Artificial Intelligence and Olive Oil Cultivation and Production
Artificial Intelligence and Olive Oil Cultivation and Production
Artificial intelligence: international research team brought Innovation in olive oil production
Short-term scenarios on which to base investments, take preventive measures and take action with agronomic practices will be available to olive oil producers with the help of Artificial Intelligence (AI), thanks to an international research team.
The joint research team from the Italian National Research Council (CNR), the Agency for New Technologies, Energy and Sustainable Development (ENEA) and the University of California, Berkeley, managed to develop an algorithm that is actually available to anyone interested, who can predict the size of an olive grove’s harvest based on the climatic data in the specific area.
The algorithm learned to make these predictions based on the last 15 years of data from Italy and in essence found the way in which different climate phenomena affect crops. The algorithm analyzes the weather patterns during the period of the olive growing cycle and thus the researchers were able to identify potential climate risks and how they affect production.
In total, harvest data from 66 Italian provinces during the period 2006-2020 was analyzed and from this analysis the factors that caused the worst harvests were revealed. But the research did not stop at identifying risk factors, but went a step further and then grouped these different variables by two months, making a list of variables and examining how they interacted over time. So they managed to be able to provide a short-term forecast, which they say is three times more accurate and better than if they had done a single-variable analysis.
They then looked at which seasonal climate variables produced either poor or excessively good harvests and completely left out those that produced close to average harvests. This, in other words, looking at the minimum and the maximum, enabled them to essentially focus on the effects that climate variables have on production, regardless of what each producer is doing.
Also helpful regarding Bactrocera Oleae (olive fruit fly)
The researchers argue that if the algorithm continues to be trained with more geographic data, the more “general” the predictions it makes will become and therefore it will be able to be used on a state scale or even larger.
The topic was highlighted by the specialized website Olive Oil Times, where the researcher who participated in the research, Ariana Di Paola, stated that: “We are working to understand the climate indicators that can trigger bad conditions and the associated possibility of negative consequences for the production olive. Examples of such indicators are conditions that favor the development of the olive-fly or high winter temperatures that may alter the olive cycle and have an impact on flowering and pollination. Understanding the seasonality of each moment enables us to predict what we should expect in the near future. These are short-term scenarios on which to base investment, take preventive measures and take action with agronomic practices.”
SOURCE: Artificial intelligence: international research team brought Innovation in olive oil production – sofokleousin.gr
Olive growing: Increasing yields and reducing costs through Artificial Intelligence
According to Olive Oil Times, researchers in Andalusia are developing a new tool that will allow farmers to leverage Artificial Intelligence (AI) to know when their olives are ready to harvest.
Citoliva, a non-profit research and technology company, maintains the belief that its AI-based predictive model could improve yields and reduce production costs. Its executives estimate that such a model will be ready for commercial use in two years.
Together with Spain’s Ministry of Industry, Trade and Tourism and Citoliva’s four private sector partners working on the project, the organization believes that the new technology will allow farmers to predict the moment of maximum olive oil content without repeated trips to the groves.
The researchers added that it is expected to reduce the time spent on information analyzes and the cost of harvesting, while allowing for more accurate production estimates.
“The tool is currently in the research phase. The idea is that it works with a combination of data provided by satellite images and ground sensors, and the farmer could operate it from his mobile device,” said Carmen Capiscol, head of research, development and innovation at Citoliva, to add to continued: “Potential users have not yet been determined, but it is likely that the approach will be to cooperative members rather than individual producers.”
Regardless of cost and ease of use, Capsicol believes the tool will help olive growers be able to harvest at the most appropriate time. This is considered even more important due to the change of climatic conditions and patterns in Andalusia and throughout the Mediterranean basin more widely shifting some of the key moments of olive development.
It is worth noting, however, that the researchers have not yet determined the cost of installing the necessary sensors or using the wearable device for farmers.
SOURCE: Olive growing – Increasing yields and reducing costs through Artificial Intelligence (AI) – Ypaithros.gr
Artificial intelligence is revolutionizing meteorology
Google, Huawei and other companies are launching AI models that improve the accuracy of predictions in weather forecasts
A new generation of artificial intelligence (AI) algorithms, some of which run even on a simple laptop computer, promise better weather forecasts than today’s meteorologists’ models that require supercomputers. The latest development comes from DeepMind, an artificial intelligence company now part of the Google group, which presented in Science magazine an artificial intelligence model that already offers top performance.
The GraphCast model produces more accurate ten-day weather forecasts than the conventional model of the European Center for Medium-Range Weather Forecasts (ECMWF), considered the world’s leading meteorological service. The final algorithm takes less than a minute to provide ten-day forecasts And the new model gives forecasts in minutes, requiring nothing more than a simple PC, unlike ECMWF’s supercomputers which take hours for each forecast.
Education The mathematical models meteorologists use today divide the Earth into large squares and use the laws of fluid physics to simulate the behavior of the atmosphere in the future, using available measurements of current conditions as a starting point. This approach is computationally demanding, and few meteorological services can afford to update their reports more than four times a day. AI models, by contrast, do not solve physics equations. They are taught by examples – meteorological observations from previous years – to learn to recognize patterns in the behavior of the atmosphere and to predict how parameters such as pressure, temperature and wind speed interact.
To train GraphCast, DeepMind researchers used ECMWF data spanning 40 years. It took dozens of computers and four weeks to complete the process, but the final, trained algorithm takes less than a minute to produce ten-day forecasts, which outperform ECMWF’s conventional forecasts in 90% of benchmarks.
DeepMind’s algorithm isn’t the only one achieving spectacular performance. Earlier this year, the Chinese company Huawei, best known for its mobile phones, presented its own similar model in the journal Nature, while Google has launched its experimental 24-hour forecasting tool MetNet-3, which outperforms most weather service reports in accuracy. . ECMWF itself has been offering AI experimental data since September. The progress is impressive, although some meteorologists and physicists remain skeptical, in part because the new technology acts as a “black box” that produces results but does not explain how they were arrived at. It is likely that AI will be used alongside and in addition to mathematical models of the atmosphere.
What is certain, however, is that Artificial Intelligence came to stay in meteorology.
by Vangelis Pratikakis
SOURCE: Artificial intelligence is revolutionizing meteorology – www.in.gr
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