基于卷积神经网络的病虫害识别与远程监测研究进展OA

In recent years,intensified environmental degradation and the ongoing invasion of harmful alien species have increasingly disrupted forest ecosystem balance.As a result,forest pest infestations have become more frequent,widespread,and persistent,posing serious threats to forest health and ecological security.These pest outbreaks cause extensive damage to forest resources and undermine the sustainable development of ecological systems.However,due to the vast distribution and complex terrain of forested regions,achieving large-scale,real-time,and efficient pest monitoring presents considerable challenges.Traditional monitoring approaches,which rely primarily on manual inspections,are characterized by low efficiency,high labor intensity,and significant resource consumption,thus limiting the effectiveness of pest prevention and control efforts.In this context,integration convolutional neural network models with remote sensing technology and embedded point-source trapping systems present a promising approach for achieving intelligent,automated,and visualized pest monitoring.Image-based object detection combined with remote monitoring technologies offers an effective means to improve pest detection accuracy,data transmission efficiency,and monitoring coverage.The fusion of CNNs with remote sensing observation and embedded detection devices enables the dynamic acquisition and recognition of pest data under complex environmental conditions,which is essential for building modern intelligent forest monitoring systems.With the rapid advancement of CNN algorithms,embedded computing platforms,and sensor technologies,the application of these integrated methods in agricultural and forestry pest identification has grown significantly in recent years.This study systematically reviews the development and application status of pest identification technologies in agroforestry,summarizes the current challenges in dataset acquisition,construction,and target detection,and analyzes the technical difficulties associated with occlusion,overlapping targets,and variable lighting conditions.Furthermore,it provides a detailed introduction to two primary technical pathways for achieving remote recognition and monitoring visualization:embedded point-source trapping systems and remote sensing observation platforms.In addition,this study discusses methodological principles,technical advantages,and inherent limitations of convolutional neural networks in remote pest monitoring,and further explores future development directions,including integrated monitoring based on multi-source data fusion,lightweight model design,and more user-friendly visualization interfaces,with the aim of enhancing the system's intelligence and practical applicability.