Abstract: The agricultural industry faces significant challenges in maintaining crop health and productivity due to plant diseases. Traditional methods of plant disease detection are labor-intensive, time-consuming, and often prone to human error. With advancements in artificial intelligence (AI), particularly deep learning (DL) algorithms, automated plant disease detection has emerged as a powerful tool for precision agriculture. This paper explores the application of deep learning techniques, such as convolutional neural networks (CNNs), for detecting plant diseases through image analysis, highlighting the efficiency, accuracy, and scalability of these methods in real-time agricultural scenarios. We also discuss the integration of deep learning models with smart farming technologies, offering a comprehensive solution for early disease detection and intervention.

Keywords: Automated Plant Disease Detection, Precision Agriculture, Deep Learning, Image Classification, Convolutional Neural Networks (CNNs), Transfer Learning, Data Augmentation, Model Training and Evaluation, Real-Time Detection.

| DOI: 10.17148/IARJSET.2024.11905