RoWeeder: Unsupervised Weed Mapping through Crop-Row Detection

TL;DR

RoWeeder is an unsupervised weed mapping framework that uses crop-row detection and noise-resilient deep learning to distinguish weeds from crops, enabling real-time aerial weed management in precision agriculture.

Contribution

The paper introduces RoWeeder, a novel unsupervised weed mapping method that leverages crop-row detection to train a lightweight deep learning model without labeled data.

Findings

Achieved an F1 score of 75.3 on WeedMap dataset

Outperformed several baseline methods

Validated through comprehensive ablation studies

Abstract

Precision agriculture relies heavily on effective weed management to ensure robust crop yields. This study presents RoWeeder, an innovative framework for unsupervised weed mapping that combines crop-row detection with a noise-resilient deep learning model. By leveraging crop-row information to create a pseudo-ground truth, our method trains a lightweight deep learning model capable of distinguishing between crops and weeds, even in the presence of noisy data. Evaluated on the WeedMap dataset, RoWeeder achieves an F1 score of 75.3, outperforming several baselines. Comprehensive ablation studies further validated the model's performance. By integrating RoWeeder with drone technology, farmers can conduct real-time aerial surveys, enabling precise weed management across large fields. The code is available at: \url{https://github.com/pasqualedem/RoWeeder}.