Plant diseases pose a major threat to global food security, causing significant reductions in crop yield and quality. Traditional methods of disease detection rely on manual inspection by experts, which is time-consuming, labor-intensive, and often inaccurate due to human error. Recent advancements in Artificial Intelligence (AI) have introduced real-time plant disease detection systems that offer rapid, accurate, and cost-effective solutions. These systems integrate computer vision and deep learning algorithms, particularly convolutional neural networks (CNNs), to analyze plant images and identify disease symptoms at early stages. By leveraging large datasets of annotated images, AI models can classify multiple plant species and disease types with high precision. Furthermore, the incorporation of Internet of Things (IoT) devices and edge computing enables real-time monitoring directly in the field, reducing latency and dependence on constant internet connectivity. This approach supports early intervention, minimizing crop losses and reducing the need for excessive pesticide use, which benefits both the environment and farmers’ economic sustainability. Challenges remain in terms of dataset diversity, image quality under varying environmental conditions, and model generalization across different geographical regions. However, continuous improvements in AI algorithms, transfer learning, and mobile deployment are making real-time plant disease detection increasingly practical. Ultimately, AI-based detection systems represent a transformative technology in precision agriculture, contributing to food security, sustainable farming, and efficient resource management.
Mohammed et al. (Tue,) studied this question.