Exploring Deep Ensemble Model for Insect and Pest Detection from Images

Abstract

Pests and insects are the main hazards to crops, and they have a significant negative impact on both human health and wealth. Identification and classification of different types of crop insects accurately is an important task to avoid these losses. A manual examination is a quite sluggish and less efficient way of accomplishing this task while computer vision technology can significantly assist in this issue. A CNN-based deep learning model can classify insects and pests efficiently. In this paper, an ensemble-based model has been proposed using transfer learning. The experimental setup comprises pre-trained models like VGG16, VGG19, and ResNetv50 with a voting classifier ensemble technique. These pre-trained models are used to train the training dataset in a parallel pipeline model which is united with Ensemble Voting Classifier to generate the final prediction over the input sample. The benchmark IP102 dataset, having more than 75000 samples over 102 classes, is used to train and evaluate the model's performance. Results show that the proposed ensemble model achieves an accuracy of 82.5%, which is significant improvement over the recent state-of-the-art models proposed over the same selected dataset, hence strongly supporting the efficacy of the model.