Construction and Application of Real-Time Monitoring Model of Nitrogen Nutrition Status of Peanut Population Based on Improved YOLOv11

Abstract

In response to the demand for real-time monitoring of the nitrogen nutritional status of peanut populations, this paper proposes a real-time monitoring system for the nitrogen nutritional status of peanut populations based on the YOLOv11 framework and spectral attention module. Traditional nitrogen detection methods have problems such as low efficiency and difficulty in achieving population-scale monitoring, while crop phenotyping technology based on computer vision faces challenges such as small leaf targets, severe occlusion, easy confusion of nitrogen deficiency symptoms, and difficulty in deploying deep learning models on mobile terminals. This study improves the YOLOv11 model, introduces the ASF (Attentional Scale Fusion) module and the DySample dynamic upsampling mechanism, enhances the model’s perception and feature expression capabilities for multi-scale targets, and effectively improves the monitoring accuracy and robustness of the nitrogen nutritional status of peanut populations. Experimental results show that the ADS-YOLO model performs well in evaluation indicators such as accuracy, recall, and mean average precision (mAP), providing technical support for precision fertilization of peanuts.

Code and data availability

The paper's peanut canopy image dataset (117 images across three nitrogen levels) and ADS-YOLO model are paper-specific phenotyping assets, but no public repository, deposit, or URL is provided; the Data Availability Statement directs inquiries to the corresponding author.

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