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Agtech Letters

This category is used for all Agtech Letters (that is, brief 5-10 minute reads).

#19 – Common vegetation indices related to crop biomass

Different vegetation indices can be used to process data from multi-spectral images from satellites or drones to valuable information on crop status and health. In this Agtech Letter, we introduce three indices that are related to the biomass of the crop, and can be used to estimate how healthy and dense the crop in a field is.

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#18 – An introduction to vegetation indices used in agriculture

Vegetation indices computed from camera images captured by satellites or drones can be used to estimate the crop status for a field. In this Agtech Letter we walk you through the basics about vegetation indices – what they are and how to compute them. In the next couple of Agtech Letters we will look at specific indices.

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#17 – Satellites as a tool for far-field sensing in agriculture

Satellites have been used to analyze fields and crops from the sky since the 1970’s, and since then the performance of the satellite sensing has increased in terms of resolution and quality. In this Agtech Letter, we look into what satellites are and how they can be used for monitoring fields, and in the coming three Agtech Letters we look more specifically at different measurements that can be done with them.

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#16 – Example: Find variations in yield using yield maps from several years

In this example, we look into how several years of yield maps can be used to gain new insights into the performance of the field and how the yield is varying over the years. AgriOpt’s AI-powered digital assistant Freja is used for the analysis. The example can be seen as a motivation and inspiration for you to start collecting yield data!

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#13 – Examples of data related to the soil

One of the foundations in data-driven and efficient farming is to collect data about the soil. The soil and its properties set a limit in the crop farming and possible yields, and by understanding them you can plan your actions and inputs to match the need of the crop given the conditions in that specific field or management zone. In this Agtech Letter we provide you with some examples of data about the soil.

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