In recent years, sugarcane production areas in Brazil have experienced a slower evolution in productivity, and one of the reasons for this is related to the increase in phytosanitary problems, such as the presence of pests. Nevertheless, limited attention has been paid to the development of tools for simulating the spatial dynamics of pests, including their impact on production. This study aims to simulate the potential population growth and dispersal of Diatraea saccharalis (sugarcane borer) in sugarcane crop fields and its estimated impacts on this crop production and to simulate biological control strategies. We developed an agent-based model to simulate the pest population and its dispersal in a one-hectare sugarcane crop field in Pederneiras, São Paulo, Brazil, delimited with the aid of satellite imagery, considering two scenarios: the first without biological control and the second with biological control using the parasites Trichogramma galloi and Cotesia flavipes. The model was developed using the NetLogo 6.3.0 software. The results indicate that the model accurately reproduced the infestation rates reported in the literature. Additionally, it provided insights into expected pest dispersal, potential production losses, and how the use of T. galloi in association with C. flavipes could mitigate production losses. We believe that the model can be used to simulate different biological control strategies and the implementation of integrated pest management (IPM) to achieve adequate control levels and greater productivity in sugarcane production.

The objective of this study is to estimate the canopy height of three sugarcane varieties at different growth stages, with UAV data and to evaluate its relationship with two vegetation indices (VIs) (NDVI and EVI) at different spatial resolutions (3m, 10m and 30m). The indices were calculated using images from the PlanetScope, Sentinel-2, and Landsat 8 satellites, acquired as close as possible to the UAV imaging date. The estimated canopy height for each field was obtained by subtracting the Digital Surface Model (DSM) from the Digital Terrain Model (DTM), built by the Structure from Motion (SfM) technique with UAV RGB images as input. The average from each estimated height was compared with the average measured in the field, to verify the accuracy of the model. Both Pearson’s correlation and the Determination Coefficient (R²) were calculated between the estimated heights and the VIs. The average estimated canopy height and measurements in the field were different (p<0.05), with the model generally underestimating the height. However, the plantation’s surface models portrayed the spatial variability within the field. The use of GCPs is mandatory to reduce errors in estimation. Regarding the indices, the spatial resolution did not influence the correlation analysis, with NDVI showing higher values than EVI, except for area A. However, all values, for both coefficients, were below 0.5 for all areas. Despite that, a temporal analysis is necessary to improve the relationship between the canopy height and VIs. The potential of UAV data as a proxy to zonal management should be addressed in future studies.