vineyard – Corbeau Innovation

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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Jun 1 2021

Vineyard yield estimation with smartphone imaging and AI

vine shoot and cluster — **Vine shoot and flower buds**

There is so much potential in those tightly closed flower buds. Over the course of the summer the flowers on vines bloom, turn into tiny green spheres and ultimately heavy bunches of grapes. Or at least that is the hope of the vineyard owner and winery. Accurately estimating the size of the harvest well in advance has a number of advantages. Early yield estimation allows the right number of pickers to be hired at a reasonable rate and the right amount of tank space, bottling and packaging.

Yield estimation by manual visual inspection is the method recommended by the Grape and Wine Research and Development Corporation (GWRDC, Australian Government). In a 2010 guidance sheet, Professor Gregory Dunn (University of Melbourne) recommends randomly counting grape clusters across entire vineyard parcels. There is good correlation between the number of grape clusters per vine and the ultimate yield.

**Correlation between vineyard production and grape cluster count (courtesy of GWRDC)**

It is not always easy to make an accurate random sample count of bunches and previous yields can vary from year to year. Counting in vineyards in cool climates like the UK has both these difficulties because seasons tend to be more variable than further south and more vigorous vines tend to be planted. Vigorous vines like Reichensteiner produce thick leaf canopies that obscure developing fruit.

Researchers at Cornell University recently reported a novel, cheap and effective method of early yield estimation based on smart phone video footage of a whole vineyard and artificial intelligence (AI) analysis of the recorded images.

smartphone_ATV grape vine imaging — **All terrain vehicle with smartphone on gimbal and LED lighting panels (courtesy of Frontiers in Agronomy and Cornell University)**

Stereo-imaging and LIDAR measuring devices have been around for a while now but they are expensive, think £’000 to £’0,000 to equip a vineyard with a system. The Cornell system is essentially a smartphone on a gimbal with a lighting boom that can be driven or walked up and down the rows of a vineyard at night.

In addition to being a low cost solution, it is also effective. They report a cluster count error rate of only 4.9%, almost half that of traditional manual cluster counting. Improved cluster counting and therefore better yield estimation is obtained mainly due to better random sampling of vineyard and better identification of clusters. Over two growing seasons the Cornell team found that early video imaging gave the best results because small clusters and shoots were not obscured by large leaves and a dense canopy.

So how did they turn a rather long video into an accurate and precise cluster count?

Firstly the different objects (leafs, shoots, clusters, posts etc) in the video needed to be classified. Building the classifier is the major task in a machine learning implementation. There are a number of Open Source tools readily available to do this. They chose a Convolutional Neural Network (CNN) to identify objects in the video images. CNNs apply digital filters to simplify and exaggerate whole images, making them look more round, jaggy, linear etc. These are applied to the image under test to find the combination that finds a result that fits a set of defined examples of a particular object. But how does the CNN know what an object is? who defines the objects? The Cornell answer is student interns. They were given sets of training images and a copy of Open Source Python app LabelImg and tasked with drawing boxes around each object of interest and giving them the label ‘cluster’. The other useful source of information to train the CNN was the so-called Microsoft COCO (Common Objects in COntext) dataset. COCO is essentially a large set of sorted images that are not grape clusters. The image below shows how clusters are identified from video footage.

vine clusters located by CNN — **Vine clusters identified by trained CNN (courtesy of Frontiers in Agronomy and Cornell University)**

TensorFlow, a user-friendly Open Source platform was used to train the neural network and apply it to the video footage.

It would be fascinating to apply this cost-effective and early yield technology in the UK, where the climate is warming but seasons are still variable.