Leaf disease detection from Pepper images using transfer learning

Automated detection and classification of plant diseases are essential for ensuring crop health and productivity. Various sensors are used to monitor plant health, which is then analyzed using advanced techniques. This research presents a method for detecting and classifying pepper diseases through the utilization of convolutional neural networks with transfer learning. The study utilized images of pepper leaves from the PlantVillage dataset. By employing state-of-the-art pre-trained convolutional neural networks and customizing them for our dataset, our results outperformed previous studies. Our approach achieved a notable ۱۰۰% accuracy rate for test data, surpassing existing methodologies. This highlights the precision of our method in identifying and categorizing diseases in pepper plants. Furthermore, our research emphasizes the importance of integrating advanced analytical approaches to enhance disease management in agriculture.

Keywords:

precision agriculture , deep learning , machine learning , disease detection