Insect pests are among the most critical factors affecting crops and result in a severe reduction in food yield. At the same time, early and accurate identification of insect pests can assist farmers in taking timely preventative steps to reduce financial losses and improve food quality. However, the manual inspection process is a daunting and time-consuming task due to visual similarity between various insect species. Moreover, sometimes it is difficult to find an experienced professional for the consultation. To deal with the problems of manual inspection, we have presented an automated framework for the identification and categorization of insect pests using deep learning. We proposed a lightweight drone-based approach, namely a custom CornerNet approach with DenseNet-100 as a base network. The introduced framework comprises three phases. The region of interest is initially acquired by developing sample annotations later used for model training. A custom CornerNet is proposed in the next phase by employing the DenseNet-100 for deep keypoints computation. The one-stage detector CornerNet identifies and categorizes several insect pests in the final step. The DenseNet network improves the capacity of feature representation by connecting the feature maps from all of its preceding layers and assists the CornerNet model in detecting insect pests as paired vital points. We assessed the performance of the proposed model on the standard IP102 benchmark dataset for pest recognition which is challenging in terms of pest size, color, orientation, category, chrominance, and lighting variations. Both qualitative and quantitative experimental results showed the effectiveness of our approach for identifying target insects in the field with improved accuracy and recall rates.

害虫是影响作物的最关键因素之一，会导致粮食产量严重下降。同时，早期准确地识别害虫可以帮助农民及时采取预防措施，减少经济损失，提高食品质量。然而，由于各种昆虫物种之间的视觉相似性，人工检查过程是一项艰巨且耗时的任务。此外，有时很难找到有经验的专业人士进行咨询。为了解决人工检查的问题，我们提出了一个使用深度学习识别和分类害虫的自动化框架。我们提出了一种基于无人机的轻量级方法，即以 DenseNet-100 作为基础网络的自定义 CornerNet 方法。引入的框架包括三个阶段。感兴趣的区域最初是通过开发稍后用于模型训练的样本注释来获取的。通过使用 DenseNet-100 进行深度关键点计算，在下一阶段提出了自定义 CornerNet。一级检测器 CornerNet 在最后一步识别和分类几种害虫。DenseNet 网络通过连接其所有先前层的特征图来提高特征表示的能力，并协助 CornerNet 模型将害虫检测为成对的生命点。我们在用于害虫识别的标准 IP102 基准数据集上评估了所提出模型的性能，这在害虫大小、颜色、方向、类别、色度和照明变化方面具有挑战性。