Plant Scan: Advanced CNN Model for Leaf Disease Detection

Farmers around the world face significant challenges in managing crop diseases, which can drastically reduce yield and quality, leading to economic losses.Traditional methods of disease detection, relying on manual inspection by specialists, are time-consuming, prone to human error, and often unable to detect diseases at early stages.To address these issues, advanced technologies like deep learning offer promising solutions for automated, accurate, and rapid disease detection.This study presents the application of deep learning models for the detection of leaf diseases across pepper bell, tomato, and potato crops.Specifically, we evaluate the effectiveness of several well-established models, including VGG19, DenseNet201, ResNet50, and AlexNet, for classifying 15 disease types using the PlantVillage dataset.In addition to these pre-trained models, we introduce our new convolutional neural network, Plant Scan, designed to address the unique challenges of plant disease detection.Plant Scan optimizes the balance between computational efficiency and high classification accuracy, making it a practical tool for real-world agricultural applications.The models were evaluated on various performance metrics such as precision, recall, F1 score, and classification accuracy, using Python along with Keras and TensorFlow frameworks on a GPU-equipped system.The results provide valuable insights into the strengths and limitations of these models, helping to create reliable tools for early disease detection, ultimately improving crop management and enhancing agricultural productivity.