Deep Learning VGG19 Model for Precise Plant Disease Detection

Background: Agriculture is very important for human existence since time immemorial. Approximately sixty percent of the world’s population is dependent on agriculture and its allied activities. Due to plant diseases every year farmers bear heavy economic loss which can be reduced by having an early detection system for plant disease. Conventional plant disease detection techniques are laborious and based on chemical and analytical testing. In this work, we suggest a deep learning method to precisely detect plant diseases by applying the VGG19 convolutional neural network. Methods: Modifications specifically designed for the classification of plant diseases were applied to the VGG19 model. By first freezing the bottom layers of the pre-trained VGG19 model-which had been trained on the ImageNet dataset-and then fine-tuning the upper layers to better fit the PlantVillage dataset, transfer learning was used. Resizing, standardization and data augmentation are a few of the image preprocessing approaches that were used to increase the variety of the dataset and boost model performance. The efficacy of the model was assessed through the use of metrics like F1-score, recall, accuracy and precision. Result: The constructed model performed well in the classification of plant diseases, with over 95% accuracy on the test set. The success of the model in generalizing across different plant disease categories was largely attributed to the application of transfer learning and data augmentation. The findings show that deep learning techniques-in particular, the use of VGG19-can significantly enhance agricultural practices and decision-making processes by helping to quickly and accurately identify plant illnesses. VGG9-based image processing offers an accurate and automated solution for plant disease identification, achieving 98% accuracy in classifying healthy and diseased leaves. By integrating mobile applications, drones and smart farming cameras, farmers can detect diseases early and take timely action, improving crop health and yield. Future advancements in dataset expansion and real-time processing will further enhance its effectiveness in precision agriculture.