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Automatic fruit blossom detection plays a crucial role in agricultural intelligence to predict fruit yield. Existing deep learning methods for vision-based fruit blossom detection oftentimes rely on large labeled samples and are tailor for a single category of fruit blossoms, limiting their flexibility and adaptability in real applications. In this paper, a fast and data-efficient framework is proposed to achieve multi-class fruit blossom detection using few labeled samples, which not only enhances the flexibility of model training by reducing the need for extensive samples but also extends the model's applicability to a wider range of blossom categories. Specifically, the proposed framework incorporates the paradigm of few-shot object detection into a lightweight two-stage detector named CenterNet2. To improve the recall of blossom proposals, we introduce a location guidance module (LGM) to highlight foreground regions in query images that bear resemblance to the blossom reference provided by support images. Moreover, a contrastive learning scheme (CLS) is introduced to further distinguish fruit blossom categories with similar appearances, enhancing classification accuracy. Experimental results demonstrated that our framework achieved comparable performance to existing state-of-the-art models, with a mean average precision of 74.33% over 12 categories of fruit blossoms and a frame per second rate of 47. In summary, our proposed framework can reduce data dependency and enhance flexibility of existing deep learning detectors, making it a promising solution for automatic monitoring in real-world agricultural applications. • The proposed framework can detect multi-class fruit blossoms with few labeled samples. • A location guidance module is introduced to guide the framework to produce more accurate blossom proposals. • A contrastive learning scheme is introduced to distinguish between different fruit blossoms with similar appearances. • The proposed framework achieves comparable performance to existing state-of-the-art models and faster inference speed. • The proposed framework can reduce data dependency and enhance flexibility of existing deep learning detectors. [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]

• From 2017 to 2022, the area and number of PGs in Zhoukou increased by 41.38% and 47.08%, respectively. • A model YXLD + was proposed that can capture both the area and the quantity of PGs. • New CSPLayer method can fully extract the spatial feature of images. • A multi-temporal greenhouse map based on VHR remote sensing imagery was plotted. • The spatial distribution of PGs was mainly affected by the slope. Plastic greenhouses (PGs), as safeguard agricultural facilities, have changed the cultivation structure of agriculture, to a certain extent, and can seriously impact the environment. Exploring the temporal characteristics of greenhouses in terms of quantity and area is significant for sustainable agricultural development and environmental protection. Pixel or object-based large-scale greenhouse mapping has difficulty meeting the demand for the simultaneous extraction of greenhouse quantity and area. Multibranch task model can provide the number and area information of greenhouse. However, there are various labeling methods and model between different branches, which lead to the deviation between quantity and area results. This paper proposes a model that is based on improved you only look once X (YOLOX) and kullback–leibler divergence (KLD), called YOLOX and KLD plus (YXLD +), to obtain spatiotemporally consistent greenhouse mapping. The new model still has stable performance in dense scenes, with a 5.7 % improvement in average precision compared to BBAVectors. Finally, a multitemporal greenhouse map of Zhoukou City was obtained using imagery provided by Google Earth (GE). Overall, the distribution of greenhouses in Zhoukou City remained stable between 2012 and 2017. However, the number and area of greenhouses in local areas has increased rapidly in the past five years, with the number of greenhouses increasing by 47.08 % and the area expanding by 41.38 %. Greenhouses' distribution and changing characteristics concentrate on the gently sloping areas from the northwest to the southeast. These results provide an objective perspective on the development of the local agricultural cultivation structure, which is of significant importance for the promotion of agricultural modernization. [ABSTRACT FROM AUTHOR]