ConvViT Driven Multi Context Feature Fusion for Sustainable Pest Monitoring in Agriculture

In the last few years, the union of modern imaging technology and AI has given rise to agriculture. Probably the most promising of its uses is AI-powered models in agricultural pest imaging, giving new meaning to pest identification, categorization, and monitoring. The world’s food security and farming yields are at risk are endangered by pests, and, too often, this necessitates undue need for pesticides that degrade the environment and the health of people. AI can be brought into play for detecting pests in a new way before they turn invasive, relying less on chemicals and perhaps even ushering in sustainable agricultural methods. Deep learning (DL), a subfield of AI especially designed for image recognition, has seemed especially promising, particularly in the highly precise and highly productive automation of pest detection. In this study, the hybrid model known as ConvViT (fusing the local detail extraction strength of Convolutional Neural Networks (CNNs) with the global contextual reasoning power of Vision Transformers (ViTs)) is introduced. To address the challenges from real-world datasets such as background clutter and image quality, viewpoint differences, as well as other exceptions, ConvViT is developed to boost pest classification performance. The proposed framework is based on a framework that shows superior accuracy than traditional models like ResNet50, EfficientNetB3, and standalone ViTs using a curated agricultural pest image dataset. This approach is an aligned, scalable, intelligent solution for next-generation crop protection by presenting a set of AI capabilities aligned with sustainable agriculture objectives.