Rubber is a natural resource widely produced and used throughout the world. The uses of rubber are varied and range from the manufacture of tires and automotive products to the production of footwear or medicines….
What are Predictive Models?
These are algorithms that use historical data and patterns identified in that data to predict future events or outcomes.
Advantages of Predictive Models:
Predictive production models are becoming very useful in agriculture, are fundamental for data-driven decision making and offer companies and organizations a competitive advantage by helping them to anticipate future events and trends, especially in the scenario resulting from climate change.
The Data Age:
Climatic information (temperatures, precipitation…), satellite data (NDVI, NDRE, SAR…), field data (biometrics, production data…) are a cocktail that could become indigestible, especially when we try to simplify the Machine Learning processes that involve our models.
How we started:
Hemav performs previous studies before modeling a crop, not only using bibliographic information and scientific studies, but also analyzing specific cases of our clients’ data.
Here is an example:
Determining which parameters are fundamental and how to structure them helps in the training of the models, and for this it is advisable to combine statistics with the agronomic point of view, as in this example where the phenological cycle of the crop tells us what to look at and when.
We are eager to hear about your experiences:
Are you using predictive models in your work dynamics in the agricultural sector, and would you be interested in starting to do so? Let me know your experiences and concerns.
If you are interested in the use of predictive models do not hesitate to contact us, knowing in advance the production can be vital in the management of your crop.