AGRICULTURE


An extremely good vintage


Greg Blackman looks at the relatively recent introduction of vision in farming, from measuring vigour in vineyards to grading cherries


C


ompared to the history of agriculture and viticulture, machine vision has been around for a heartbeat – and


machine vision in agriculture a mere cardiac flutter. Te culture of wine growing is an ancient one with special emphasis on place and what the characteristics of a place – the soil, the climate, the topography of the land – bestow on a wine. Te concept is known as terroir, from the French word terre meaning ‘land’ and is the basis for characterising wines according to geographical region. A classic example is the Grand Cru vineyards in Burgundy, which were originally established by Benedictine and Cistercian monks, who, because of their vast land holdings in the region, were able to delineate plots of land producing exceptional wine. Te culture of wine growing and terroir is


held in high esteem, but new utterly modern methods stemming from precision agriculture are also finding their way into the practice of growing vines. Precision agriculture, a farming management method that aims to improve yield by responding to intra-field variations, was initially implemented on large farms in Australia and the US, but is now being applied in South Africa, Chile and Europe. It is based on satellite imaging and GPS, but other tools like camera sensors are also now available to farmers to help them better manage their land. One project being undertaken by the


Polytechnic University of Valencia in Spain is to use imaging to estimate vine vigour and correlate it with yield. Te researchers want to develop the system as a tool to give farmers more accurate information on when to harvest the grapes.


Te concept of precision farming has been


applied with some success in the Corn Belt in the US, mainly with bulk crops like corn, wheat, and soybeans. One of the problems though with introducing novel technology to farming, according to Rovira Más, is that a lot of it hasn’t reached actual farmers yet. ‘Tere is a big gap – the technology is being developed by universities and research institutes, most of the time by PhD students, and when the researchers finish a project most of the solutions die and never make it to the market. Tis is largely because of the technology’s complexity, price, and the difficulty of managing all this information.’ Te Polytechnic University of Valencia’s


Speaking with farmers,


they say they have problems in determining when to harvest grapes


Francisco Rovira Más is leading the project


at the Polytechnic University of Valencia: ‘We’re trying to make wineries in Europe more cost efficient. European countries are competing with other countries where labour costs are lower and wine costs less to produce. One of the ways to improve the cost efficiency of wine production is to introduce new technologies.’ As wine is a speciality, high-value crop, as


opposed to crops like wheat, which is a necessity crop, growers are more likely to employ new technologies like machine vision, according to Rovira Más.


18 Imaging and Machine Vision Europe • June/July 2012


system comprises one GPS receiver, a computer, and one imaging sensor, a monochrome camera from JAI. Te researchers wanted to keep their system simple and so avoided implementing things like multispectral sensors and thermographic cameras, which would give useful data, but would raise the complexity to a point where the system would never be commercialised in the short run. ‘Speaking with farmers, they say they have


problems in determining when to harvest grapes to produce high quality wine,’ explains Rovira Más. ‘Farmers say that many times they don’t know what they’re going to get in terms of quality.’ At harvesting time, typically in October, there is a lot of rain and the window for harvesting is quite short. A lot of the time all the grapes are mixed together, which means the quality is unknown. Te imaging system provides the farmer with information on the vigour of the grapevines prior to harvesting.


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