Yoğun net kullanılarak bitki hastalıklarının tespiti

The precise and prompt identification of plant diseases (PD) is paramount in safeguarding crop productivity and fortifying global food security. As deep learning (DL) methodologies continue to evolve, the prominence of automated PD prediction mechanisms has witnessed a significant upswing. This study delves into the formulation of such an advanced system. Here, contemporary architectures, such as CNNs, DenseNet, ResNet, MobileNet, and VGG, are meticulously implemented, evaluated, and then juxtaposed against one another. Amidst these, DenseNet stands out, registering an exemplary ACC of 98%, thereby outstripping other state-of-the-art models. However, beyond mere ACC, our study extends to ensure the interpretability of the model's predictions. By integrating Explainable Artificial Intelligence (XAI) techniques, specifically the Grad-CAM approach, we strive to illuminate the decision-making pathways of the model, granting users a transparent view into its diagnostic process. This not only fortifies trust in the predictions but also provides valuable insights for potential real-world applications. In essence, this research underscores the pivotal role of automation in PD detection and demonstrates the compelling potential of blending DL with XAI for enhanced transparency and efficacy.