In Taiwan, fruit fly populations are normally monitored using traps that are manually checked every 10 days. Cheng-Long Chuang and colleagues at the National Taiwan University in Taipei wanted to automate the counting process, so they placed infrared beams in the traps. Each trap records when the beam is broken, indicating that an oriental fruit fly has entered, attracted by a chemical designed to lure the insect. The results collected are sent via radio to a local station every 30 minutes, allowing real-time measurements of the population.
Part-funded by the Taiwanese government, the team have so far set up 240 traps on fruit farms around the country. Machine learning algorithms pool the continuous data arriving from each of these traps and predict when the local fruit fly population is about to explode.
To help in this prediction, the traps are also fitted with weather sensors that monitor temperature, humidity, wind speed and rainfall. Fruit fly population surges tend to match changes in weather - when it is humid, the level of insects is expected to rise, for example.
In Taiwan's current system, a red alert is issued when the number of flies caught in a trap surges beyond 1024 in a 10-day period. But the AI system can learn what counts as a normal level of fruit flies in an area and adapt its warnings on the basis of the current weather and time of year. It can also work out where the pest is likely to be breeding.
When a potentially devastating infestation is predicted, it automatically sends a text message to government officials' cellphones, providing the time, location and severity of the potential outbreak. The warning should allow authorities to pre-empt the outbreak by putting down insecticide.
Tested on historical data taken from the network of traps, the AI system was accurate in predicting an outbreak 88 per cent of the time.
- More Here
Part-funded by the Taiwanese government, the team have so far set up 240 traps on fruit farms around the country. Machine learning algorithms pool the continuous data arriving from each of these traps and predict when the local fruit fly population is about to explode.
To help in this prediction, the traps are also fitted with weather sensors that monitor temperature, humidity, wind speed and rainfall. Fruit fly population surges tend to match changes in weather - when it is humid, the level of insects is expected to rise, for example.
In Taiwan's current system, a red alert is issued when the number of flies caught in a trap surges beyond 1024 in a 10-day period. But the AI system can learn what counts as a normal level of fruit flies in an area and adapt its warnings on the basis of the current weather and time of year. It can also work out where the pest is likely to be breeding.
When a potentially devastating infestation is predicted, it automatically sends a text message to government officials' cellphones, providing the time, location and severity of the potential outbreak. The warning should allow authorities to pre-empt the outbreak by putting down insecticide.
Tested on historical data taken from the network of traps, the AI system was accurate in predicting an outbreak 88 per cent of the time.
- More Here
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