Author(s):
The early detection of crop diseases is crucial for ensuring food security and maximizing agricultural productivity. Traditional methods of disease identification often involve manual inspection, which is time-consuming and prone to human error. Artificial Intelligence (AI), specifically machine learning (ML) and computer vision technologies, has emerged as a transformative solution for detecting crop diseases quickly and efficiently. This paper explores the role of AI in revolutionizing disease detection in agriculture. It examines the integration of AI models with sensors, drones, and satellite imagery to monitor crops and identify early signs of disease. The use of deep learning algorithms in image analysis allows for accurate disease identification, even at early stages, when interventions can be most effective. The paper discusses various AI-based tools and platforms, their accuracy, benefits, and challenges, as well as future prospects for integrating AI into precision agriculture. Through a review of case studies and current trends, this paper highlights the potential of AI to enhance the sustainability and productivity of modern farming practices.
