A new technology would allow olive producers to identify when the olive fruit fly is present in their groves and react accordingly.
The system, which was developed by Citoliva and Inoleo, is comprised of an opto-electric fly sensor and communication network. This allows data about Spain’s most prolific olive-related pest to be gathered, synthesized and easily visualized on a smartphone, tablet or computer.
“The sensor works by comparing the spectral frequency of the flutter of the insect and comparing it with the pattern of that of the olive fly,” Carmen Capiscol, a member of the research, development and innovation team at Citoliva, said. “Then deciding whether it is in fact the olive fly or not.”
Data from the separate sensors are collected and uploaded onto the Cloud, where they are combined with an integrated pest control system. Temperature and time data are also recorded and stored in the integrated pest management tool.
“With the data, a spatial decision support system identifies when and where to start the system and activates it,” Capiscol said. “When a fixed temperature threshold is crossed, the degree-day growth is calculated and estimates the time at which the first fly peak will appear.”
Olive producers would be able to identify when the olive fruit fly is present and react accordingly. The system’s developers believe this would reduce energy consumption involved with monitoring for the fly as well as lead to a more pragmatic application of pest control measures.
The sensor would be placed inside of a modified McPhail trap, an inverted funnel with a transparent bell on top. Flies crawl through the funnel and are drawn to the combination of light and a pheromone, which is placed at the top of the transparent bell. This combination keeps the fly attracted until it runs out of energy and subsequently drowns in a dish of soapy water placed on top of the inverted funnel.
Unlike traditional McPhail traps, which indiscriminately capture flies, this specialized one would only open when the sensor identified the approaching fly as an olive fruit fly.
When the system was tested in a laboratory earlier this year, it correctly identified the olive fruit fly 91 percent of the time. The system then correctly synthesized and sent the appropriate data to the Cloud, 95 percent of the time. More