The accurate identification of overlapping Agaricus bisporus in a factory environment is one of the challenges faced by automated picking. In order to better segment the complex adhesion between Agaricus bisporus, this paper proposes a segmentation recognition algorithm for overlapping Agaricus bisporus. This algorithm calculates the global gradient threshold and divides the image according to the image edge gradient feature to obtain the binary image. Then, the binary image is filtered and morphologically processed, and the contour of the overlapping Agaricus bisporus area is obtained by edge detection in the Canny operator, the convex hull and concave area are extracted for polygon simplification, and the vertices are extracted using Harris corner detection to determine the segmentation point. After dividing the contour fragments by the dividing point, the branch definition algorithm is used to merge and group all the contours of the same Agaricus bisporus. Finally, the least squares ellipse fitting algorithm and the minimum distance circle fitting algorithm are used to reconstruct the outline of Agaricus bisporus, and the demand information of Agaricus bisporus picking is obtained. The experimental results show that this method can effectively overcome the influence of uneven illumination during image acquisition and be more adaptive to complex planting environments. The recognition rate of Agaricus bisporus in overlapping situations is more than 96%, and the average coordinate deviation rate of the algorithm is less than 1.59%.

In industrial environments, steel plate surface inspection plays a crucial role in quality control. However, vibrations during laser scanning can significantly impact measurement accuracy. While traditional vibration compensation methods rely on complex dynamic modeling, they often face challenges in practical implementation and generalization. This paper introduces a novel point cloud vibration compensation algorithm that combines an improved Gaussian–Laplacian filter with adaptive local feature analysis. The key innovations include (1) an FFT-based vibration factor extraction method that effectively identifies vibration trends, (2) an adaptive windowing strategy that automatically adjusts based on local geometric features, and (3) a weighted compensation mechanism that preserves surface details while reducing vibration noise. The algorithm demonstrated significant improvements in signal-to-noise ratio: 15.78% for simulated data, 6.81% for precision standard parts, and 12.24% for actual industrial measurements. Experimental validation confirms the algorithm’s effectiveness across different conditions. This approach achieved a practical, implementable solution for surface inspection in steel plate surface inspection.

The precise measuring and control of fiber tension are critically important for enhancing structural and mechanical properties in spinning processes, as tension directly influences orientation, crystallinity, and mechanical properties. However, current tension measurement methods primarily operate offline and lack real-time measuring capabilities. A non-contact fiber tension detection system is introduced to investigate the effects of draw tension and its uniformity on the structure and mechanical properties of polyester fibers. During experiments conducted at a spinning speed of 1200 m/min across different draw ratios, the non-contact system demonstrated strong agreement with the contact tension detector. The results showed that increasing the tension from 34 cN to 164 cN reduced the monofilament diameter from 39.61 µm to 20.35 µm. Simultaneously, the orientation factor nearly tripled, while crystallinity increased from 55.72% to 77.39%. Mechanical testing revealed a 50.96% improvement in breaking strength, rising from 1.57 to 2.37 cN/dtex, accompanied by a significant decrease in elongation at break from 275.55% to 34.95%. However, tension fluctuations, characterized by an average fluctuation coefficient increase from 4.51% to 18.18%, caused diameter inconsistency. These fluctuations also reduced the orientation factor by 10.78%, lowered crystallinity, and substantially deteriorated mechanical properties. These findings underscore the critical importance of real-time, online tension monitoring for ensuring polyester fiber quality and performance during production.

The photocatalytic reduction of hexavalent chromium (Cr(VI)) in wastewater represents a critical environmental challenge, given its high toxicity and mobility in wastewater. Conventional optimization methods relying on repetitive batch experiments with excessive reagent consumption. Herein, we proposed a synergistic experimental–deep learning approach to realize efficient photocatalytic reduction of Cr(VI). Sludge and cellulose precursors were used to prepare N-doped porous carbon through a green fabrication method at low temperature. After optimizing the composition and experimental factors, the removal efficiency of Cr(VI) could reach 92.7% after visible light irradiation in an acidic environment. The high efficiency originated from the coupled adsorption and photocatalytic mechanism. Innovatively, after comparing three algorithms, TCN and Transformer were employed to predict the desirable removal efficiency with <5% prediction error under different reaction conditions. This work highlights the novel experimental–deep learning method for heavy metal remediation using N-doped waste-derived porous carbon.

Vision-based estrus monitoring for goats has matured rapidly, yet endocrine preliminary feasibility evaluation in indigenous breeds is largely missing. Here we conducted a preliminary feasibility evaluation of whether behavioral estrus events captured by a machine vision pipeline align with reproductive endocrine dynamics in Chongming white goats. Ten multiparous does were monitored continuously for 21 days under standard housing conditions; daily jugular samples yielded 206 paired serum measurements of estradiol (E2), FSH, LH, and progesterone (P4). Behavioral events were extracted from infrared video by a YOLOv8-seg + MMPose pipeline and cross-checked against teaser-buck confirmation. Hormone profiles differed markedly between estrus and non-estrus windows (all p < 0.001): estradiol rose from 14.81 ± 4.17 to 21.79 ± 2.10 ng/L, and progesterone fell from 2386.70 ± 466.93 to 1719.68 ± 157.52 pmol/L. Estrus status correlated moderately with E2 (r = 0.459), FSH (r = 0.435), and LH (r = 0.446), and inversely with P4 (r = −0.406); the E2/P4 ratio produced the most pronounced per-event peak and is proposed as a preliminary sensitive auxiliary indicator. Vision-based detection reached 86.96% accuracy. The data indicate that camera-derived behavioral markers track real physiological state changes in this breed, supporting the deployment of vision-based estrus screening as a first-pass tool, with hormone assays reserved for confirmation in commercial Chongming white goat herds.