Rice spikelet size, spikelet length and spikelet width, are very important traits directly related to a rice crop’s yield. The accurate measurement of these parameters is quite significant in research such as breeding, yield evaluation and variety improvement for rice crops. Traditional measurement methods still mainly rely on manual labor, which is time-consuming, labor-intensive and error-prone. In this study, a novel method, dubbed the “SSM-Method”, based on convolutional neural network and traditional image processing technology has been developed for the efficient and precise measurement of rice spikelet size parameters on rice panicle structures. Firstly, primary branch images of rice panicles were collected at the same height to build an image database. The spikelet detection model using convolutional neural network was then established for spikelet recognition and localization. Subsequently, the calibration value was obtained through traditional image processing technology. Finally, the “SSM-Method” integrated with a spikelet detection model and calibration value was developed for the automatic measurement of spikelet sizes. The performance of the developed SSM-Method was evaluated through testing 60 primary branch images. The test results showed that the root mean square error (RMSE) of spikelet length for two rice varieties (Huahang15 and Qingyang) were 0.26 mm and 0.30 mm, respectively, while the corresponding RMSE of spikelet width was 0.27 mm and 0.31 mm, respectively. The proposed algorithm can provide an effective, convenient and low-cost tool for yield evaluation and breeding research.

This paper addresses the three-dimensional trajectory tracking problem of underactuated autonomous underwater vehicles (AUVs) operating in the presence of external disturbances and unmodeled dynamics by proposing a predefined-time adaptive control scheme. Firstly, the underactuated AUV system was decoupled into drive and non-drive subsystems to facilitate the design of a controller that does not rely on specific model parameters. Radial basis function neural networks (RBFNNs) were employed to estimate the external disturbances. To enhance tracking performance, a predefined-time adaptive control law was designed to ensure that tracking errors converged to a small neighborhood around the origin within the predefined time. The adaptive control law compensated for the unmodeled components. Finally, we used theoretical proofs and simulations to show that our method is effective and superior.

Floating debris threatens aquatic ecosystems, yet detecting it in close-range aquatic remote sensing imagery remains difficult because small, irregular, or partially submerged objects resemble specular highlights and ripples. This study proposes FLNet, a YOLO11-L-based detector for water-surface monitoring from unmanned surface vehicles (USVs). FLNet redesigns the backbone, neck, and head with three components: a Dynamic Polarity-Aware Robustness Block that calibrates deep activations and models similar and opposing feature relations; an Adaptive Multi-Receptive Pyramid that repeatedly fuses three adjacent scales through multi-branch depthwise convolutions; and a Detail-Preserving Shared Head that applies one boundary-sensitive prediction trunk to all scales. On a custom five-category dataset of 12,237 images, FLNet achieved 85.93% macro-Precision, 85.08% macro-F1, and 88.77% mean average Precision at an intersection-over-union threshold of 0.5 (mAP@0.5), the highest single-run values among the evaluated detectors, with 24 M parameters and 83 GFLOPs. Across three random seeds, its macro-Precision and macro-F1 advantages over the strongest competitor, Hyper-YOLO, exceeded seed-to-seed variability, whereas its mAP@0.5 was statistically comparable to that of Hyper-YOLO and Hyper-YOLO retained a higher mAP@0.5:0.95. In separately trained benchmarks, FLNet achieved the highest F1-score on FloW-Img (80.26%) and the highest macro-F1 on MarineDebris-yolo (79.82%). With TensorRT half-precision (FP16) acceleration, detector-side inference reached 29.8, 111.2, and 56.4 frames per second on Jetson Xavier NX, AGX Orin, and Orin Nano Super, respectively. FLNet therefore offers a favorable accuracy–efficiency balance for close-range floating-debris detection.