Custom CornerNet: a drone-based improved deep learning technique for large-scale multiclass pest localization and classification

Abstract

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.