What question did this study set out to answer?

The aim is to enhance weed identification in rice cultivation using a deep learning approach.

February 20, 2026Open Access

Weed Identification in Rice Cultivation Employing Semantic Segmentation

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Compares weed detection outcomes in rice fields using an enhanced SegNet model, indicating improved precision in complex environments.

The aim is to enhance weed identification in rice cultivation using a deep learning approach.
Employs an attention-enhanced SegNet model for weed detection.
Trains on images of paddy fields for pixel-level segmentation.
Compares standard SegNet and SegNet-Attention architectures.
Assesses performance using accuracy, precision, recall, IoU, and dice coefficient.
The SegNet-Attention model shows improved weed boundary delineation.
Enhanced segmentation accuracy in challenging conditions with overlapping crops.
The attention-enhanced model consistently outperforms the standard SegNet.

Neha et al. (2026) studied this question.

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