Abstract:
This study investigates the use of deep learning models, specifically YOLOv8 and
Faster R-CNN, for real-time object identification and autonomous plant disease
diagnosis. The research uses a dataset consisting of 38 examples from 11 distinct
classes, which include various plant species such as apple, grape, and potato. Within
these categories, there are specific classes representing healthy conditions and
various diseases. This diverse dataset, featuring 3171 apple images, 4063 grape
images, and 2852 potato images, supports comprehensive training and evaluation
of machine learning models for disease identification. A statistical analysis of the
subset reveals different distributions of images across the classes, highlighting the
prevalence and significance of certain diseases within each plant category. The
effectiveness of YOLOv8 and Faster R-CNN is assessed using performance metrics
like Intersection over Union (IoU), saliency score, and inference time. Although
specific numerical values are not provided, the data indicate that YOLOv8 performs
well in terms of IoU and achieves a higher saliency score compared to Faster RCNN. Conversely, Faster R-CNN shows superior IoU performance but with a lower
saliency score. Additionally, YOLOv8 demonstrates faster inference times, while
Faster R-CNN has significantly longer inference times.By comparing these metrics,
the study provides valuable insights into the strengths of each model, guiding the
selection and optimization of deep learning architectures for plant disease
diagnosis. The research also emphasizes the trade-offs between speed and accuracy
inherent in object identification models, underscoring the importance of considering
application-specific requirements. Overall, this study advances agricultural
technology by exploring the potential of deep learning models in combating harmful
plant diseases. It lays the groundwork for future advancements in autonomous
plant disease diagnosis and contributes to global food security by enhancing
diagnostic techniques.
