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

Manual de utilização da plataforma SomosIPSS

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TheoFrameAccountability: apresentação de resultados: seminário final

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TheoFrameAccountability: relatório final

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The inventory of geological heritage of the state of São Paulo, Brazil: Methodological basis, results and perspectives

An inventory of geological sites based on solid and clear criteria is a first step for any geoconservation strategy. This paper describes the method used in the geoheritage inventory of the State of São Paulo, Brazil, and presents its main results. This inventory developed by the geoscientific community aimed to identify geosites with scientific value in the whole state, using a systematic approach. All 142 geosites representative of 11 geological frameworks were characterised and quantitatively evaluated according to their scientific value and risk of degradation, in order to establish priorities for their future management. An online database of the inventory is under construction, which will be available to be easily consulted and updated by the geoscientific community. All data were made available to the State Geological Institute as the backbone for the implementation of a future state geoconservation strategy.The authors acknowledge the Science Without Borders Programme, Process 075/2012, which supported this study and the São Paulo Research Foundation (FAPESP), Process 2011/17261-6. We also thanks C. Mazoca for his help with maps and figures.info:eu-repo/semantics/acceptedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio