Bearbmagovva

SIT-ia: A Software-Hardware System to Improve Male Sorting Efficacy for the Sterile Insect Technique

Furkejuvvon:
Publikašuvnnas:Insects vol. 16, no. 11 (2025), p. 1108-1121
Váldodahkki: de la Vega Gerardo
Eará dahkkit: Smith, Luciano, Soria-Mercier Lihuen, Edwards, Wilson, Triñanes Federico, Masagué Santiago, Corley, Juan
Almmustuhtton:
MDPI AG
Fáttát:
Shenzhen China
China
Software
Males
Invasive species
Artificial intelligence
Deep learning
Hardware
Invasive insects
Management methods
Male sterility
Females
Data processing
Population growth
Pest control
Computer vision
Insects
Economic impact
Sterilization
Insecticides
Lasers
Neural networks
Pests
Classification
Effectiveness
Fruit flies
Sex
Algorithms
Sterilized organisms
Control methods
Environmental
Liŋkkat:Citation/Abstract
Full Text + Graphics
Full Text - PDF
Fáddágilkorat: Lasit fáddágilkoriid
Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!
Abstrákta:Invasive insects can cause significant economic impacts to agriculture worldwide and impact human health. Traditional pest management methods that include chemical insecticides have raised increasing environmental and health concerns, prompting the need for sustainable alternatives. The Sterile Insect Technique (SIT), which consists of releasing sterile males of a target pest to mate with wild females, is held as a promising solution. However, the success of SIT relies on the release of sterile males. The efficient separation of sexes prior to sterilization and release is necessary. This study presents SIT-ia, a software–hardware system that utilizes artificial intelligence (AI) and computer vision to automate the sex-sorting process. We showcase its use with the fruit fly pest D. suzukii. The system was able to identify males from females with a 98.6% accuracy, sorting 1000 sterile flies in ~70 min, which is nearly half the time involved in manual sorting by experts (i.e., ~112 min). This simple device can easily be adopted in SIT production protocols, improving the feasibility and efficacy of improved pest management practices.
ISSN:2075-4450
DOI:10.3390/insects16111108
Gáldu:Agriculture Science Database