Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.
A 45 d pot experiment was conducted to examine the effects of silicon fertilizer or iron fertilizer on the growth of two typical Ipomoea aquatica cultivars(Daye and Liuye) and arsenic(As) accumuation of Daye and Liuye grown in As-contaminated soils at different As dosage levels. The results showed that the application of these two fertilizers generally enhanced the growth of the plants, which may be partly attributable to the reduction in As toxicity. The addition of these two fertilizers also significantly reduced the uptake of As by the plants though the iron fertilizer was more effective, as compared to the silicon fertilizer. The accumulation of As in shoot portion was weaker for Daye than for Liuye. The research findings obtained from this study have implications for developing cost-effective management strategies to minimize human health impacts from consumption of As-containing I. aquatica.
