PDF(Pubmed)
Abstract：
Using artificial intelligence (AI) and the IoT (Internet of Things) is a primary focus of applied engineering research to improve agricultural efficiency. This review paper summarizes the engagement of artificial intelligence models and IoT techniques in detecting, classifying, and counting cotton insect pests and corresponding beneficial insects. The effectiveness and limitations of AI and IoT techniques in various cotton agricultural settings were comprehensively reviewed. This review indicates that insects can be detected with an accuracy of between 70 and 98% using camera/microphone sensors and enhanced deep learning algorithms. However, despite the numerous pests and beneficial insects, only a few species were targeted for detection and classification by AI and IoT systems. Not surprisingly, due to the challenges of identifying immature and predatory insects, few studies have designed systems to detect and characterize them. The location of the insects, sufficient data size, concentrated insects on the image, and similarity in species appearance are major obstacles when implementing AI. Similarly, IoT is constrained by a lack of effective field distance between sensors when targeting insects according to their estimated population size. Based on this study, the number of pest species monitored by AI and IoT technologies should be increased while improving the system\'s detection accuracy.
摘要：
使用人工智能（AI）和物联网（IoT）是提高农业效率的应用工程研究的主要重点。这篇综述论文总结了人工智能模型和物联网技术在检测、分类，统计棉花害虫和相应的益虫。全面回顾了人工智能和物联网技术在各种棉花农业环境中的有效性和局限性。这篇综述表明，使用摄像头/麦克风传感器和增强的深度学习算法，可以以70％至98％的精度检测昆虫。然而,尽管有许多害虫和有益的昆虫，只有少数物种被AI和物联网系统作为检测和分类的目标。毫不奇怪,由于识别未成熟和掠夺性昆虫的挑战，很少有研究设计系统来检测和表征它们。昆虫的位置,足够的数据大小，集中在图像上的昆虫，物种外观的相似性是实施人工智能时的主要障碍。同样，当根据昆虫的估计种群大小瞄准昆虫时，物联网受到传感器之间缺乏有效场距离的限制。基于这项研究，应增加AI和物联网技术监测的害虫物种数量，同时提高系统的检测精度。
