Abstract


 
 
 Codling moth, Cydia pomonella (Lepidoptera: Tortricidae), is a key pest of apples exported from New Zealand and is dif cult to control at levels required to ensure quarantine security demanded by many countries. Market access for New Zealand apples into countries with strict codling-moth quarantine regulations currently relies on methyl bromide fumigation combined with cold treatment (e.g. Japan) or the use of a rigorous systems approach (e.g. Taiwan). Detection of codling moth in apples would enable the very few apples in the packhouse with codling moth to be graded out. In this study, a commercially available Compac Spectrim grading system was used to determine if codling moth entry holes could be detected. This system provides high-clarity images through enhanced lighting and optics, as well as using various infrared wavelengths to target different defects and machine-learning algorithms to differentiate defects. Apples infested with first- or third-instar codling moth larvae were processed through the Spectrim machine. The system successfully identi ed 100% of apples infested with rst- instar larvae and 96% of apples infested with third-instar larvae. Additionally, damage caused by the two life stages was able to be differentiated.
 
 

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call