The development of machine vision-based technologies to replace human labor for rapid and exact detection of agricultural product quality has received extensive attention. In this study, we describe a low-rank representation of jointly multi-modal bag-of-feature (JMBoF) classification framework for inspecting the appearance quality of postharvest dry soybean seeds. Two categories of speeded-up robust features and spatial layout of L*a*b* color features are extracted to characterize the dry soybean seed kernel. The bag-of-feature model is used to generate a visual dictionary descriptor from the above two features, respectively. In order to exactly represent the image characteristics, we introduce the low-rank representation (LRR) method to eliminate the redundant information from the long joint two kinds of modal dictionary descriptors. The multiclass support vector machine algorithm is used to classify the encoding LRR of the jointly multi-modal bag of features. We validate our JMBoF classification algorithm on the soybean seed image dataset. The proposed method significantly outperforms the state-of-the-art single-modal bag of features methods in the literature, which could contribute in the future as a significant and valuable technology in postharvest dry soybean seed classification procedure.

Detecting ship targets in optical remote sensing images is crucial for ensuring the safety of maritime traffic. However, remote sensing ship detection faces challenges such as low resolution, dense distribution, and multi-scale variations. Additionally, complex backgrounds and changing weather conditions further complicate the detection process. To address these challenges, a lightweight remote sensing ship detection architecture derived from YOLO11 is proposed. Firstly, a Fourier convolution dual-domain fusion module is designed to efficiently integrate multi-scale convolution operations in both the spatial and frequency domains, thereby enhancing the model’s capacity to recognize ship targets under complex geographical and weather conditions. Furthermore, a partially parallel synergistic attention mechanism is developed to comprehensively model both global and local features, effectively mitigating the difficulty of extracting ship features caused by complex background interference. Finally, a Ship intersection over union loss function is proposed, which dynamically calculates the loss by focusing on the shape and scale of the ship targets, ensuring that the geometric properties of the targets are better captured by the model. The improved model demonstrates superior detection performance and lower parameter parameters on the HRSC2016 and DOTA1.0 public remote sensing datasets compared to other advanced algorithms.