利用无人机、传感器和图像分析检测蚕蛾(鞘翅目:Coccinellidae)对豆类的危害。

Roghaiyeh Karimzadeh, Kushal Naharki, Yong-Lak Park
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摘要

墨西哥豆甲虫 Epilachna varivestis Mulsant(鞘翅目:Coccinellidae)是豆类的主要害虫,早期发现豆类的损害对于及时防治 E. varivestis 至关重要。本研究旨在评估使用无人机和光学传感器来量化豆角夜蛾对大田豆角造成的危害的可行性。共对 14 块不同落叶程度的豆类地块进行了空中调查,无人机配备了红蓝绿(RGB)、多光谱和热传感器,距离豆类地块冠层 2 至 20 米。地面验证取样包括收割整块蚕豆地并拍摄单个叶片。图像分析用于量化 E. varivestis 在航空图像和地面验证照片上取食的落叶量。线性回归分析用于确定在航空图像上测量的 E. varivestis 造成的豆类落叶与地面验证发现的落叶之间的关系。研究结果表明,通过地面验证和使用 RGB 图像评估的豆类损害之间存在显著的正相关关系,而豆类实际落叶量与归一化差异植被指数值之间存在显著的负相关关系。没有检测到与豆类落叶有关的热特征。利用地理统计学进行的空间分析表明,E. varivestis 对豆类落叶的影响与空间有关。这些结果表明,在离树冠 2 到 6 米的飞行高度上使用 RGB 和多光谱传感器可用于早期检测和针对具体地点管理 E. varivestis,从而提高管理效率。
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Detection of bean damage caused by Epilachna varivestis (Coleoptera: Coccinellidae) using drones, sensors, and image analysis.

The Mexican bean beetle, Epilachna varivestis Mulsant (Coleoptera: Coccinellidae), is a key pest of beans, and early detection of bean damage is crucial for the timely management of E. varivestis. This study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis. A total of 14 bean plots with various levels of defoliation were surveyed aerially with drones equipped with red-blue-green (RGB), multispectral, and thermal sensors at 2 to 20 m above the canopy of bean plots. Ground-validation sampling included harvesting entire bean plots and photographing individual leaves. Image analyses were used to quantify the amount of defoliation by E. varivestis feeding on both aerial images and ground-validation photos. Linear regression analysis was used to determine the relationship of bean defoliation by E. varivestis measured on aerial images with that found by the ground validation. The results of this study showed a significant positive relationship between bean damages assessed by ground validation and those by using RGB images and a significant negative relationship between the actual amount of bean defoliation and Normalized Difference Vegetation Index values. Thermal signatures associated with bean defoliation were not detected. Spatial analyses using geostatistics revealed the spatial dependency of bean defoliation by E. varivestis. These results suggest the potential use of RGB and multispectral sensors at flight altitudes of 2 to 6 m above the canopy for early detection and site-specific management of E. varivestis, thereby enhancing management efficiency.

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