ABSTRACT: Knowing the spatial variability of sugarcane biomass in the early stages of development may help growers in their management decision-making. Proximal canopy sensing is a promising technology that can identify this variability but is limited to quantifying plant-specific parameters. In this study, we evaluated whether biometric variables integrated with canopy reflectance data can assist in the generation of models for early-stage sugarcane biomass prediction. To substantiate this assertion, four sugarcane-producing fields were measured with an active crop canopy sensor and 30 sampling plots were selected for manually quantifying chlorophyll content, plant height, stalk number and aboveground biomass. We determined that Random Forest and Multiple Linear Regression models are similarly able to predict biomass, and that associating biometric variables such as number of stalks and plant height with reflectance data can assist model performance, depending on the attributes selected. This indicates that, when estimating biomass in the early stages, sugarcane growers can carry out site-specific management in order to increase yield and reduce the use of inputs.

ABSTRACT: The Random Forest algorithm is a data mining technique used for classifying attributes in order of importance to explain the variation in an attribute-target, as soil CO2 flux. This study aimed to identify prediction of soil CO2 flux variables in management systems of sugarcane through the machine-learning algorithm called Random Forest. Two different management areas of sugarcane in the state of São Paulo, Brazil, were selected: burned and green. In each area, we assembled a sampling grid with 81 georeferenced points to assess soil CO2 flux through automated portable soil gas chamber with measuring spectroscopy in the infrared during the dry season of 2011 and the rainy season of 2012. In addition, we sampled the soil to evaluate physical, chemical, and microbiological attributes. For data interpretation, we used the Random Forest algorithm, based on the combination of predicted decision trees (machine learning algorithms) in which every tree depends on the values of a random vector sampled independently with the same distribution to all the trees of the forest. The results indicated that clay content in the soil was the most important attribute to explain the CO2 flux in the areas studied during the evaluated period. The use of the Random Forest algorithm originated a model with a good fit (R2 = 0.80) for predicted and observed values.

Sistemas de informações tecnológicas exercem um papel fundamental na agricultura, pois proveem informações de forma rápida e eficiente, dando suporte a decisões estratégicas. No entanto, o excesso de informação disponível pode confundir e dificultar o acesso à informação desejada. Uma alternativa para amenizar esse problema é a adoção de sistemas que ofereçam recomendações automáticas de acordo com o perfil de uma comunidade de usuários. O objetivo deste trabalho foi desenvolver um sistema de recomendação, concebido a partir de técnicas de mineração de dados, para conteúdos relacionados à cultura da cana-de-açúcar. O modelo adotado foi o de listas de recomendação, que são regras de associação entre as páginas web visitadas, produzidas a partir dos dados de navegação dos usuários. O sistema foi implantado no portal da Agência de Informação Embrapa, que é um sistema web, com o objetivo de organizar, tratar, armazenar e divulgar informações técnicas agrícolas. Dentre os resultados alcançados com a implantação do sistema, destaca-se uma base de conhecimento na forma de regras de associação que descreve o comportamento de uma comunidade de usuários e pode ser utilizada para indicar os links mais importantes em páginas web relacionadas à cultura da cana-de-açúcar. Com a adoção desse sistema de recomendação, os principais benefícios para os usuários da agência são: a) acesso a informações detalhadas de etapas de produção, com indicações de material de referência e fontes diferentes de estatísticas confiáveis; b) acesso a informações que favorecem a produção eficiente em termos técnicos, ambientais, sociais e econômicos; c) apoio a agentes públicos e privados no planejamento e tomada de decisão; d) transferência de conhecimento atualizado, em linguagem amigável, para o setor sucroenergético.

Information technology systems play an important role in agriculture, since they provide information in a quick and efficient way to support strategic decisions. However, the excess of information available may confuse and hinder the access to specific information. An alternative is the adoption of systems that provide automatic recommendations according to the profile of a user community. This work aimed to develop a recommender system based on data mining techniques to recommend content regarding the sugarcane crop. The adopted recommendation model relies on recommendation lists that are association rules between web pages, produced from the data users' browsing. The system was deployed on the portal of Embrapa Information Agency, which is a web system that aims to organize process, store and disseminate agricultural technological information. Among the results achieved with the system, it can be highlighted a knowledge base built with association rules, that describes the behavior of a user community and can indicate the most important links in web pages concerning sugarcane. With the adoption of this recommender system, the main benefits to users are: a) detailed information of production stages, with indications of reference material and different sources of reliable statistics; b) information that favors the efficient production considering technical, environmental, social and economic aspects; c) support to public and private stakeholders in planning and decision making; d) transfer of current knowledge with a friendly language for the sugarcane industry.