A Multimodal Approach for Advanced Pest Detection and Classification
It addresses pest detection for agriculture, but is incremental as it integrates existing methods like R-CNN, ResNet-18, and tiny-BERT.
This paper tackled agricultural pest detection by developing a multimodal deep learning framework that combines text and image data, achieving superior performance as indicated by ROC and AUC analyses.
This paper presents a novel multi modal deep learning framework for enhanced agricultural pest detection, combining tiny-BERT's natural language processing with R-CNN and ResNet-18's image processing. Addressing limitations of traditional CNN-based visual methods, this approach integrates textual context for more accurate pest identification. The R-CNN and ResNet-18 integration tackles deep CNN issues like vanishing gradients, while tiny-BERT ensures computational efficiency. Employing ensemble learning with linear regression and random forest models, the framework demonstrates superior discriminate ability, as shown in ROC and AUC analyses. This multi modal approach, blending text and image data, significantly boosts pest detection in agriculture. The study highlights the potential of multi modal deep learning in complex real-world scenarios, suggesting future expansions in diversity of datasets, advanced data augmentation, and cross-modal attention mechanisms to enhance model performance.