticas do Cerrado brasileiro, em que o crescimento das plantas não são influenciadas pelo uso da irrigação, quando comparadas ao cultivo sem irrigação