A deep learning-based method for rapidly identifying diseases and pests of edible mushrooms
By employing a deep learning-based method for identifying pests and diseases in edible fungi, and utilizing image and gas sensors for monitoring, the spread of pests and diseases can be identified and predicted, providing early warnings and control measures. This solves the problem of low efficiency in identifying pests and diseases in edible fungi and achieves efficient and accurate pest and disease management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHENGDU UNIV OF INFORMATION TECH
- Filing Date
- 2026-04-24
- Publication Date
- 2026-07-17
AI Technical Summary
In existing technologies, the identification of diseases and pests in edible fungi mainly relies on manual inspections, which is inefficient and time-consuming, making it difficult to achieve efficient and timely identification. This leads to a reduction in cultivation quality and yield. Furthermore, manual identification is prone to misjudgment and oversight, making it impossible to take timely and targeted management measures.
A deep learning-based approach is used to monitor and analyze the mycelial stage of edible fungi. Through image recognition and gas sensor monitoring, diseased fungal bed areas are identified, the direction and level of disease and pest spread are predicted, and early warning prompts are provided to enable targeted control measures.
It enables efficient and rapid identification and timely prevention and control of diseases and pests in edible fungi, ensuring the quality and yield of edible fungi, avoiding the spread and loss of diseases and pests, and improving identification efficiency and accuracy.
Smart Images
Figure CN122409652A_ABST