With the wide application of machine vision technology in agricultural fields, the image-based pests diagnosis of rice planthoppers becomes a fast and effective approach. Although the effective automatic segmentation is a very important pretreatment technology for the analysis of rice planthopper images, the traditional graph cuts based active contour method has the shrinking bias problem during segmentation. This paper proposes an innovative approach to overcome that problem. By changing bidirection dilation of the contours to inside direction dilation to improve the overlap of adjacent contour neighborhoods and reduce the computation scale, the shrinking bias problem is improved effectively. The result shows that the approach adopted in this paper can clearly segment the contour of rice planthoppers.
Accurate records and prediction of the number of the rice planthopper's outbreaks and the environmental information of
farmland are effective measures to control pests' damages. On the other hand, a new round of technological revolution
from the Internet to the Internet of things is taking place in the field of information. The application of the Internet of
things in rice planthopper and environmental online monitoring is an effective measure to solve problems existing in the
present wired sensor monitoring technology. Having described the general framework of wireless sensor nodes in the
Internet of things in this paper, the software and hardware design schemes of wireless sensor nodes are proposed,
combining the needs of rice planthopper and environmental monitoring. In these schemes, each module's design and key
components' selection are both aiming to the characteristics of the Internet of things, so it has a strong practical value.
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