Seed germination rate is a critical factor in agricultural productivity. Traditional approaches to germination assessment necessitate human scrutiny, introducing subjectivity and diminishing operational efficiency. While fully supervised deep learning approaches offer objectivity, reproducibility and efficiency, they require large-scale and high-quality labeled datasets, which are often challenging to obtain. To address this limitation, this study introduce a Semi-Supervised Germination Detection (SSGD) method built upon the Soft Teacher framework. SSGD employs a Faster R-CNN detector with ResNet50-FPN feature extraction network in both the teacher model and the student model. To ensure higher learning stability, the parameters of the teacher model are dynamically improved by an exponential moving average (EMA) updating mechanism. This study conducted a comprehensive evaluation of SSGD on the publicly available Pennisetum glaucum (PG), Secale cereale (SC), and Zea mays (ZM) datasets. Remarkably, with only 10% of labeled data, SSGD achieved mAP50 scores of 0.954, 0.928, and 0.961 on PG, SC, and ZM, respectively, surpassing fully supervised methods trained on 100% labeled data, including YOLOv3, FCOS, Cascade R-CNN and Faster R-CNN. Moreover, SSGD consistently outperformed the Faster R-CNN baseline across various annotation ratios (1%, 10%, 20% and 30%). Notably, even when the PG dataset’s labeling rate dropped to only 1%, SSGD maintained a high mAP50 of 0.94, exceeding the baseline by 5 percentage points. These findings underscore SSGD’s strong adaptability to limited data and further emphasize the effectiveness of semi-supervised learning in seed germination detection.

In the field of Earth observation and computer vision, oriented object detection for remotesensing images is a crucial task that aims to locate objects more accurately in complex scenes containing a large number of densely arranged, large aspect ratio, and arbitrarily oriented objects. Although recently proposed methods have achieved remarkable performance, there are still several challenges to address: 1) interference from complex backgrounds, 2) imbalanced and mismatched label assignments caused by tiny objects and objects with large aspect ratios, and 3) misalignment between the tasks of classification and localization. In this article, we propose a frequency-assisted dynamic learning network (FADL-Net) to overcome the crucial challenges. Concretely, we introduce a spatial-spectral feature pyramid network to adaptively capture global long-range dependency feature representations containing various frequency domains. Meanwhile, to produce more reliable training samples for objects with extreme shapes, we design a geometric aware dynamic label assignment to dynamically mitigate the imbalance and mismatch in label assignment in a coarse-to-fine manner, thereby achieving more stable optimization during the training process. Moreover, we propose a joint-learning rotated quality loss that addresses the inconsistency between classification and localization by dynamically adapting the weights of different samples in the training stage. Extensive experiments on several public remote sensing datasets demonstrate that our method performs favorably against state-of-the-art detection approaches.

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the detection performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted to the detection of arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, this article proposes a semisupervised oriented object detection framework (S$^{2}$O-Det) to reduce annotation costs while improving detection performance in a semisupervised manner. Initially, the proposed task-consistent learning aims to alleviate the inconsistencies between classification and localization, which provides consistent confidence for the pseudolabels. Subsequently, the introduced coarse-to-fine sample mining employs dense prediction for pseudolabel assignment, adopting a divide-and-conquer approach to independently identify consistent and reliable labels for both classification and localization tasks. Finally, a probabilistic distillation loss ensures the harmonization of the probability distributions across the teacher and student feature domains, thereby reciprocally enhancing the learning competencies. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that S$^{2}$O-Det achieves promising performance across different labeling ratios.

This paper proposes a formalism for nonmonotonic reasoning based on prioritized argumentation. We argue that nonmonotonic reasoning in general can be viewed as selecting monotonic inferences by a simple notion of priority among inference rules. More importantly, these types of constrained inferences can be specified in a knowledge representation language where a theory consists of a collection of rules of first order formulas and a priority among these rules. We recast default reasoning as a form of prioritized argumentation and illustrate how the parameterized formulation of priority may be used to allow various extensions and modifications to default reasoning. We also show that it is possible, but more difficult, to express prioritized argumentation by default logic: Even some particular forms of prioritized argumentation cannot be represented modularly by defaults under the same language.