Ethylene, an important phytohormone, significantly influences plant growth and the ripeness of fruits and vegetables. During the transportation and storage of agricultural products, excessive ethylene can lead to economic losses due to rapid deterioration. Metal oxide semiconductor (MOS)-based chemo-resistive sensors are a promising technology for the detection of ethylene due to their low cost, high sensitivity, portability, etc. This review comprehensively summarizes the materials, fabrications, agricultural applications, and sensing mechanisms of these sensors. Moreover, the current challenges are highlighted and the potential solutions are proposed.

To improve the wear resistance of 45 steel surfaces, a Ni−P alloy coating was prepared on the surface of 45 steel with an immersion-assisted jet-electrodeposition technology. Scanning electron microscopy, energy dispersive spectrometry, X-ray diffraction and confocal microscopy were used in testing the surface morphology, composition, structure, grain size, and wear scar parameters of the coating. The effect of immersion-assisted jet-electrodeposition on the wear resistance of Ni−P alloy coating at current densities of 20–60 A·cm⁻² were explored and analyzed. Results showed that the surface quality, microhardness, and wear resistance of Ni−P alloy coatings prepared through immersion-assisted jet-electrodeposition were improved compared with those of the coatings prepared through traditional jet-electrodeposition. With the increase in the current density, the surface cell structure of the alloy coating was refined, the flatness was improved, the surface Ni content was increased, the grain size was refined, and the coating thickness, the microhardness, and wear resistance showed a trend of first increasing and then decreasing. The best surface quality of the coating was observed at a current density of 50 A·cm⁻². Moreover, the unit cell structure was obvious, the surface was flat and dense, the coating thickness was the largest, reaching 21.42 μm, the highest Ni content was obtained (98.25 wt.%), the smallest grain size (6.6 nm) was obtained, the microhardness of the coating reached a maximum value (725.58 HV_0.1), and the best wear resistance was observed.

Ni-P-SiC composite coating was prepared on 45 steel surfaces through sandblasting and scanning electrodeposition to explore the relationship between element penetration region and composite coating properties. The single-factor control variable method with particle concentration as the research variable was used. Results showed that with the gradually increasing concentration of SiC nanoparticles, a trend of first increasing and then gradually decreasing was observed for the surface and cross-sectional microstructure of the coating, interpenetration ability of the elements, adhesion performance, and corrosion resistance. The best deposition quality of the coating was obtained when the concentration of SiC nanoparticles was 3 g·L⁻¹. For cross-sectional microstructure, the scratch test revealed that the maximum coating thickness was 17.3 μm, the maximum range of elemental penetration region was 28.39 μm, and the maximum adhesion of the composite coating was 36.5 N. The electrochemical test showed that the composite coating had a −0.30 V self-corrosion potential and 8.45 × 10⁻⁷ A·cm⁻² self-corrosion current density, the slowest corrosion rate. In addition, the composite coating had the best corrosion resistance and the largest impedance arc radius corresponding to an equivalent impedance value R₂ of 3108 Ω.

Accurate path tracking is crucial for greenhouse robots operating in complex environments. However, traditional curve tracking algorithms suffer from low tracking accuracy and large tracking errors. This study aim to develop a high precision greenhouse autonomous following robot, use ANSYS Workbench 19.2 to perform stress and deformation analysis on the robot, then propose a path tracking method based on Linear Quadratic Regulator (LQR) to optimize the pure tracking to ensure high precision curved path tracking for curved tracking, finally perform a comparative simulation analysis in MATLAB R2024a. The structural analysis shows that the maximum equivalent stress is 196 MPa and the maximum deformation is 1.73 mm under a load of 600 kg, which are within the yield limit of 45 steel. Simulation results demonstrate that at a speed of 2 m/s, the conventional Pure Pursuit algorithm incurs a maximum lateral error of 0.3418 m and a heading error of 0.2669 rad under high curvature conditions. By contrast, the LQR–Pure Pursuit algorithm reduces the peak lateral error to 0.0904 m and confines the heading error to approximately 0.0217 rad. Experimental validation yielded an RMSE of 0.018 m for lateral error and 0.016 m for heading error. These findings confirm that the designed robot can sustain its payload under most operating scenarios and that the proposed tracking strategy effectively suppresses deviations and improves path-following accuracy.

A simple mathematical model was developed to predict the machined depth and surface profile in laser surface texturing of micro-channels using a picosecond laser. Fabrication of micro-craters with pulse trains of different numbers was initially performed. Two baseline values from the created micro-craters were used to calculate the estimated simulation parameters. Thereafter, the depths and profiles with various scanning speeds or adjacent intervals were simulated using the developed model and calculated parameters. Corresponding experiments were conducted to validate the developed mathematical model. An excellent agreement was obtained for the predicted and experimental depths and surface profiles. The machined depth decreased with the increase of scanning speed or adjacent interval.