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首页> 外文期刊>American journal of applied sciences >A Reliable Identification System for Red Palm Weevil | Science Publications
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A Reliable Identification System for Red Palm Weevil | Science Publications

机译：红掌象甲的可靠识别系统科学出版物

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> Problem statement: Red Palm Weevil (RPW) is a widely found pest among palm trees and is known to cause significant losses every year to palm growers. Existing identification techniques for RPW comprise of using traps with pheromones to detect these pests. However, these traditional methods are labor-intensive, expensive to implement and unreliable for early detection of RPW infestation. Early detection of these pests would provide the best opportunity to eradicate them and minimize the potential losses of palm trees. Approach: In this study, a reliable identification system is developed to identify RPW by using only a small number of image descriptors in combination with neural network models. The neural networks were developed by using between three to nine image descriptors as inputs and a large database of insects? images was used for training. Three different training ratios ranging from 25-75% were used and the network was trained by two different algorithms. Further, several scenarios were formulated to test the efficacy and reliability of the newly developed identification system. Results: The results indicate that the identification system developed in this study is capable of 100% recognition of RPW and 93% recognition of other insects in the database by taking as input only three easily-calculable image descriptors. Further, the average training times for these networks was 13 sec and the testing time for a single image was only 0.015 sec. Conclusion: The new system developed in this study provided reliable identification for RPW and was found to be up to 14 times faster in training and three times faster in testing of insects? images.
机译： > 问题陈述：红棕榈象鼻虫（RPW）是在棕榈树中广泛发现的害虫，并且已知每年对棕榈种植者造成重大损失。 RPW的现有识别技术包括使用具有信息素的诱捕器来检测这些害虫。但是，这些传统方法劳动强度大，实施成本高并且对于RPW侵染的早期检测不可靠。尽早发现这些有害生物将为根除这些有害生物提供最大的机会，并最大程度地减少棕榈树的潜在损失。 方法：在这项研究中，开发了一种可靠的识别系统，通过仅使用少量图像描述符和神经网络模型来识别RPW。通过使用三到九个图像描述符作为输入和大型昆虫数据库来开发神经网络。图像用于训练。使用了从25％到75％的三种不同的训练比率，并且通过两种不同的算法训练了网络。此外，制定了几种方案来测试新开发的识别系统的功效和可靠性。 结果：结果表明，通过仅输入三个易于计算的图像描述符作为输入，本研究开发的识别系统能够100％识别RPW，识别93％数据库中的其他昆虫。此外，这些网络的平均训练时间为13秒，单个图像的测试时间仅为0.015秒。 结论：在这项研究中开发的新系统为RPW提供了可靠的识别，发现其训练速度快14倍，昆虫测试速度快3倍？图片。

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