precision viticulture – Corbeau Innovation

agriculture

Oct 31 2021

Precision viticulture coming to fruition

baco_multispectral_vine_leaf_imager — **BACO multispectral leaf imager on a grape crate**

Mists, check. Mellow fruitfulness, check. Maturing sun, – eventually check! In the UK the 2021 growing season has been good but not exceptional. A late spring followed by a warm June and July gave way to a disappointing August with plenty of warm damp days to encourage the development of mildew. It has been fascinating to follow the 2021 Llanerch Precision Viticulture Pilot Study from bud-burst to final harvest. As followers of the study will know, we started in April with the installation of a SensIT microclimate weather station and then made the first BACO multispectral leaf measurements at the end of May. BACO leaf measurements continued approximately every two weeks until harvest was completed on 22nd October.

Season of mists and mellow fruitfulness,
Close bosom-friend of the maturing sun;
Conspiring with him how to load and bless
With fruit the vines that round the thatch-eves run;
To Autumn by John Keates (1795-1821)

SensIT and BACO are two of the key components of the Xloora precision viticulture system. SensIT is an IoT (Internet Of Things) weather station measuring %RH, T, wind speed and direction, wet-leaf, rainfall, sunshine and air pressure. Hourly readings are uploaded to the Xloora cloud platform and used to predict the likelihood of disease development. BACO uses seven different wavelengths of light from deep blue to near infrared to take pictures of vine leaves. Ratios of these images can give measures of leaf pigments such as chlorophyll, carotene and anthocyanin. They can also give early indication of disease development, forewarning the farmer of problems before they are obvious to the human eye.

SensIT microclimate IoT station
BACO multispectral leaf imager
Reichensteiner BACO image GPS locations

BACO also has GPS so that the locations of vine readings can be associated with blocks of different grape varieties. The web browser user interface shows individual measurements, alerts and reports over longer periods of time.

The grape harvest at Llanerch this year has been tremendous, a great crop achieved with few interventions to control disease. From reichensteiner to solaris, to seyval blanc, phoenix and even the old triomphe d’Alsace, vines have been heavy with bunches of grapes. A great collection of outputs from the vineyard. What about the Pilot Study?

**Harvesting grapes at Llanerch vineyard**

Over the season there were more than 3,500 IoT data uploads and more than 28,000 microclimate records generated. Almost 300 multispectral sets of images were generated by BACO making more than 2,000 leaf images in total.

Detailed analysis of the data, reports and alerts has just started but some interesting results are already clear. At harvest just one block, the reichensteiner, had obvious signs of developing mildew on some of the vines. A time series of representative leaf images from the rows in question shows the progressive change:

**NDVI leaf images of susceptible reichensteiner vines, acquired with BACO from June to October**

It appears by visual inspection that the leaf ratio (normalised difference vegetation index, NDVI) images become much less uniform as the disease burden grows through the year. Finally mildew is evident to the human eye in late October.

SensIT microclimate data from the vineyard at Llanerch was compared with a feed from a commercial weather station in the area. While the temperature and relative humidity values were generally similar, there were notable deviations.

Comparison of both the temperature and Relative Humidity data from the same period in August reveals that the microclimate of the vineyard follows the general trend of the meteo reports but changes are much more pronounced. Presumably this is because the vineyard at Llanerch has an open aspect which warms and cools more quickly than the location of the meteo report sensors (which was not known precisely). Accurate data from the vineyard is likely to be crucial to the success of predicting the start and spread of diseases like downy and powdery mildew.

A more detailed analysis of the Xloora precision viticulture platform results will be carried out over the coming weeks in preparation for extending the trial next year.

Visit us at the Vineyard Show 2021 at the Kent Showground on 24th November, booth S8

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May 2 2021

Precision viticulture for table grapes

Some farmers do not pick grapes, squash them and then ferment the juice. Some farmers just sell them for eating. And why not? Grapes have long been the gift of choice for hospital visitors. The glucose sugar in grapes is absorbed quickly into the blood stream to provide a burst of energy for a sick patient. Table grapes are also a major global fruit crop, with almost 26 million metric tonnes being grown last year, 2020. Typically table grapes account for roughly one third of global grape production, less than 10% are grown for dried fruit and the bulk are grown for wine making.

