This abstract presents a detailed exploration of deep learning techniques for crop image classification, with a specific focus on AlexNet, LeNet, Vision Transformers (ViT-B/8, ViT-B/16, ViT-B/32, ViT-S/16) and Transfer Learning Models (VGG16, VGG19, Xception, Inception). A dedicated dataset, meticulously crafted for assessing paddy crop health, forms the basis of this study. The research rigorously examines various deep learning models, with the investigation extending to their architectures, activation functions, and the intricacies of the training processes employed. The results not only showcase the performance of each model but also underscore their potential contributions to crop image classification in precision agriculture. The study emphasizes the practical implications of the distinct deep learning models for the specific purpose of enhancing precision agriculture practices. By presenting a detailed comparison of various models, this study aims to provide insights for developers and practitioners in the field of computer vision. The findings can guide the selection of optimal models for crop health assessment in precision agriculture. The presented results affirm the role of these models as key contributors to the evolving landscape of agricultural technology.