Coir-fiber-reinforced epoxy resin composites are an environmentally friendly material, and the use of coir fibers improves the mechanical properties of epoxy resin. In order to improve the interfacial adhesion between coir fibers and the epoxy resin matrix, microwave treatment, alkali treatment, acetic anhydride modification, 3-aminopropyltriethoxysilane modification and their reasonable combination method treatments were carried out on coir fibers, respectively. Scanning electron microscopy (SEM), Fourier transform-infrared (FTIR) and X-ray diffraction (XRD) were used to analyze the effects of the different treatments on the characteristics of the coir fibers, and single-fiber pullout tests were performed on the pullout specimens made from the above coir fibers. The results calculated by the proposed estimation method show that the combination method of alkali treatment and 3-aminopropyltriethoxysilane surface modification could better enhance the interfacial bonding ability between coir fibers and epoxy resin with an interfacial shear strength and pullout energy of 6.728 MPa and 40.237 N·mm, respectively. The principal analysis shows that the method can form both mechanical interlocking and chemical bonds at the interface to enhance the interfacial bonding ability. This study provides a more suitable method for improving the interfacial properties of coir-fiber-reinforced epoxy resin composites and has implications for the study of natural fiber composites.

As a type of natural fiber with excellent elongation, coir fiber has been applied in a wide range of fields. To ensure superb performance, coir fiber is usually treated with alkali before being applied. Previous studies paid little attention to the multiple alkali treatment of coir fiber; however, this study focuses on its influence on the mechanical properties of coir fiber and conducts multi-objective optimization and analysis of the tensile strength, elastic modulus and elongation of coir fiber. Our objective is the comprehensive enhancement of the mechanical properties of coir fiber. In this study, the experimental design is based on the Box-Behnken design method, and three treatment parameters were selected for the study, namely NaOH concentration, treatment time and treatment temperature. Analysis of variance (ANOVA) was adopted to analyze the experimental data, and response surface methodology (RSM) was used to investigate how the treatment factors interact with each other and affect the responses values. To improve the tensile strength, elastic modulus and elongation of coir fiber simultaneously, the experimental parameters were optimized. The results showed that the optimal values of NaOH concentration, treatment time and treatment temperature were 4.12%, 15.08 h and 34.21 °C, respectively. Under these conditions, the tensile strength of coir fiber was 97.14 MPa, the elastic modulus was 2.98 GPa and the elongation was 29.35%, which were 38.28%, 39.91% and 25.59% higher than that of untreated coir fiber, respectively. Furthermore, scanning electron microscopy (SEM), thermogravimetric analysis (TGA-DTG), Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to analyze the changes in surface, weight loss, composition and crystallinity of coir fiber treated with alkali under optimum conditions compared with untreated coir fiber to obtain a deeper insight into the influential mechanisms of alkali treatment.

Seed quality testing is crucial for ensuring food security and stability. To accurately detect the germination status of corn seeds during the paper medium germination test, this study proposes a corn seed germination status detection model based on YOLO v8n (CSGD-YOLO). Initially, to alleviate the complexity encountered in conventional models, a lightweight spatial pyramid pooling fast (L-SPPF) structure is engineered to enhance the representation of features. Simultaneously, a detection module dubbed Ghost_Detection, leveraging the GhostConv architecture, is devised to boost detection efficiency while simultaneously reducing parameter counts and computational overhead. Additionally, during the downsampling process of the backbone network, a downsampling module based on receptive field attention convolution (RFAConv) is designed to boost the model’s focus on areas of interest. This study further proposes a new module named C2f-UIB-iAFF based on the faster implementation of cross-stage partial bottleneck with two convolutions (C2f), universal inverted bottleneck (UIB), and iterative attention feature fusion (iAFF) to replace the original C2f in YOLOv8, streamlining model complexity and augmenting the feature fusion prowess of the residual structure. Experiments conducted on the collected corn seed germination dataset show that CSGD-YOLO requires only 1.91 M parameters and 5.21 G floating-point operations (FLOPs). The detection precision(P), recall(R), mAP_0.5, and mAP_0.50:0.95 achieved are 89.44%, 88.82%, 92.99%, and 80.38%. Compared with the YOLO v8n, CSGD-YOLO improves performance in terms of accuracy, model size, parameter number, and floating-point operation counts by 1.39, 1.43, 1.77, and 2.95 percentage points, respectively. Therefore, CSGD-YOLO outperforms existing mainstream target detection models in detection performance and model complexity, making it suitable for detecting corn seed germination status and providing a reference for rapid germination rate detection.