It is logical then that table grape growers are becoming more and more interested in new precision viticulture technologies being adopted in winery vineyards. The key questions for farmers are: which technology to adopt? what are the benefits? how easy is it to use? These are the questions a group from the Agricultural University of Athens (AUA) have been keen to answer and they recently published their findings in a paper last month.

Emmanouil Psomiadis and his colleagues from AUA made a comparison of two common approaches to vineyard monitoring: remote (via satellite) and proximal (ground based) multispectral analysis of a table grape crop canopy. Multispectral analysis measures the amount of light of different wavelengths reflected by a crop. Whereas the human eye can only see three different colour bands (red, green and blue), imaging detectors like the Multi Spectral Instrument (MSI) on the European Sentinel-2 satellite have up to 13 bands from the violet end of the visible spectrum to the invisible infrared. Having more spectral bands offers the possibility of better resolution of different plant pigments like chlorophyll, xanthophyll and lycopene. More accurate measurement of plant chemistry can potentially be used to pick up early signs of disease and nutrient deficiencies.

ESA Sentinel-2 satellite (courtesy European Space Agency)

The team from AUA were also interested to discover which of the many so-called Vegetation Indices (VIs) were the most useful. One of the features of satellite measurements of the earth surface is that the light on the ground is not always the same. It’s true that the sun’s output is pretty much constant but the angle of the sunlight falling on plants in a vineyard changes throughout the day and from season to season. The atmosphere tends to scatter blue and violet light more than red and infrared and therefore sunlight appears red in the evening and yellow in a clear sky at midday. Sunlight reflected back to an orbiting satellite from a vine canopy therefore changes from day to day.

Vegetation Indices, which are ratios of different spectral bands, were introduced to overcome these variations. Three of the most widely used are Normalise Difference Vegetation Index (NDVI), Normalised Difference Red Edge (NDRE) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Researchers have their own favourite VI but each tries to accurately determine the fraction of the ground covered by green vegetation within a particular image pixel.

Measurements with proximal multispectral instruments have a number of advantages over satellite imaging. Handheld or tractor mounted instruments cannot cover the vast areas caught in a single frame by a satellite detector but they are never interrupted by cloud and they can be fitted with their own light sources. With a consistent light source and measuring just the vines, rather than the earth or cover crop between them, proximal multispectral imaging could offer better quality vegetation indices than satellites. The AUA group chose to use satellite data from the ESA Sentinel-2 satellite and proximal multispectral imaging from a Holland Scientific Crop Circle ACS-470 (see below mounted on a quad-bike).

Crop Circle ACS-470 on boom attached to quad-bike for multispectral measurements of vine canopy
(courtesy Agricultural University of Athens and Agronomy Journal)

Slide showing spectral wavelength (microns)and width of Sentinel-2 detector bands and chlorophyll absorptions (arrows)

To test the ability of both proximal and remote multispectral imaging to quantify grapevine vigour (as a VI), Psomiadis and colleagues chose a small vineyard near Corinth, Greece growing table grapes. They monitored the vineyard through the season, recording proximal multispectral data from vine canopies and downloading satellite images from the start of ripening (veraison) to the point of harvest. The best correlation between the two sources of spectral measurements was obtained with NDVI and FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) indices.

NDVI = (Reflectivity(Near infrared) – Reflectivity(Red)) / (Reflectivity(Near infrared) + Reflectivity(Red))
Equation for calculating NDVI; the higher the ratio, the greener the canopy

Interestingly it was found that the best correlation (correlation >0.87) between proximal and remote measurements was at the later stages of grape ripening. Sentinel-2 satellite images have at best 10m spatial resolution therefore measurements earlier in the growing season, before the full development of the leaf canopy will include a bigger contribution from bare earth or a cover crop grown between the rows of vines.

Many more details about the vineyard studied and the calculation of different VI ratios can be found in the Agronomy journal paper. Importantly, the study has shown that multispectral measurement of vineyard vegetation indices using both remote and proximal technologies give consistent results.

