Deep Learning for Precision Agriculture: Detecting Tomato Leaf Diseases with VGG-16 Model

Artificial intelligence (AI), automation, and the Internet of Things (IoT) have transformed modern agricultural operations, particularly crop management and disease detection.Plant disease diagnosis and environmental monitoring have grown more accurate and efficient because of machine learning and deep learning techniques such as convolutional neural networks (CNNs).In the context of precision agriculture, this paper investigates the widely recognized CNN architecture VGG-16, which has been specially tailored for detecting tomato leaf diseases.Through meticulous experimentation, our proposed model has showcased an impressive accuracy rate of 99.2%, alongside a remarkable f1 score of 99.499.These findings demonstrate the effectiveness of deep learning techniques in the early identification of plant diseases, allowing prompt therapeutic interventions.The results of this study open the door for deep learning-driven disease detection systems to be widely used in agriculture, with the promise of increased crop yields and the encouragement of sustainable farming methods.This work advances precision agriculture by tackling the problems caused by tomato leaf diseases.It also emphasizes the value of using cutting-edge technologies to address pressing agricultural difficulties.