Advancing precision agriculture with deep learning enhanced SIS-YOLOv8 for Solanaceae crop monitoring.

Journal: Frontiers in plant science
Published Date: Jan 9, 2025

Abstract

INTRODUCTION: Potatoes and tomatoes are important Solanaceae crops that require effective disease monitoring for optimal agricultural production. Traditional disease monitoring methods rely on manual visual inspection, which is inefficient and prone to subjective bias. The application of deep learning in image recognition has led to object detection models such as YOLO (You Only Look Once), which have shown high efficiency in disease identification. However, complex climatic conditions in real agricultural environments challenge model robustness, and current mainstream models struggle with accurate recognition of the same diseases across different plant species.

Authors

  • Ruiqian Qin
    College of Information Technology, Jilin Agricultural University, Changchun, China.
  • Yiming Wang
    Teaching Resource Information Service Center, Changchun Institute of Education, Changchun, China.
  • Xiaoyan Liu
    College of Information Technology, Jilin Agricultural University, Changchun, China.
  • Helong Yu
    Smart Agriculture Research Institute, Jilin Agricultural University, Changchun, China.

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