Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops

Abstract

Cotton is one of the world’s most economically significant agricultural products; however, it is susceptible to numerous pest and virus attacks during the growing season. Pests (whitefly) can significantly affect a cotton crop, but timely disease detection can help pest control. Deep learning models are best suited for plant disease classification. However, data scarcity remains a critical bottleneck for rapidly growing computer vision applications. Several deep learning models have demonstrated remarkable results in disease classification. However, these models have been trained on small datasets that are not reliable due to model generalization issues. In this study, we first developed a dataset on whitefly attacked leaves containing 5135 images that are divided into two main classes, namely, (i) healthy and (ii) unhealthy. Subsequently, we proposed a Compact Convolutional Transformer (CCT)-based approach to classify the image dataset. Experimental results demonstrate the proposed CCT-based approach’s effectiveness compared to the state-of-the-art approaches. Our proposed model achieved an accuracy of 97.2%, whereas Mobile Net, ResNet152v2, and VGG-16 achieved accuracies of 95%, 92%, and 90%, respectively.

New Trends and Challenges in Precision and Digital Agriculture

“…In this regard, deep learning methods [29] such as convolutional neural networks (CNNs) are widely used. CNNs are used, among others, in the popular image detection algorithm-YOLO (You Only Look Once) [30][31][32][33]. In addition, CNNs can be successfully used to map various crops [34] or predict soil moisture in vegetated areas [35].…”

Intelligent Cotton Pest and Disease Detection: Edge Computing Solutions with Transformer Technology and Knowledge Graphs

“…KG Integration = Transformer Output + KG Embedding (4) In this equation, KG Integration represents the output after integrating the knowledge graph, Transformer Output is the output of the Transformer model, and KG Embedding is the embedding representation of the knowledge graph. Overall, the application of the Transformer model in the scenario of cotton pest identification offers a new perspective [27]. The combination of self-attention mechanisms and knowledge graphs allows the model to not only process complex image data but also utilize expert knowledge to improve recognition accuracy.…”

Identification of cotton pest and disease based on CFNet- VoV-GCSP -LSKNet-YOLOv8s: a new era of precision agriculture

“…(2023) introduced MRF-YOLO, a deep learning method with multi-receptive field extraction based on YOLOX, integrating a small target detection layer to enhance precision. Jajja et al. (2022) proposed a Compact Convolutional Transformer (CCT)-based approach is to classify the image dataset, achieving an impressive accuracy of 97.2% and proving its effectiveness compared to state-of-the-art approaches.…”