Precision weed detection in wheat fields for agriculture 4.0: A survey of enabling technologies, methods, and research challenges

Abstract

Weeds pose a serious threat to the safe wheat production. They are an important factor contributing to the reduction in wheat yield and quality. The current weed control methods in wheat fields have relied primarily on chemical control. The inability to determine the precise location of weeds has led to excessive usage and low utilization rate of pesticides, causing severe pollution. As agronomic operations evolve towards Agriculture 4.0, weed control technology in wheat fields is progressively becoming more precise and intelligent. Weed detection technologies and methods in wheat fields may provide the groundwork for improving the accuracy and efficiency in weeding. This study begins with a review of common weed species and distribution pattern in wheat fields and provides an in-depth analysis of current technologies and developments for weed detection. We focused on the current states of research in spectroscopy, image, imaging spectroscopy, depth information, and multi-modal information fusion for weed detection in wheat fields. We also summarized the trend of weed detection algorithms from traditional machine learning to deep learning and proposed trends of future development in wheat field weed detection. Our study has contributed to the implementation of more automatic and precise weeds management.