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Why you need more than 5 image data points per season

In-season timing, potential of use cases during the whole season, analytics, and weather are the main reasons.

According to TerrAvion, a large provider of aerial image data for agriculture, growers need more than 5 image data points per season. The company states in a recent weblog that it captures image data frequently. “Although it can vary greatly depending on region and subscription, we, on average, deliver around 12-14 image data points per growing season in the United States, for example.”

For the 2020 growing season, once TerrAvion starts flying in a region, they aim to collect all fields in their system each week, making three attempts per week to get the image data. The company says the method of collection they have developed, using a crewed fixed-wing aerial platform, creates a reliable service with the maximum chance of getting data.

Why so many data points?

One of the questions TerrAvion often encounters is why they have such a high-frequency image subscriptions. “There is a multitude of reasons but the main four are in-season timing, potential of use cases during the whole season, analytics, and last but not least, weather.”

According to TerrAvion many growers and agronomists think that they only need a 5 or 7 data points depending on the programs they run on their fields. “And while theoretically, that can be true, we have learned from experience that while you might think that you need data this week, not all your fields might be at the growth progress that is required in order to provide value for the program that needs that data.”

“For example during harvest time when a field on one side of the road is ready, but the field, or part of the field, on the other side is not; or early in the season when not all fields get planted at the same time, or some fields are ready for fertiliser while others not just yet. Frequent imagery can show you where your fields are at,” says TerrAvion.

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The dynamic vigor layer brings out the most detail per image data. The large variety in color shows that fields are not all in the same growing phase at any time, and to be able to capture useable data for each field, you need to have more than 5 image data points during a whole growing season. - Photos: TerrAvion
The dynamic vigor layer brings out the most detail per image data. The large variety in color shows that fields are not all in the same growing phase at any time, and to be able to capture useable data for each field, you need to have more than 5 image data points during a whole growing season. - Photos: TerrAvion

Disease infestations or irrigation issues

Then there are use cases that you were not planning for, like pests or disease infestations or irrigation issues. “If you do not capture data regularly, you will very likely miss those early enough to prevent yield loss. Frequent image data can find those unexpected events,” says the company.

And having many data points during the season helps tells the whole story of each field during the growing season, so all your actions can be evaluated for the value they provide, instead of only looking at the yield at the end of the season. “Yield is essential, but knowing what event actually contributed positively or negatively to it, will provide valuable information for plantings in years to come.”

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This early season pansharpened image of a pivot has a large variety in color. The dark-colored parts of the field are waterlogged which most likely will affect the growth throughout the season differently than the yellow-orange colored areas.
This early season pansharpened image of a pivot has a large variety in color. The dark-colored parts of the field are waterlogged which most likely will affect the growth throughout the season differently than the yellow-orange colored areas.

Analytics

Next are the analytics driving the platforms we all use to work in a digital world. “Our partners that provide tools for agribusiness from Farm Management Systems to Equipment Manufacturers and Precision Agriculture Systems all need the maximum amount of data to understand the trends. Without high-frequency imagery, one can not understand what data point is an outlier and what, in fact, is a true data point. It is this high density of information which allows not only experts in the field to understand but also computer algorithms, machine learning, and artificial intelligence to recognise, understand, alert, and make recommendations. Affordable frequent image data points provide the necessary information.”

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According to TerrAvion. early season bare soil imagery is extremely useful to find answers for issues that arise during the growing season. If you do not know what the soil looks like before you start growing, you do not know the basis of your fields.
According to TerrAvion. early season bare soil imagery is extremely useful to find answers for issues that arise during the growing season. If you do not know what the soil looks like before you start growing, you do not know the basis of your fields.

Weather

The weather is the single most significant limiting factor, according to TerrAvion. “Consider Landsat, a series of satellites we all know, which has, for decades, provided an image every 16 days without fail. This twice-monthly frequency collects 8-10 images a season; however, historically speaking, we only find 3-4 usable clear images during that time.”

The reason why TerrAvion does not deliver an image data point to customers each week with their weekly flight schedule is for that same reason, the company says: “We cannot control the weather. If there are clouds in the sky, it affects the quality of the data collected from haze to clouds or cloud shadows. Additionally, the clouds might hinder us from flying at the right height, as our cameras can not see through clouds once they are below our planned flight height.”

However, a crewed aerial platform does allow pilots to judge the local weather, fly around storm systems, and make accommodations to collect the maximum number of fields every single day to guarantee the highest reliability in image delivery during the growing season, concludes TerrAvion.

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