Accurate detection and counting of rice ears serve as a critical indicator for yield estimation, but the complex conditions of paddy fields limit the efficiency and precision of traditional sampling methods. We propose REU-YOLO, a model specifically designed for UAV low-altitude remote sensing to collect images of rice ears, to address issues such as high-density and complex spatial distribution with occlusion in field scenes. Initially, we combine the Additive Block containing Convolutional Additive Self-attention (CAS) and Convolutional Gated Linear Unit (CGLU) to propose a novel module called Additive-CGLU-C2F (AC-C2f) as a replacement for the original C2f in YOLOv8. It can capture the contextual information between different regions of images and improve the feature extraction ability of the model, introduce the Dropblock strategy to reduce model overfitting, and replace the original SPPF module with the SPPFCSPC-G module to enhance feature representation and improve the capacity of the model to extract features across varying scales. We further propose a feature fusion network called Multi-branch Bidirectional Feature Pyramid Network (MBiFPN), which introduces a small object detection head and adjusts the head to focus more on small and medium-sized rice ear targets. By using adaptive average pooling and bidirectional weighted feature fusion, shallow and deep features are dynamically fused to enhance the robustness of the model. Finally, the Inner-PloU loss function is introduced to improve the adaptability of the model to rice ear morphology. In the self-developed dataset UAVR, REU-YOLO achieves a precision (P) of 90.76%, a recall (R) of 86.94%, an mAP_0.5 of 93.51%, and an mAP_0.5:0.95 of 78.45%, which are 4.22%, 3.76%, 4.85%, and 8.27% higher than the corresponding values obtained with YOLOv8 s, respectively. Furthermore, three public datasets, DRPD, MrMT, and GWHD, were used to perform a comprehensive evaluation of REU-YOLO. The results show that REU-YOLO indicates great generalization capabilities and more stable detection performance.

As one of the world’s most important staple crops providing food, feed, and industrial raw materials, corn requires precise kernel detection for seed phenotype analysis and seed quality examination. In order to achieve precise and rapid detection of corn seeds, this study proposes a lightweight corn seed kernel rapid detection model based on YOLOv11n (LWCD-YOLO). Firstly, a lightweight backbone feature extraction module is designed based on Partial Convolution (PConv) and an efficient multi-scale attention module (EMA), which reduces model complexity while maintaining model detection performance. Secondly, a cross layer multi-scale feature fusion module (MSFFM) is proposed to facilitate deep feature fusion of low-, medium-, and high-level features. Finally, we optimized the model using the WIOU bounding box loss function. Experiments were conducted on the collected Corn seed kernel detection dataset, and LWCD-YOLO only required 1.27 million (M) parameters and 3.5 G of FLOPs. Its precision (P), mean Average Precision at 0.50 (mAP_0.50), and mean Average Precision at 0.50:0.95 (mAP_0.50:0.95) reached 99.978%, 99.491%, and 99.262%, respectively. Compared to the original YOLOv11n, the model size, parameter count, and computational complexity were reduced by 50%, 51%, and 44%, respectively, and the FPS was improved by 94%. The detection performance, model complexity, and detection efficiency of LWCD-YOLO are superior to current mainstream object detection models, making it suitable for fast and precise detection of corn seeds. It can provide guarantees for achieving seed phenotype analysis and seed quality examination.

The case of randomly scattered maize seeds presents the problem of complex background and high density, making detection difficult. To address this challenge, this paper proposes an improved YOLOv8n model (OBW-YOLO) for detecting damaged maize seeds. The introduction of the C2f-ODConv module enhances the model’s ability to extract damaged features, especially in complex scenarios, allowing for better capture of local information. The improved BIMFPN module optimizes the fusion of shallow and deep features, reduces detail loss, and improves detection accuracy. To accelerate model convergence and improve detection precision, the traditional bounding box loss function has been replaced by WIoU, significantly enhancing both accuracy and convergence speed. Experimental results show that the OBW-YOLO model achieves a detection accuracy of 93.6%, with mAP@0.5 and mAP@0.5:0.95 reaching 97.6% and 84.8%, respectively, which represents an improvement of 2.5%, 1%, and 1.2% compared to previous models. Additionally, the number of parameters and model sizes have been reduced by 33% and 22.5%, respectively. Compared to other YOLO models, OBW-YOLO demonstrates significant advantages in both accuracy and mAP. Ablation experiments further validate the effectiveness of the improvements, and heatmap analysis shows that the improved model is more precise in capturing the damaged features of maize seeds. These improvements enable the OBW-YOLO model to achieve significant performance gains in maize seed damage detection, providing an effective solution for the automated quality inspection of maize seeds in the agricultural field.