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Index prediction

It is an advanced forecasting tool that uses machine learning models to predict the evolution of agricultural vegetation indices. It allows users to select a specific field and index (NDVI, MSAVI, LSWI, RECI, or NDRE) and anticipate crop health trends, supporting proactive management decision-making.

It is an advanced forecasting tool designed to predict the future trajectory of key agricultural vegetation indices. Using a time-series machine learning model, users can select a specific field and index (such as NDVI, MSAVI, LSWI, RECI, or NDRE) to generate a forecast of its evolution, enabling them to anticipate crop health trends and make proactive management decisions before issues escalate.

Water production and storage, and water use efficiency

Main theme:

North

Region:

300 - 400

Precipitation (mm):

Medium

Application difficulty:

1, 2, 8, 5, 10, 13, 15, 14, 12 and 17

SDGs impacted:

Electric

Energy used:

70 - 90

Efficiency (%):

Urban/Rural

Sector:

Vegetation index forecasting makes it possible to anticipate changes in crop health, enabling more efficient and preventive agricultural management. By early identification of water stress, vegetation degradation, or anomalies in crop development, the tool helps optimize water use, reduce unnecessary application of agricultural inputs, and minimize associated environmental impacts. This supports more sustainable agricultural practices, improved conservation of natural resources, and a reduction in the environmental footprint of agricultural production.By streamlining and facilitating companies’ compliance with the rigorous European Sustainability Reporting Standards (ESRS), the tool strengthens corporate accountability and transparency. This encourages organizations to measure, report, and actively manage their environmental footprint with greater accuracy, leading to better-informed strategies to reduce emissions, conserve resources, and minimize waste. Ultimately, it helps ensure that corporate sustainability claims are supported by verifiable data and actions.

Expected environmental impact:

Free

Estimated value:

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