<p>Plant diseases, environmental stress, and climate-related challenges have negatively impacted agricultural productivity, leading to the growing need for intelligent, real-time agricultural monitoring systems. Despite the progress achieved in deep learning, most existing methods are restricted by their dependence on RGB-only images, constrained contextual reasoning, and reliance on cloud-based inference, which hinders their use in remote or bandwidth-restricted field environments. This study introduces a novel Edge Integrated Vision Transformer with Cross Spectral Attention Fusion (EdgeViT-CSAF) designed to achieve reliable and precise analysis of seed and crop health using image data from RGB, Near-Infrared (NIR), and thermal spectrum. The innovation lies in merging cross-spectral attention with a transformer framework for global context learning and enhancing real-time edge deployment via quantization and knowledge distillation. This information includes balanced pseudo-3D alignments to fully utilize the properties of volumetric data, employing a two-stream model with a dynamic spectral attention method to derive channel-wise features by emphasizing the significance of FrameWise features to improve the ultimate prediction. This allows for edge-level deployment combined with quantization and knowledge distillation methods, leading to a lightweight yet high-performing solution appropriate for IIoT devices. The experimental outcomes of the suggested frameworks demonstrate a classification accuracy of 99.1% and a 28% enhancement in inference time compared to conventional backbone models such as Efficient-CNN and ResNet. The model demonstrated improved resilience against class imbalance and visually comparable disease categories as well. Our research enhances the performance of deep learning applications in agriculture, enabling robust, scalable, and edge-compatible AI systems that provide real-time crop insights within real-world limitations.</p>