Authors:
P. Malathi, T. Pranavadit, R. V. Vishal, M. A. Bala Kumar, V. Sanjai, Bhopendra Singh
Addresses:
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Engineering, Amity University Dubai, Dubai, 345019, United Arab Emirates.
Abstract:
Crop diseases are a problem for agricultural output because they cause a variety of challenges, which frequently lead to financial losses and food poverty. It is essential to identify plant diseases and classify them accurately in the early stages of infection to execute interventions promptly. This study presents a system driven by artificial intelligence (AI) and employing deep learning techniques for the real-time identification and forecasting of crop diseases. The model analyses visual symptoms from photos of crop leaves to determine the types of diseases present. It does this with a high degree of accuracy by utilising convolutional neural networks (CNNs) under the TensorFlow framework. To ensure the system is robust enough to be used across a wide range of crop types and disease categories, it is trained on a heterogeneous dataset. The objective of this research is to assist farmers and agricultural professionals in identifying diseases more efficiently by automating the process. This will enable more informed decision-making, which in turn will improve crop health management, encourage sustainable farming techniques, and enhance food security.
Keywords: Plant Disease Detection; Convolutional Neural Networks (CNN); Deep Learning; Image Classification; Smart Agriculture; Real-Time Inference; Web-Based Deployment; Flask Framework.
Received: 01/08/2024, Revised: 18/11/2024, Accepted: 25/12/2024, Published: 03/03/2025
DOI: 10.64091/ATIHL.2025.000120
AVE Trends in Intelligent Health Letters, 2025 Vol. 2 No. 1 , Pages: 52-61