Technological innovation in sugarcane production: HEMAV presents new ways to optimize crops

Our predictive system (AI) in sugarcane production has been trained on more than 40 million hectares.

Accurate forecasts are the key to proper campaign planning; from day one, they contribute to good resource management during growth and harvest.

Our users reported great satisfaction with LAYERS’ PREDICTIVE-TECH: it has become a tool is to ensure the constant supply of the factory.

Context:

The mills have a weekly and monthly plan to follow for tons of cane to be processed. It is vital that the factory never stops and that it meets the targets set because its costs and therefore its profit depends on it.

Having a ATR or SAC growth model by plot allows the selection of the farms to be harvested and to know the production of these farms before they are harvested and taken to the factory.

Results:

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.

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