The detection of defects on the surface is of great importance for both the production and the application of strip steel. In order to detect the defects accurately, an improved YOLOv7-based model for detecting strip steel surface defects is developed. To enhances the ability of the model to extract features and identify small features, the ConvNeXt module is introduced to the backbone network structure, and the attention mechanism is embedded in the pooling module. To reduce the size and improves the inference speed of the model, an improved C3 module was used to replace the ELAN module in the head. The experimental results show that, compared with the original models, the mAP of the proposed model reached 82.9% and improved by 6.6%. The proposed model can satisfy the need for accurate detection and identification of strip steel surface defects.

Cassava is a staple crop in developing countries because its starchy roots provide essential dietary carbohydrates. The aim of this research was to conduct a comprehensive inquiry and scientific evaluation of the nutritional value of cassava tubers. Eight nutritional characteristics were examined in native and imported cassava variants: starch, reduced sugar, anthocyanins, protein, dietary fiber, quinic acid, vitamin C, and dry matter content. Principal component analysis (PCA) was conducted to minimize the dimensionality of the nutritional markers. A scientific assessment technique was developed to calculate a composite score for the various cassava samples. Analysis of the data revealed noticeable variance among the samples’ nutritional indicators, suggesting varying degrees of association. Starch had a substantial positive link with lower sugar, protein, and dry matter content (p < 0.01). Anthocyanins and quinic acid interacted favorably (p < 0.05), and a positive link between protein and dry matter content was observed (p < 0.05); however, protein and dietary fiber interacted negatively (p < 0.05). The contribution rate of the top three PCA factors was over 76%, demonstrating that these factors incorporated the primary information acquired from the eight original nutritional indices, while maintaining excellent representativeness and impartiality. The experimental results showed a preliminary nutritional grade for 22 cassava tuber samples. The top five types were Guangxi Muci, Gui Cassava 4, Glutinous Rice Cassava, Huifeng 60, and Dongguan Hongwei. In the cluster analysis, the levels of similarity between the data showed that the 22 types of cassava tubers could be grouped into five categories, each with their own set of nutrients. This study promotes the directed breeding of cassava species and offers a theoretical foundation for creating and using various cassava varieties. Furthermore, this work lays the groundwork for a systematic and dependable technique for the quality assessment, comprehensive evaluation, and reasonable classification of cassava species and similar crops.

This study investigates the geometrical and mechanical properties of two cassava varieties: ‘Newly Elected No. 48’ and ‘Jin Yue HRMUS’. We analyzed various properties, including width, length, geometric diameter, thickness, compression firmness, shape ratio, surface area, shear strength, rupture force, and shear rupture force (SRF), at different moisture content (MC) levels ranging from 35.0 to 80.0% wet basis. Our findings indicate that all mechanical properties of cassava (MPC) were significantly influenced by moisture content within this range. Specifically, as moisture content decreases, the compression rupture force (CRF) increases linearly in the vertical direction, while a quadratic relationship is observed in the horizontal direction, characterized by an initial increase followed by a decrease. Additionally, a quadratic relationship was established between moisture content and firmness. In contrast, shear fracture force and strength exhibited a quadratic relationship that decreased initially and then increased with rising moisture content. Furthermore, both compression and shear fracture forces were significantly higher in the vertical loading direction compared to the horizontal direction. Notably, the ‘Jin Yue HRMUS’ variety demonstrated greater mechanical strength than the ‘Newly Elected No. 48’. These findings provide valuable insights for developing, optimizing, and evaluating cassava harvesting and processing machinery.

Accurate and efficient pixel-wise segmentation of wood panels is crucial for enabling machine vision technologies to optimize the sawing process. Traditional image segmentation algorithms often struggle with robustness and accuracy in complex industrial environments. To address these challenges, this paper proposes an improved DeepLabV3+-based segmentation algorithm for wood panel images. The model incorporates a lightweight MobileNetV3 backbone to enhance feature extraction, reducing the number of parameters and computational complexity while minimizing any trade-off in segmentation accuracy, thereby increasing the model’s processing speed. Additionally, the introduction of a coordinate attention (CA) mechanism allows the model to better capture fine details and local features of the wood panels while suppressing interference from complex backgrounds. A novel feature fusion mechanism is also employed, combining shallow and deep network features to enhance the model’s ability to capture edges and textures, leading to improved feature fusion across scales and boosting segmentation accuracy. The experimental results demonstrate that the improved DeepLabV3+ model not only achieves superior segmentation performance across various wood panel types but also significantly increases segmentation speed. Specifically, the model improves the mean intersection over union (MIoU) by 1.05% and boosts the processing speed by 59.2%, achieving a processing time of 0.184 s per image.

