In the ever-evolving landscape of agricultural technology, a groundbreaking development has emerged that promises to revolutionize disease detection and monitoring in strawberry crops. Researchers have introduced a lightweight, two-stage edge-AI system that combines real-time disease detection with severity assessment, all powered by advanced machine learning models. This innovation could significantly reduce the reliance on manual inspections and enhance the efficiency of plant disease management.
The system, detailed in a recent study published in *Computers*, employs a YOLOv10n detector to identify leaves affected by seven common strawberry diseases, including Leaf Spot. The detector operates on an embedded platform, enabling real-time detection on a mobile agricultural robot. Once the affected leaves are located, patches are automatically extracted and transmitted to a second module—a compact MobileViT-S-based classifier. This classifier assesses the severity of Leaf Spot on three levels: mild, moderate, and severe.
The lead author of the study, Raikhan Amanova from the Department of Big Data and Artificial Intelligence at Al-Farabi Kazakh National University, emphasized the practical implications of this research. “Our system not only detects diseases but also provides a detailed assessment of their severity,” Amanova said. “This dual capability is crucial for farmers and agronomists, as it allows for more targeted and timely interventions.”
The effectiveness of the proposed system was demonstrated through a comparative experiment with other lightweight architectures, including ResNet-18, EfficientNet-B0, MobileNetV3-Small, and Swin-Tiny. The Ordinal MobileViT-S classifier outperformed all baseline models, achieving an accuracy of approximately 97% with just 4.9 million parameters. The YOLOv10n detector also showed impressive performance, with an [email protected] of 0.960, an F1 score of 0.93, and a recall of 0.917.
The commercial impact of this research is substantial. By automating disease detection and severity assessment, farmers can reduce labor costs and improve the overall health of their crops. The real-time capabilities of the system also enable quicker responses to disease outbreaks, potentially saving crops and increasing yields. “This technology has the potential to transform the way we manage plant diseases,” Amanova noted. “It can be integrated into existing agricultural practices, making it a valuable tool for both small-scale farmers and large-scale operations.”
The study’s findings suggest that the “YOLOv10n + Ordinal MobileViT-S” cascade could serve as the foundation for future real-time crop health monitoring systems. As the agricultural sector continues to embrace technological advancements, this research paves the way for more sophisticated and efficient disease management strategies. The integration of edge-AI systems into mobile agricultural robots represents a significant step forward in the quest for sustainable and productive farming practices.
With the increasing demand for food and the need for sustainable agricultural practices, innovations like this are more important than ever. The research conducted by Raikhan Amanova and her team at Al-Farabi Kazakh National University highlights the potential of edge-AI in transforming the agriculture sector, offering a glimpse into a future where technology and farming go hand in hand.