首页> 外文期刊>Computers and Electronics in Agriculture >Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning
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Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning

机译:使用可见的近红外高光谱成像技术识别和诊断Trogoderma种植细胞和Trogoderma变形的碎片,以及深度学习

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摘要

The khapra beetle, Trogoderma granarium Everts, is the most critical biosecurity pest threat which threatens the grains industry worldwide. To prevent incursion of the khapra beetle, very accurate and reliable diagnostic tools are required to differentiate the khapra beetle from other morphologically, closely related Trogoderma sp., in particular the larva stage. However, at present, it can only be identified by highly skilled taxonomists. Furthermore, often suspected Trogoderma sp. found in grain products are the body fractions such as larval skins or fragmented adult, which are impossible to diagnose morphologically. This work explored the combination of visible near infrared hyperspectroscopy (VNIH) and deep learning tools to identify the khapra beetle. About 2000 hyperspectral images were acquired under this study. Images of T. granarium and Trogoderma variabile, adult, larvae, larvae skin, fragments of adult and larvae images, were subjected to two deep learning models; Convolutional Neural Networks (CNN) and Capsule Network for analysis. Overall, above 90% accuracy was obtained with both models, whereas Capsule Network achieved a higher accuracy of 96%. For whole adult body and adult fragments, the accuracy achieved was 96.2% and 91.7%, respectively. For whole larvae, larvae skin and larvae fragment, accuracies of 93.4%, 91.6%, and 90.3% were achieved. Ventral orientation gave better accuracy over dorsal orientation of the insects for both larvae and adult stages. Based on the above results, VNIH imaging technology coupled with appropriate machine learning tools can be used to identify one of the most notorious stored grain pests, the khapra beetle, from other morphologically similar Trogoderma sp like T. variabile. Particularly, the technology offers a new approach and possibility of an effective identification of Trogoderma sp. from its body fragments and larvae skins, which are otherwise impossible to diagnose taxonomically.
机译:Khapra Beetle,Trogoderma Manarium Everts是最关键的生物安全威胁威胁全世界谷物行业。为防止喀喇叭甲虫的入侵,需要非常准确和可靠的诊断工具来区分哈普拉甲虫与其他形态,密切相关的Trogoderma Sp。,特别是幼虫阶段。然而,目前,它只能由高技能的分类家识别。此外,经常怀疑Trogoderma sp。在谷物产品中发现是幼虫皮肤或碎片化成人的体级,这是不可能诊断形式的。这项工作探索了可见的近红外高光谱(VNIH)和深层学习工具的组合来识别Khapra Beetle。在这项研究中获得了大约2000年的高光谱图像。 T.种植植物和Trogoderma变形,成人,幼虫,幼虫皮肤,成人和幼虫图像的片段进行了两个深入学习模型;卷积神经网络(CNN)和分析胶囊网络。总的来说,通过两种型号获得高于90%的精度,而胶囊网络达到96%的更高精度。对于整个成年体和成年片段,所取得的准确性分别为96.2%和91.7%。对于全幼虫,幼虫皮肤和幼虫片段,达到93.4%,91.6%和90.3%的精度。腹向取向对幼虫和成人阶段的昆虫背面的更好准确性。基于上述结果,与适当的机器学习工具相结合的VNIH成像技术可用于识别最臭名昭着的储存谷物害虫,KHAPRA BEEETLE,来自其他形态学上类似的Trogoderma SP如T. Variabile。特别是,该技术提供了一种新的方法和有效识别Trogoderma SP的可能性。从其体片段和幼虫皮肤,否则不可能诊断分类。

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