How HEMAV contributes to sugarcane cultivation optimization

Our AI predictive system for sugarcane cultivation optimization has been trained on more than 40 million hectares.

Accurate forecasts are pivotal for strategic campaign planning; from the outset, they support optimal resource management during growth and harvest periods.

Our users have expressed significant satisfaction with LAYERS’ PREDICTIVE-TECH, highlighting its importance in ensuring the factory’s consistent supply and optimizing sugarcane cultivation.

Context

The mills maintain a meticulous weekly and monthly plan for the tons of cane to be processed, essential for sugarcane cultivation optimization. Ensuring that the factory operates continuously and meets its targets is critical, as its profitability directly depends on these factors.

Possessing an ATR or SAC growth model by plot not only facilitates the selection of farms to be harvested but also provides insights into the production of these farms before their harvest and delivery to the factory.

Results in sugarcane cultivation optimization

Thanks to our viewer, LAYERS, in the case of not reaching the target planned in their planning, they have time to change the weekly/monthly selection of plots, ensuring the fulfillment of the target and avoiding a possible setback.

The image shows the crop monitoring viewer, and the TCH panel by plot vs. crop days, used by field and logistics managers.

Three selected plots are shown for the same harvest month (July 2022). It is observed that their yields are very different (ART of 87, 102 and 144) in spite of being close, having similar treatments and similar size.

The accuracy of the model helps to prepare logistics and ensure that the harvest target is met. The accuracy of our model was very high. A maximum deviation of 3% from the actual data collected.