The paper suggests three answers to the key questions from grape farmers:

use either remote or proximal multispectral measurement, ideally both as they are complementary
the key benefit demonstrated from the study is that plant vigour can be non-invasively quantified either with low resolution satellite imaging or higher resolution proximal measurement
Sentinel-2 imagery is freely available but some knowledge is required to create the VI maps of a vineyard. Proximal measurement is quicker than traditional field walking but with GPS tracking it can be straightforward to relate VI ratios to vines

Read the full paper here.

Find out more about the ESA Sentinel-2 mission here.

Find out more about Corbeau’s own multispectral vineyard project.

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Mar 6 2021

Discovering precision viticulture

Imagine sharing a bottle of wine as the sun sets slowly over the vineyard. Who wouldn’t want to work in a winery? Corbeau founder Pierre Graves talks to agronomist Miguel Rodrigues from XpectralTEK about his passion for wine and technology.

So what originally inspired Miguel to become a viticulturalist?

“It’s actually perfect that you are asking me that because it was the UK that helped me to make the decision.”

Wine making was a kind of spiritual enlightenment for Miguel. During an eight day walk to Santiago in northern Spain, following the Camino de Santiago pilgrimage route, he got talking to a retired English winemaker who enthused about tending the vines, making the wine and of course drinking it!

It’s maybe a cliche but it is the people he meets who make his work as an agronomist so rewarding.

“A farmer will always have something to teach you.”

Searching for new developments in viticulture, Miguel discovered the FREND precision viticulture project website and was impressed with the technology. Basic weather-station sensors have been used in agriculture for some years but FREND uses artificial intelligence (AI) and multispectral imaging as well as physical sensors.

“This FREND project is taking viticulture to another level.”

XpectralTEK has a team of around 17 people including physicists, software programmers, electronic engineers and designers working on the project. Development work takes place close to where Miguel is based in Braga, Portugal and on the Greek island of Crete. He joined XpectralTEK during the pandemic and has been trialling the technology in Portuguese vineyards. The first vineyard in Famalicão, near Braga, hosted initial tests using prototype instrumentation. This was followed by a further pilot study in a second vineyard in Melgaço, further north in Portugal.

The project aims to provide farmers with as much information as possible to help their decision making. Information about disease, lack of nutrition and water stress are three key requirements the project has focussed on. These problems can cause real damage to a vineyard and there is usually little warning before the vines are affected. So what technologies does the XpectralTEK system use to reveal this information?

“BACO is a multispectral device which takes images [of vine leaves] and with the help of AI it can show the presence of disease and lack of nutrition in the leaves. With this solution you can see the problems before the human eye can see them.”

The other device used in the vineyard is SensIT, a multi-sensor weather station adapted to alert to conditions that can lead to disease development.

“The platform is a smart, easy to use system that anyone can use without special technical knowledge.”

Portugal grows many local grape varieties therefore the system was applied to five different types of vine (Loureiro, Alvarinho, Trajadura, Arinto and Avesso). Monitoring the leaves of different varieties directly allows the AI system to learn how each leaf type responds to growing conditions. Vines were trained largely in a single guyot system. The user interface is web-browser based so you can use a smartphone, tablet or laptop in the vineyard or in the office. All the gathered information is stored and analysed in the Cloud. Miguel explained how the precision viticulture system is used in the vineyards.

“SensITs are monitoring the vineyards the whole time. When the AI advises that conditions in the field are good for growth it makes no recommendation to the user. When SensITs detect conditions when disease may start, the AI advises the user to go to the field and make leaf measurements to either get reassurance that there is no disease detectable or to get an advanced warning of disease not seen by eye. The farmer can then intervene early with a treatment, avoiding a check in growth.”

The platform can learn from the farmer as well as the vineyard itself. For example if the farmer often experiences mildew when the temperature is higher than a particular value and the relative humidity is above a certain value, the platform can set an alert for mildew.

Once baseline data from a vineyard was established, the XpectralTEK platform was found to reduce the number of trips farmers had to make to the vineyard, saving time and money.

As Spring approaches, Pierre is looking forward to starting a pilot study of the precision viticulture platform at a vineyard in South Wales. Later in the year, pandemic permitting, it will be great to open a bottle of wine with Miguel and watch the sun slowly setting over the vineyard.

You can find out more about the XpectralTEK FREND project here. If you are interested in receiving updates on the UK vineyard pilot study, leave your contact details here.

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