<p>Deep Neural Networks (DNNs) have emerged as a efficient tool for handling challenging tasks in variety of fields due to their ability to assimilate hierarchical representations from data. Among DNNs, Static Neural Networks (SNNs) possess a fixed architecture with a predetermined number of layers, nodes, and connections, remaining unchanged during both training and inference. While effective for well-defined and stable problems, static networks often struggle to adapt to dynamic and variable inputs. In contrast, Dynamic Neural Networks (DyNNs) can modify their structure or computational pathways during inference, enabling them to adaptively process diverse inputs and improve performance in terms of accuracy, efficiency, expressive capability, and versatility. This study presents a comparative analysis of static neural networks and DyNNs in the field of plant disease detection, highlighting their architectural differences and their impact on learning and generalization for various plant diseases. This paper presents a detailed investigation of representative architectures such as DenseNet and CondenseNet and their applications across various domains primarily focusing plant diseases. Furthermore, both qualitative and quantitative analyses of research publications are presented to illustrate trends, datasets, and evaluation metrics. The study concludes that DyNNs offer significant advantages over static networks, yet further research is necessary to enhance their robustness, efficiency, and applicability to real-world, dynamic scenarios across fields including Computer Vision, Natural Language Processing, Robotics, Healthcare, and plant disease detection.</p>