This paper presents a novel approach to plant disease recognition using deep convolutional neural networks (CNNs) for leaf image classification. The authors developed a model that can distinguish between 13 different types of plant diseases and healthy leaves, with the ability to identify plant leaves from their surroundings. The method involves gathering and preprocessing images, augmenting the dataset, and training a deep CNN using the Caffe framework. The model achieved an overall accuracy of 96.3% and top-1 accuracy of 96.3%, with top-5 accuracy reaching 99.99%. The study highlights the effectiveness of deep learning in pattern recognition and object detection, providing a valuable tool for precision agriculture and sustainable crop management. Future work includes expanding the database, fine-tuning the model, and integrating it into a mobile application for real-time disease detection.This paper presents a novel approach to plant disease recognition using deep convolutional neural networks (CNNs) for leaf image classification. The authors developed a model that can distinguish between 13 different types of plant diseases and healthy leaves, with the ability to identify plant leaves from their surroundings. The method involves gathering and preprocessing images, augmenting the dataset, and training a deep CNN using the Caffe framework. The model achieved an overall accuracy of 96.3% and top-1 accuracy of 96.3%, with top-5 accuracy reaching 99.99%. The study highlights the effectiveness of deep learning in pattern recognition and object detection, providing a valuable tool for precision agriculture and sustainable crop management. Future work includes expanding the database, fine-tuning the model, and integrating it into a mobile application for real-time disease detection.