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• A novel deep learning model is proposed for critical emerging frost detection. • The detection accuracy has been improved and real-time is satisfied. • It provides technical support for other researches related to plant frost detection. Natural radiation frost is a potential threat or even a serious disaster during the growth of agricultural crops. To avoid or reduce the risk of frost damage to crops, frost detection especially the timely and real-time detection of emerging frost crystals, is of great importance in agricultural frost protection. Therefore, this paper proposes an object detection model Frost-YOLOv5 based on microscopic imaging of emerging frost crystals on the leaf surface. Firstly, a YOLOv5 initial detection model was established by observing frost under natural conditions and using microscopic cameras to acquire images of emerging frost crystals formed on the leaf surface. From these images, the channel attention mechanism and spatial attention mechanism were combined to construct a small frost crystal feature depth extraction network. Secondly, the RGB three-channel Wiener filtering algorithm was used to recover the noise source images from motion blur in the frost crystal dataset. Through experimental validation on different light intensities and different frost crystal cluster datasets, the Frost-YOLOv5 model all showed better robustness compared with various object detection algorithms, and its frost crystal detection accuracy P and average accuracy AP values were 98.89% and 81.72%, respectively. Ultimately, the Frost-YOLOv5 model can effectively detect emerging frost crystal targets and provide theoretical principles and technical support for agrometeorological monitoring research. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep learning automates precise cereal plant head detection in agriculture. • Maize, rice, wheat, and sorghum case studies evaluated. • Comparison of deep learning approaches for object detection and image segmentation. • Challenges and opportunities for future research outlined. • Emphasis on how deep learning can contribute to increasing crop yields and food security. Cereal plant heads must be identified precisely and effectively in a range of agricultural applications, including yield estimation, disease detection, and breeding. Traditional methods that rely on manual feature extraction and thresholding take a lot of time and work, and they are also impacted by crop variability. Deep learning algorithms can be used to automate this procedure because they can directly extract complicated information from images and produce cutting-edge outcomes. This review provides a comprehensive overview of recent research on deep learning-based head detection in cereal plants, emphasizing object detection and image segmentation. We also discuss the major benefits and drawbacks of different deep learning architectures and training methods, as well as examples of their application in maize, rice, wheat, and sorghum. Developing robust image processing algorithms, using deep learning in other domains like unmanned aerial vehicles, and utilizing large and diverse datasets are all challenges outlined in our study as future research directions. Through the integration of advanced computer vision techniques with precision agriculture, this paper attempts to promote further research and innovation in this intriguing field. We provide a thorough analysis of current developments in deep learning-based head detection for cereal plants and emphasize how this technology can contribute significantly to precision agriculture. [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]