Leveraging computer Vision and AI for real-time crop disease detection and prevention in smallholder farming systems

This research explores the potential of leveraging computer vision and artificial intelligence (AI) to revolutionise crop disease detection and prevention specifically within smallholder farming systems. Smallholder farmers, who are critical to global food security, face significant challenges due to crop diseases that can lead to substantial yield losses. Traditional disease management methods are often inadequate, highlighting the urgent need for scalable, accurate, and timely solutions. This paper presents a conceptual framework for integrating AI-driven image recognition and data analytics to enable real-time disease detection and facilitate proactive prevention strategies tailored to the constraints of resource-limited smallholder farms. By examining existing methodologies, the applications of computer vision in agriculture, and current research gaps, this work outlines a system design, compares suitable AI models, and discusses crucial implementation considerations such as scalability, accessibility, and ethical implications. Ultimately, this paper envisions the transformative impact of AI in bolstering resilience against disease outbreaks, promoting sustainable farming practices, and ensuring global food security by empowering smallholder farmers with advanced technological tools.