In this study, V x O y thin films were deposited on borosilicate glass substrates using direct current (DC) magnetron sputtering. The optoelectronic thermochromic properties of the resulting multiphase vanadium oxide thin films were investigated. As-deposited (at 280 °C) films were annealed at 350, 450, and 500 °C in an oxygen atmosphere for 30 min in a tube furnace to improve the crystallinity. Structural analysis indicated the formation of a mixed-phase vanadium oxide film consisting of VO2(B), V4O9, and V2O5 phases on the amorphous substrate after annealing above 350 °C. The results showed that the semiconductor-to-metallic phase transition temperature of the vanadium oxide film increased from 48 to 63 °C with increasing annealing temperatures. The sample annealed at 450 °C exhibited the highest variation in the infrared (IR) transmittance (ΔT IR = 28.42%) and the resistivity switch decreased by two orders of magnitude (1.4 × 10–1–2.3 × 10–3 Ω/cm). The thermal treatment temperature affected the width of the thermal hysteresis loop (H LW) and slope stiffness. A narrower H LW of 1.9 °C and a sharp slope stiffness of 8.74 were obtained for the sample annealed at 500 °C. The slope stiffness plays an important role in the fabrication of ultrafast tunable energy-saving smart windows and IR switches.

Vanadium dioxide (VO 2 )-based thin films have received considerable attention in recent years due to their superior performance in creating next-generation color-rendering materials. The near-room-temperature metal−insulator transition of VO 2 promises the advantage of active color tuning in the visible wavelength range. Although various results of dynamic color generation combined with plasmonic nanostructures are currently being investigated, so far, very few studies have addressed the visible-light optical performance of pure VO 2 thin films prepared on conventional substrates. This article shows in detail the phasetransition behavior of VO 2 thin films in the visible wavelength range of 400−750 nm prepared on glass with subsequent annealing at temperatures of 350, 400, 450, and 500 °C. The results show an anomalous phase transition reducing the overall RGB reflectivity correlated with the crystallization behavior of the VO 2 phase and scattering effect. The sample annealed at 350 °C shows the smallest phase transition at 47 °C, correlating with a crystallite size of 7 nm. The blue band reflectivity anomaly after annealing at 450 °C was considered an effect of the secondary reflection. The results of this research could play a huge role in the production of activeswitching photonic devices, color-managed reflectors, and temperature indicators.

Deep learning and machine learning (ML) technologies have been implemented in various applications, and various agriculture technologies are being developed based on image-based object recognition technology. We propose an orchard environment free space recognition technology suitable for developing small-scale agricultural unmanned ground vehicle (UGV) autonomous mobile equipment using a low-cost lightweight processor. We designed an algorithm to minimize the amount of input data to be processed by the ML algorithm through low-resolution grayscale images and image binarization. In addition, we propose an ML feature extraction method based on binary pixel quantification that can be applied to an ML classifier to detect free space for autonomous movement of UGVs from binary images. Here, the ML feature is extracted by detecting the local-lowest points in segments of a binarized image and by defining 33 variables, including local-lowest points, to detect the bottom of a tree trunk. We trained six ML models to select a suitable ML model for trunk bottom detection among various ML models, and we analyzed and compared the performance of the trained models. The ensemble model demonstrated the best performance, and a test was performed using this ML model to detect apple tree trunks from 100 new images. Experimental results indicate that it is possible to recognize free space in an apple orchard environment by learning using approximately 100 low-resolution grayscale images.

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