Department of Information Science, Xi'an University of Technology, Xi'an 710048, China.
Chinese Academy of Agricultural Mechanization Sciences, Beijing 100083, China.
Sensors (Basel). 2021 May 24;21(11):3647. doi: 10.3390/s21113647.
Weeds are one of the most important factors affecting agricultural production. The waste and pollution of farmland ecological environment caused by full-coverage chemical herbicide spraying are becoming increasingly evident. With the continuous improvement in the agricultural production level, accurately distinguishing crops from weeds and achieving precise spraying only for weeds are important. However, precise spraying depends on accurately identifying and locating weeds and crops. In recent years, some scholars have used various computer vision methods to achieve this purpose. This review elaborates the two aspects of using traditional image-processing methods and deep learning-based methods to solve weed detection problems. It provides an overview of various methods for weed detection in recent years, analyzes the advantages and disadvantages of existing methods, and introduces several related plant leaves, weed datasets, and weeding machinery. Lastly, the problems and difficulties of the existing weed detection methods are analyzed, and the development trend of future research is prospected.
杂草是影响农业生产的最重要因素之一。全覆盖化学除草剂喷洒造成的农田生态环境浪费和污染日益明显。随着农业生产水平的不断提高，准确区分作物和杂草并实现仅对杂草进行精确喷洒变得尤为重要。然而，精确喷洒取决于准确识别和定位杂草和作物。近年来，一些学者已经使用了各种计算机视觉方法来实现这一目标。本文详细阐述了使用传统图像处理方法和基于深度学习的方法来解决杂草检测问题的两个方面。它综述了近年来各种杂草检测方法，分析了现有方法的优缺点，并介绍了几种相关的植物叶片、杂草数据集和除草机械。最后，分析了现有杂草检测方法存在的问题和困难，并展望了未来研究的发展趋势。
