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Lightweight cabbage segmentation network and improved weed detection method
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AI
DOI：10.1016/j.compag.2024.109403.png)
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Abstract
En 中文
This study addressed the challenge of machine vision-based weed detection for precision herbicide application, a task complicated by the diversity of weed species, ecotypes, and variations in growth stages. We propose an indirect approach that segments crops and classifies the remaining green objects as weeds. A novel, lightweight segmentation network was developed to reduce computational demands without compromising accuracy. The model, with a size of just 2.64 MB, achieves an impressive mean Intersection over Union (mIoU) of 97.9 %, with a recall of 93.4 %, and a precision of 97.6 %, while also enhancing inference speed. Subsequently, improvements were implemented using the image processing method for extracting green plants. A crop mask was generated using a segmentation algorithm, and a mask expansion mechanism was introduced to rectify errors in the initial phase of crop segmentation. A cost-effective threshold adjustment operation was applied to eliminate the environmental influences on the detection results. The results indicate that the weed detection method completely avoided the complexity related to the variations in species, ecotypes, growth stages, and densities of weeds across different fields and realized accurate, effective, and reliable weed detection in cabbage.
Keywords:
Deep learning
Image processing
Lightweight architecture
Semantic segmentation
Weed detection
Journal
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