No abstract

The use of mechanistic plant growth models relies on the availability of high-quality inputs to reduce uncertainty in estimates. Measurements of photosynthetically active radiation inside a protected environment are either more expensive to obtain or dependent on assumptions regarding external measurements. This study aimed to reduce the influence of uncertainty in the measurements of low-cost lux meters by using a data assimilation strategy. We first determined, by simulation, the impact of different sensors on the estimates. We then used the Ensemble Kalman Filter to assimilate artificial observations of tomato growth in the Reduced-State Tomgro model, in simulations for which the solar radiation inputs were obtained from a low-cost lux meter. We compared the assimilated estimates to the simulations that used solar radiation obtained with a scientific-grade quantum sensor. For periods of larger radiation intensity, in which the differences in measurements from both instruments are larger, assimilation of observations with low errors lead to estimates that are closer to the ones obtained by scientific grade sensors. These results suggest that low-cost sensors could be used to obtain inputs for growth models in protected environments, provided there are also imperfect observations of the state.

Borer infestation (Diatraea saccharalis) is one of the main concerns in the sugarcane crop because it affects productivity directly and negatively. In order to find alternatives that minimize these damages, the objective of this work is to develop predictive models using data mining tools to predict the infestation of the borer in the sugarcane crop.

Due to the high costs associated with planting a new sugarcane field, sugarcane ratooning is explored to decrease production costs. However, ratoons have successively smaller yields, because of the effect known as sugarcane yield decline, which can impair the profits. The factors underpinning the ratoon yield decline are yet to be established. The objective of this work is to apply decision trees to sugarcane production mill data to evaluate factors related to the sugarcane ratoon yield decline. For this, meteorological and production data from four sugarcane mills were evaluated, comparing the yield obtained with the yield of the following year.

Mais dados têm sido gerados em aplicações agrícolas por novas fontes e com grande potencial de uso. Obtidos no campo ou em fazendas verticais, eles podem ser usados, por exemplo, em gêmeos digitais, que visam conectar observações a um modelo do sistema. Essa conexão pode ocorrer por assimilação de dados e em ambientes protegidos, em que as plantas podem ser monitoradas mais intensamente, mais dados estariam disponíveis. Neste trabalho, realizamos assimilação em um modelo de tomateiro usando dados coletados por câmeras e por células de carga, observando que essas fontes fornecem boas estimativas de biomassa da parte aérea e que a técnica melhora as estimativas obtidas pelo modelo Tomgro Reduzido sem calibração.