Fertilizer can improve the yield of crops per unit area, and uniform fertilizer discharge can improve the fertilizer utilization rate. Therefore, it is meaningful to improve the performance of fertilizer-discharge devices in order to improve the modernization level of crop field fertilizer management. To address the problems of operational smoothness, stability and poor uniformity of fertilizer discharge, and other difficult problems encountered with strip fertilizer-discharge devices, this study designs a disturbed fertilizer strip-discharge device with a vertical pendulum. The main factors affecting the performance of fertilizer discharge were the wedge angle of the push-disturbing main pendulum bar (PMPB), the inclination angle of the aided-stirring pendulum pick (APP), the flow gap of the pendulum bar (FGPB), and the operation frequency of the swing-rod combination (SRC). The discrete element method (DEM) was used to establish a simulation model of the fertilizer device to explore the influence of the main factors on the performance of fertilizer discharge, with the coefficient of variation (CV) of fertilizer discharge uniformity and fertilizer discharge accuracy (FDA) used as the evaluation indices. The results show that the factors affecting the CV of fertilizer discharge uniformity and FDA were, in order of priority, the operation frequency of the SRC, the FGPB, the wedge angle of the PMPB, and the inclination angle of the APP. The optimal parameters after rounding were as follows: the wedge angle of the PMPB was 45°, the inclination angle of the APP was 46°, the operation frequency of the SRC was 188 times/min, and the FGPB was 4.5 mm. At this point, the model predicted that the CV of fertilizer discharge uniformity would be 10.53%, and that the FDA would be 3.19%. Using the optimal parameters for bench test verification, it was found that the wedge angle of the PMPB was 45°, the inclination angle of the APP was 46°, the operation frequency of the SRC was 188 times/min, the FGPB was 4.5 mm, the CV of the uniformity of the fertilizer discharge was 11.06%, and the FDA was 3.51%. In the test, the fertilizer-discharge device was stable and had good adaptability to different fertilizers. The results of this study can provide a theoretical reference for the development of precision strip-fertilizer application devices.

This study investigates the collision model of cassava seed stems in precision planters. Utilizing a physical property analyzer and a custom test platform based on collision dynamics principles, we measured and analyzed the forces and recovery coefficients of seed stem collisions. Mixed orthogonal and one-way tests were conducted to identify the main factors affecting the collision recovery coefficient of seed stems, including collision contact material, drop height, seed stem mass, moisture content, drop direction, and seed stem variety. The results from the orthogonal tests indicated that the factors influencing the collision recovery coefficient were ranked as follows: collision contact material > drop height > seed stem mass > moisture content > drop direction > seed stem variety. Notably, the effects of impact contact material, drop height, stem mass, and moisture content were significant, while the effects of drop direction and seed stem variety were relatively insignificant. The one-way test results revealed that the collision recovery coefficients for cassava seed stems with structural steel Q235, rubber sheet, seed stems, and sandy loam soil decreased progressively, with values for SC205 being 0.8172, 0.6975, 0.6649, and 0.6341, respectively, and values for GR4 being 0.7796, 0.7132, 0.6913, and 0.6134, respectively. Furthermore, as drop height increased, the collision recovery coefficient of cassava seed stems decreased; similarly, higher stem mass and moisture content correlated with lower coefficients. To minimize impact during critical stages of cassava planting, transportation, and processing, materials with lower recovery coefficients should be prioritized in equipment design. Incorporating rubber coatings can effectively mitigate collision effects in components such as seed supply and planting mechanisms. These findings provide valuable insights for designing and enhancing key mechanical features in machinery used for planting, transporting, and processing cassava.