Due to new possibilities for using sugarcane (Saccharum spp.) trash for electricity generation, and the production of 2nd generation ethanol and others chemicals, the interest for its recovery has increased. However, the question of how much trash can be removed from sugarcane field still needs to be clarified. This study evaluated the amount of dry matter, nutrients content, structural compounds and efficiency of the enzymatic hydrolysis of the hydrothermal pretreated materials for tops and dry leaves in samples from sugarcane varieties. Tops and dry leaves present differences in nutrients content and moisture. Therefore, the amount of trash to be collected should not be simply based on percentages, but also should take into account the different fractions of the crop residues. For instance, around 80 % of N, P and K were derived from tops. Therein, the environmental indicators of the entire chain of sugarcane could be benefited because more nutrients would be recycled and less mineral fertilizers might be used for sugarcane production if tops are left on the field. Further, the tops have seven times more moisture than dry leaves and higher amounts of extractives (organic compounds of low molecular weight). Moreover, as the result of yield obtained in the pretreatment steps for dry leaves were superior to the tops and the glucose yields obtained in the enzymatic hydrolysis step were similar, it can be predicted that for second generation ethanol production, it is more viable to recover parts of the dry leaves fraction, leaving the tops on the field

The use of unmanned aerial systems (UAS) as remote-sensing platforms has tremendous potential for obtaining detailed, site-specific descriptions of crop features, which would be very useful for precision agriculture. In sugarcane plantations, for example, cane height can be an indicator of yield and other parameters because it is highly influenced by the soil, total sugar content, leaf nitrogen content, temperature and light intensity. This article describes the generation of crop surface models (CSMs) from high-resolution images that were obtained using a UAS to estimate sugarcane height. Using a UAS with an on-board RGB camera, we created densified three-dimensional point clouds of the study area in two different flight line directions (North/South and East/West) using structure from motion (SfM) with multi-view stereo (MVS). Then, the digital surface model (DSM) and digital terrain model (DTM) were extracted and used to create CSMs. Maps of sugarcane height were created based on this information. We investigated the influences of different flight line directions (N/S and E/W) on sugarcane height estimations and their accuracy by comparing our maps with ground references. From the validation conducted using both flight lines, the average heights were closer to the field-verified data. The resulting maps showed differences in sugarcane height that were confirmed by field measurements. This method has potential for future use by sugarcane-related industries, researchers and farmers to estimate average crop height.

Fast and accurate quantification of the available pasture biomass is essential to support grazing management decisions in intensively managed fields. The increasing temporal and spatial resolutions offered by the new generation of orbital platforms, such as Planet CubeSat satellites, have improved the capability of monitoring pasture biomass using remotely sensed data. Here, we assessed the feasibility of using spectral and textural information derived from PlanetScope imagery for estimating pasture aboveground biomass (AGB) and canopy height (CH) in intensively managed fields and the potential for enhanced accuracy by applying the extreme gradient boosting (XGBoost) algorithm. Our results demonstrated that the texture measures enhanced AGB and CH estimations compared to the performance obtained using only spectral bands or vegetation indices. The best results were found by employing the XGBoost models based only on texture measures. These models achieved moderately high accuracy to predict pasture AGB and CH, explaining 65% and 89% of AGB (root mean square error (RMSE) = 26.52%) and CH (RMSE = 20.94%) variability, respectively. This study demonstrated the potential of using texture measures to improve the prediction accuracy of AGB and CH models based on high spatiotemporal resolution PlanetScope data in intensively managed mixed pastures.