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A Organização das Nações Unidas para Alimentação e Agricultura (FAO) estima um crescimento populacional que chega a 11.2 bilhões até ano de 2100, o que sem dúvida contribuirá para o crescimento da demanda por produtos agrícolas alimentares e não alimentares, tornando a otimização de recursos hídricos indispensável. Neste contexto, o parâmetro de evapotranspiração de referência determinado pelo método FAOPM destaca-se. Contudo, uma medida precisa necessita de diversos parâmetros climáticos, que podem não estar disponíveis em algumas regiões rurais, neste sentido, uma solução promissora são abordagens que se utilizam da menor quantidade de parâmetros climáticos, que podem ser medidos por satélites e estações meteorológicas da região e modelos de aprendizagem de máquina. Nesta pesquisa os modelos MLP (Multlayer perceptron) e SVM (Support Vector Machines) foram utilizados para modelar a evapotranspiração de referência a partir de dados de satélites e estações meteorológicas sob duas abordagens: a abordagem local, onde os modelos foram treinados e testados em um local de treinamento, e a abordagem regional, onde os modelos treinados no local de treinamento foram aplicados em um local de teste, em dois experimentos: um em uma região de clima temperado e outra em uma região de clima tropical. Os resultados indicam que o modelo MLP se sobressaiu diante ao modelo SVM em todos as simulações realizadas, no qual os modelos treinados com os parâmetros relativos a temperatura e radiação obtiveram as métricas R2 de 0.6568, RMSE de 0.1103 e MAE de 0.0882 para o experimento da região de clima temperado e métricas R2 de 0.7391, RMSE 0.1266 e MAE de 0.1063 para a região de clima tropical na abordagem local, o que demonstra o potencial de uso de apenas estes parâmetros para a modelagem da evapotranspiração de referência. Já na abordagem regional o modelo MLP pode ser aplicado com exito, o qual obteve as métricas R2 de 0.7158, RMSE de 0.1592, e MAE de 0.1428, contudo, no experimento de clima temperado, os resultados da aplicação foram insatisfatórios, demonstrando que para as condições daquele local os modelos não puderam ser aplicados. The Food and Agriculture Organization of the United Nations (FAO) estimates that population growth will reach 11.2 billion by the year 2100, which will contribute to food and agricultural product demand, making irrigation optimization essential. In this context is highlighted the evapotranspiration parameter determined by the FAOPM method. However, a precise measurement of this parameter requires several climatic parameters that can not be available in rural areas, in which, a promise solution belongs in approaches that use just a few climatic parameters which can be obtained by satellites and weather stations combined with machine learning models. In this research, the MLP (Multilayer perceptron) e SVM (Support Vector Machines) models were used to model the reference evapotranspiration with satellite and weather stations data under two approaches: the local approach where the models are trained and tested on the training location, and the regional approach where the models trained on the training location were applied on a test location. These approaches were applied in two experiments: the first on a temperate climate zone, and the second on a tropical climate zone. The results indicate that the MLP model stood out when compared with the SVM model in all tests realized, in which, the models trained with the climatic parameters of temperature and radiation obtained the metrics of R2 of 0.6568, RMSE of 0.1103, and MAE de 0.0882 for the temperate climatic zone experiment and metrics R2 of 0.7391, RMSE 0.1266, and MAE of 0.1063 for the tropical climate zone experiment on the first approach which demonstrate the potential of using only these parameters to model de evapotranspiration. For the second approach the MLP model could be applied on the tropical climate zone in which the metrics R2 of 0.7158, RMSE of 0.1592, and MAE of 0.1428 were obtained. Yet the result obtained by the models applied on the temperate climate zone was inconclusive which indicates that for the conditions of this location the models can’t be applied with the second approach.

This paper presents a case of study of a IoT cloud plat-form composed of a microservices architecture that has been developed to integrate the CRITERIA-1D into the ZENTRA cloud. CRITERIA-1D is an open-source agro-hydrological model developed by ARPAE simulating one-dimensional soil water fluxes, crop development, and crop water needs. CRITERIA-1D comes with a default set of crops and soils that can be used or tuned for a specific scenarios. Taking as input the weather forecasts (i.e., temperatures and precipitations), the model can be used to predict the soil water content and soil water potential at different depths. Along with the design of the implemented solution, this paper presents the process of tuning crop and soil parameters for a specific use case. The results show that the tuned model estimates very well with respect to the measures observed by sensors, paving the way to its application within the larger context of the METER's ZENTRA cloud.

In recent years, the concept of Agriculture 4.0 has emerged as an evolution of precision agriculture (PA) through the diffusion of the Internet of things (IoT). There is a perception that the PA adoption is occurring at a slower pace than expected. Little research has been carried out about Agriculture 4.0, as well as to farmer behavior and operations management. This work explores what drives the adoption of PA in the Agriculture 4.0 context, focusing on farmer behavior and operations management. As a result of a multimethod approach, the factors explaining the PA adoption in the Agriculture 4.0 context and a model of irrigation operations management are proposed. Six simulation scenarios are performed to study the relationships among the factors involved in irrigation planning. Empirical findings contribute to a better understanding of what Agriculture 4.0 is and to expand the possibilities of IoT in the PA domain. This work also contributes to the discussion on Agriculture 4.0, thanks to multidisciplinary research bringing together the different perspectives of PA, IoT and operations management. Moreover, this research highlights the key role of IoT, considering the farmer&rsquo, s possible choice to adopt several IoT sensing technologies for data collection.

A key point to decrease water usage in agriculture is to employ precision agriculture by adopting technologies such as the Internet of Things (IoT). Many solutions using the IoT rely on the use of soil moisture sensors to gather data, in real-Time, from the plant's root zone. This study presents a linear calibration curve, obtained by the thermogravimetric method, for a capacitive soil moisture sensor for a silty clay soil sampled at the FEI University Center. The curve obtained has an adjusted R2 of 0.86 and a residual standard error of 2.8%. The soil sensor was tested in the field during the end of February revealing that the sensor is capable of indicating changes in soil moisture within the error obtained from the calibration procedure and that it can be used to indicate changes by rain or irrigation procedures.