Plant detection and counting play a crucial role in modern agriculture, providing vital references for precision management and resource allocation. This study follows the footsteps of machine learning experts by introducing the state-of-the-art Yolov8 technology into the field of plant science. Moreover, we made some simple yet effective improvements. The integration of shallow-level information into the path aggregation network (PANet) served to counterbalance the resolution loss stemming from the expanded receptive field. The enhancement of upsampled features was accomplished through combining the lightweight up-sampling operator Content-Aware ReAssembly of Features (CARAFE) with the Multi-Efficient Channel Attention (Mlt-ECA) technique to optimize the precision of upsampled features. This collective approach markedly amplified the discernment of small objects in Unmanned Aerial Vehicle (UAV) images, naming it Yolov8-UAV. Our evaluation is based on datasets containing four different plant species. Experimental results demonstrate the strong competitiveness of our proposed method even when compared to the most advanced counting techniques, and it possesses sufficient robustness. In order to advance the cross-disciplinary research of computer vision and plant science, we also release a new cotton boll dataset with detailed annotated bounding box information. What’s more, we address previous oversights in existing wheat ear datasets by providing updated labels consistent with global research advancements. Overall, this research offers practitioners a powerful solution for addressing real-world application challenges. For UAV scenarios, recommend using the specialized Yolov8-UAV, while Yolov8-N is a wise choice for general scenes due to its sufficient accuracy and speed in the majority of cases. Furthermore, we contribute two meaningful datasets that have research significance, effectively promoting the application of data resources in the field of plant science. In short, our contribution is to improve the use of Yolov8 in UAV scenarios and open two datasets with bounding boxes. The curated data and code can be accessed at the following link: https://github.com/Ye-Sk/Plant-dataset.

Background: Detection and counting of wheat heads are of crucial importance in the field of plant science, as they can be used for crop field management, yield prediction, and phenotype analysis. With the widespread application of computer vision technology in plant science, monitoring of automated high-throughput plant phenotyping platforms has become possible. Currently, many innovative methods and new technologies have been proposed that have made significant progress in the accuracy and robustness of wheat head recognition. Nevertheless, these methods are often built on high-performance computing devices and lack practicality. In resource-limited situations, these methods may not be effectively applied and deployed, thereby failing to meet the needs of practical applications.Results: In our recent research on maize tassels, we proposed TasselLFANet, the most advanced neural network for detecting and counting maize tassels. Building on this work, we have now developed a high-real-time lightweight neural network called WheatLFANet for wheat head detection. WheatLFANet features a more compact encoder-decoder structure and an effective multi-dimensional information mapping fusion strategy, allowing it to run efficiently on low-end devices while maintaining high accuracy and practicality. According to the evaluation report on the global wheat head detection dataset, WheatLFANet outperforms other state-of-the-art methods with an average precision AP of 0.900 and an R²value of 0.949 between predicted values and ground truth values. Moreover, it runs significantly faster than all other methods by an order of magnitude (TasselLFANet: FPS: 61).Conclusions: Extensive experiments have shown that WheatLFANet exhibits better generalization ability than other state-of-the-art methods, and achieved a speed increase of an order of magnitude while maintaining accuracy. The success of this study demonstrates the feasibility of achieving real-time, lightweight detection of wheat heads on low-end devices, and also indicates the usefulness of simple yet powerful neural network designs.

Accurately and rapidly counting the number of maize tassels is critical for maize breeding, management, and monitoring the growth stage of maize plants. With the advent of high-throughput phenotyping platforms and the availability of large-scale datasets, there is a pressing need to automate this task for genotype and phenotype analysis. Computer vision technology has been increasingly applied in plant science, offering a promising solution for automated monitoring of a large number of plants. However, the current state-of-the-art image algorithms are hindered by hardware limitations, which compromise the balance between algorithmic capacity, running speed, and overall performance, making it difficult to apply them in real-time sensing field environments. Thus, we propose a novel lightweight neural network, named TasselLFANet, with an efficient and powerful structure for accurately and efficiently detecting and counting maize tassels in high spatiotemporal image sequences. Our proposed approach improves the feature-learning ability of TasselLFANet by adopting a cross-stage fusion strategy that balances the variability of different layers. Additionally, TasselLFANet utilizes multiple receptive fields to capture diverse feature representations, and incorporates an innovative visual channel attention module to detect and capture features more flexibly and precisely. We conducted a series of comparative experiments on a new, highly informative dataset called MrMT, which demonstrate that TasselLFANet outperforms the latest batch of lightweight networks in terms of performance, flexibility, and adaptability, achieving an F1 measure value of 94.4%, a mAP.@5 value of 96.8%, and having only 6.0M parameters. Moreover, compared with the regression-based TasselNetV3-Seg† model, our proposed model achieves superior counting performance, with a mean absolute error (MAE) of 1.80, a root mean square error (RMSE) of 2.68, and a R² of 0.99. The proposed model meets the accuracy and speed requirements of the vision system in maize tassel detection. Furthermore, our proposed method is reliable and unaffected by geographical changes, providing essential technical support for computerized counting in the field.

Unmanned aerial vehicles (UAVs), equipped with sensors, have made a significant impact in the field of agricultural analysis. Maize, being one of the most vital crops worldwide, is intricately linked to its yield and the growth of tassels. Leveraging UAV imagery for the automatic monitoring of maize tassels holds the potential to drive the development of intelligent maize cultivation. Current research methods, nevertheless, are limited and lack robustness. To address the challenge of tassel detection in UAV images, we propose an innovative network, termed FGLNet. This network models the backbone with a 16x down-sampling to retain richer pixel information and enhances performance by effectively fusing global and local information through weighted mechanisms. Moreover, the scarcity of tassel data presents a substantial constraint. In this study, we publicly release a new dataset, named the maize tassels detection and counting UAV (MTDC-UAV), featuring annotated bounding boxes, to advance research in the agricultural domain. Although tassel detection and counting in aerial images pose formidable challenges, our approach demonstrates remarkable accuracy in evaluations based on the MTDC-UAV dataset. It achieves a detection AP ₅₀ of 0.837 and a counting R ² of 0.9409, all while maintaining a parameter count of just 0.77M. This level of performance considerably outperforms other state-of-the-art computer vision methods. Overall, this research not only introduces innovative concepts but also provides worthwhile references and a solid data foundation for future studies.

Introduction: Soybean pod count is one of the crucial indicators of soybean yield. Nevertheless, due to the challenges associated with counting pods, such as crowded and uneven pod distribution, existing pod counting models prioritize accuracy over efficiency, which does not meet the requirements for lightweight and real-time tasks.Methods: To address this goal, we have designed a deep convolutional network called PodNet. It employs a lightweight encoder and an efficient decoder that effectively decodes both shallow and deep information, alleviating the indirect interactions caused by information loss and degradation between non-adjacent levels.Results: We utilized a high-resolution dataset of soybean pods from field harvesting to evaluate the model’s generalization ability. Through experimental comparisons between manual counting and model yield estimation, we confirmed the effectiveness of the PodNet model. The experimental results indicate that PodNet achieves an R² of 0.95 for the prediction of soybean pod quantities compared to ground truth, with only 2.48M parameters, which is an order of magnitude lower than the current SOTA model YOLO POD, and the FPS is much higher than YOLO POD.Discussion: Compared to advanced computer vision methods, PodNet significantly enhances efficiency with almost no sacrifice in accuracy. Its lightweight architecture and high FPS make it suitable for real-time applications, providing a new solution for counting and locating dense objects.