Semi-supervised learning and attention mechanism for weed detection in wheat

Abstract

Machine vision-based precision herbicide application in wheat (Triticum aestivum L.) can substantially reduce herbicide input. However, detecting newly emerged weeds in wheat fields remains a challenging task. Current deep learning-based weed detection methods require the annotation of a large amount of data, which is both time-consuming and labor-intensive. To address this issue, this research improved a semi-supervised learning (SSL) algorithm based on consistency regularization and pseudo-labeling, and incorporated an attention mechanism. Compared to fully supervised learning (FSL) algorithms, the proposed method increased the classification accuracy by 16.5%, 17.84%, and 19.67% on datasets with 200 × 200, 300 × 300, and 400 × 400 pixel images, respectively, when only 100 labeled data per class were used. Overall, the developed machine vision models using the proposed method achieved weed detection with high accuracy while requiring much fewer labeled training images, and thus is more time and labor-efficient compared to an FSL algorithm.