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• This paper reviews the research status of typical machine-vision-based weeding robots. • This paper has evaluated the performance of eight baseline methods that are based on deep learning on a public dataset. • This paper presents the application of object detection algorithms based on deep learning to weed identification. Due to its obvious advantages in saving labor and pesticides, weeding robots are one of the key technologies for modern and sustainable agriculture and have attracted increasing attention from researchers and developers. Some papers on machine-vision-based weeding robots have been published in recent years, yet there is no clear attempt to systematically study these papers to discuss the components of a robotic weed control system, such as visual navigation, weed detection and directional weeding. In this paper, typical machine-vision-based weeding robots proposed or constructed in the last 30 years, together with a few open datasets for weed detection, are reviewed. Key technologies such as image preprocessing, image segmentation, navigation line extraction, and weed recognition based on machine learning (ML) or deep learning (DL) for weeding robots are discussed. To illustrate the application of DL algorithms to weed detection, this paper provides weed object detection results and a comparative analysis of eight baseline methods based on DL using a public dataset. The study found that there are still many issues that need to be addressed in each part of the robotic weeding control system. Because of environmental variation and system complexity, machine-vision-based weeding robots are still in their early stages. The results of the systematic review provide an understanding of innovative trends in the use of machine vision in weeding systems and references for future research on weeding robots. [ABSTRACT FROM AUTHOR]

• The algorithm for the detection of weeds in paddy field was proposed. • A dataset containing rice and weeds was established. • The method for rice crop localization algorithm was proposed. • The proposed model achieves a high accuracy of 94.1% and a high speed of 24.3 fps. Weeds are one of the main hazards affecting the yield and quality of rice. In farmland ecosystem, weeds compete with rice for resources such as light, water, soil and space, and also cause plant disease by providing habitats for pests, resulting in a decline in yield of rice. Spraying herbicides is used most frequently due to their lower cost than manual weeding and they can effectively control a varieties of weeds. Precise spraying application may utilise intelligent technology to precisely limit the weed growth at specific locations and control the consumption of herbicides, which not only has lower cost and risk for the environment but also increases the economic benefits of agricultural products. The target of this work is to use deep learning model to perform weed detection in rice crop images and to achieve accurate real-time detection and low machine cost resulting in widely used in practice. For this purpose, a dataset of rice and weeds for object detection is established by means of on-site shooting and crawling of images on the web, which contains rice and eight categories of weeds. However, the overlap between crops and weeds poses a great challenge to weed detection. To overcome this challenge, this paper proposed a model named WeedDet based on RetinaNet, which improves the feature extraction ability of the backbone, feature pyramid network and detection head to deal with the complex information in the image, respectively. And the structure of feature pyramid and detection head are lightened to improve speed of detection. The specific implementation methods are as follows: Firstly, we propose Det-ResNet to reduce the loss of detailed information and improve the feature extraction ability of detailed textures by modifying the initial convolution structure of ResNet. In addition, the improved feature pyramid network fuses the features extracted by Det-ResNet with higher efficiency without loss of accuracy. Secondly, we propose Efficient Retina Head (ERetina-Head) with thin feature maps (small channel number feature maps) and large separable convolution, which not only saves memory and computation, but also pooling more powerful feature maps during training and inference. Finally, we combine Smooth L 1 loss and GIoU loss to calculate the regression loss. In addition, Varifocal loss is used to unify the classification and quality evaluation branches in training. Our network achieves a high mAP of 94.1% and the frame rate of 24.3 fps, which is 5.5% mAP and 5.6 fps higher than the baseline RetinaNet. Experiments show that fps of WeedDet (24.3) is second only to YOLOv3 (26.3) while the mAP is 9.9% (0.941 vs 0.842) higher than YOLOv3, and the mAP is also higher than other models, verifying the effectiveness and efficiency of the model. [ABSTRACT FROM AUTHOR]

• An image synthesis and semi-supervised learning pipeline is proposed. • The pipeline constructs an image library of plant instances with pixelwise masks. • Images can be synthesized using the library for site-specific weed detection. • Without manual labels, the pipeline achieves precision close to supervised learning. • Ablation study provides insights into the construction of a weed image database. Precise and efficient weed detection in agricultural fields is the key for robotic weed control. Recent developments in convolutional neural networks (CNNs) have achieved significant success in this regard. CNNs that simultaneously localize and classify objects in images are the predominant forms that have been widely used to detect crops and weeds. However, the use of CNNs in agriculture, particularly for weed detection, has been impeded by a lack of large training dataset with ground truth annotations. Cut-and-paste image synthesis approach and semi-supervised learning are popular methods to alleviate the training data deficiency problem. In this paper, we propose a novel image synthesis and semi-supervised learning pipeline to train site-specific weed detection models without the need for manually labeled images. The CNN models trained by this pipeline achieve performance levels close to that of the supervised models. We investigated the behavior of the proposed pipeline by varying several key components and showed that color match between the training and testing images, training-time color augmentation, and iterative semi-supervised learning largely improve the model performance. These promising results can be used to guide the construction of a weed image database applicable to different weed detection scenarios. [ABSTRACT FROM AUTHOR]