Plants Disease Classification Using CNN

Diseases play a critical role in affecting food production, often leading to significant reductions in yield. Therefore, the prompt and accurate identification of crop diseases is crucial to minimizing these losses and ensuring agricultural productivity. Beneath every diseased leaf lies a hidden battle, silently undermining global food security and threatening the livelihoods of millions, especially in resource-constrained regions. Traditional methods of disease identification are often slow, subjective, and reliant on infrastructure that many farmers lack. Advances in deep learning and the increasing availability of digital tools, however, have opened new avenues for tackling these challenges. This research introduces a hybrid approach combining both convolutional neural networks and transfer learning to identify crop diseases with unprecedented precision. Leveraging a curated dataset of diverse plant images, our model achieves 93% accuracy during training and 95% accuracy on independent testing, significantly outperforming traditional methods.