National Taiwan University Uses Artificial Intelligence To Fight Pests
National Taiwan University has developed an Artificial Intelligence based technology to fight against pests. The system is of utmost importance in places like Taiwan which is flooded by fruit flies posing a threat to agriculture. The system includes trap which contains infrared beams and bait to attract flies. The beam helps to keep a track of flies trapped - by increasing the count by one, each time the beam is broken by a fly. The trap also contains weather sensors to monitor temperature conditions. The result collected are sent every 30 minutes, to a local station via radio.
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Machine learning algorithm collects data from each of the traps and predicts when the insect count is expected to cross the alarming range. The system is capable of predicting safe number of insects by weather conditions, area and time of year; as number of insects in humidity is expected to be higher, hence the system manages with an increased count of insects in humidity considering it as a normal insect level. In case, an alarming level of pests is detected by the technology, it automatically sends message to government officials'Â cellphones indicating time, location and severity of condition. The authorities can then take desired steps to curb the situation.
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Machine learning algorithm collects data from each of the traps and predicts when the insect count is expected to cross the alarming range. The system is capable of predicting safe number of insects by weather conditions, area and time of year; as number of insects in humidity is expected to be higher, hence the system manages with an increased count of insects in humidity considering it as a normal insect level. In case, an alarming level of pests is detected by the technology, it automatically sends message to government officials'Â cellphones indicating time, location and severity of condition. The authorities can then take desired steps to curb the situation.
Via:Â #-Link-Snipped-# | Image credits : #-Link-Snipped-#
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