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<title cf:type="text"><![CDATA[Editorial Office of Journal of Environmental Entomology -->Pest Monitoring and Quarantine]]></title>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Application of UAV multispectral remote sensing for monitoring Ailanthus altissima damage]]></title>
<link><![CDATA[http://hjkcxb.alljournals.net/hjkcxben/ch/reader/view_abstract.aspx?file_no=202602031&flag=1]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[【Aim】Eucryptorrhynchus scrobiculatus (Motschulsky) and Eucryptorrhynchus brandti (Harold) are two serious wood-boring pests specifically inflicting damage on Ailanthus altissima (Mill.) Swingle. They have caused severe damage to A. altissima, a primary afforestation species used in shelterbelts in Ningxia Hui Autonomous Region. However, no efficient method exists to identify the extent of damage to individual A. altissima at the stand scale, which limits effective pest control.【Methods】This study targeted A. altissima in Caowan Village, Qingtongxia City, Ningxia, China, to investigate techniques for identifying pest damage degree caused by E. scrobiculatus and E. brandti using Unmanned Aerial Vehicle (UAV) multispectral imagery. The damage to A. altissima was classified into four levels including Healthy, Lightly, Moderately, and Severely, based on the dead twig rate. UAV multispectral imagery combined with machine learning techniques were used to construct a classification model. To compare differences in the same features across damage grades, the Multiple Wilcoxon Rank Sum Test was applied, while Analysis of Variance (ANOVA) was used to identify pest-sensitive features related to the damage caused by E. scrobiculatus and E. brandti. Furthermore, the classification performance of three machine learning models including Random Forest (RF), Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) was evaluated and compared.【Results】The results showed that the overall accuracies of RF, SVM, and KNN models were 0.866, 0.821, and 0.795, respectively, with corresponding Kappa coefficients of 0.819, 0.762, and 0.722. Among these, the RF model performed the best.【Conclusion】These findings confirmed the feasibility and effectiveness of using UAV multispectral monitoring for detecting and assessing the damage caused by E. scrobiculatus and E. brandti.]]></description>
<pubDate>2026/4/22 10:22:42</pubDate>
<category><![CDATA[Pest Monitoring and Quarantine]]></category>
<author><![CDATA[ZHANG Gen-Zhuang, ZHOU Quan, SUN Xue-Wen, REN Li-Li, WEN Jun-Bao]]></author>
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<atom:name>ZHANG Gen-Zhuang, ZHOU Quan, SUN Xue-Wen, REN Li-Li, WEN Jun-Bao</atom:name>
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<title xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="text"><![CDATA[Improvement of quarantine techniques for detecting early-stage Callosobruchus maculatus Fabricius]]></title>
<link><![CDATA[http://hjkcxb.alljournals.net/hjkcxben/ch/reader/view_abstract.aspx?file_no=202602032&flag=1]]></link>
<description xmlns:cf="http://www.microsoft.com/schemas/rss/core/2005" cf:type="html"><![CDATA[【Aim】Callosobruchus maculatus Fabricius is a quarantine pest for imported plants and agricultural plants in China, posing a severe threat to the safety of stored beans. Despite the availability of various detection methods, they are insufficient in terms of detection accuracy and operational simplicity, and they focus only on larvae, pupae, and adults, lacking a detection method for eggs, which can lead to missed identifications. This study addresses the issue of lightly infested beans failing to float in the conventional flotation method (using saturated NaCl solution), and the absence of an effective egg detection method. We aimed to optimize the quarantine detection methods for larvae and eggs of C. maculatus and provide support for improving quarantine accuracy.【Methods】The flotation characteristics of mung beans were compared in saturated NaCl and CaCl2 solutions to determine the optimal detection solution and concentration. Meanwhile, five common types of beans (mung beans, peas, soybeans, broad beans, and chickpeas) were selected and immersed in 1%, 5%, 10%, and 20% Tween 80 solutions, and different soaking time gradients were set to compare the elution rates of C. maculatus eggs from the grain surface of each treatment group.【Results】Using a 1.35 g/cm3 CaCl2 solution enhanced detection rates, particularly for early-instar larvae, advancing the earliest detection time to 24 hours after initial larval infestation. Furthermore, all concentrations of Tween 80 solution demonstrated certain efficiency, but the elution rates varied with bean type and immersion duration. Specifically, after 4 hours of immersion in 5% Tween, 80%~85% of egg masses detached from mung beans and peas, approximately 60% from soybeans and broad beans, but only 35% from chickpeas.【Conclusion】These findings suggested that floating beans in a 1.35 g/cm3 CaCl2 solution was an effective method for detecting mung beans lightly infested by C. maculatus larvae, while 5% Tween (immersion for 4 hours) was more effective for detecting eggs of C. maculatus on mung beans and peas. The research results provided valuable insights for optimizing current quarantine techniques for C. maculatus.]]></description>
<pubDate>2026/4/22 10:22:42</pubDate>
<category><![CDATA[Pest Monitoring and Quarantine]]></category>
<author><![CDATA[SHANG Ying-Jie, NI Mei-Hong, LIU Rui, MA Jun, JIANG Ming-Xing]]></author>
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<atom:name>SHANG Ying-Jie, NI Mei-Hong, LIU Rui, MA Jun, JIANG Ming-Xing</atom:name>
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