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Optimally increasing the catalytic activity of Co3O4‐based materials is crucial for promoting their practical applications for acetone detection; however, this still remains challenging. Herein, a strategy is proposed in this work to increase the catalytic activity of Co3O4 toward the oxidation of acetone through the synergistic effect of boron doping and chemical reduction, which can substantially improve the sensing performance for acetone detection. The characterization results confirm that the present strategy facilitates the formation of more oxygen vacancies and increases the content of Co2+ ions serving as active sites. As expected, the optimal sensor yields a higher response. To extend the practical application to the auxiliary diagnosis of diabetes through exhaled breath, the optimal sensors distinguished between acetone concentrations in the breath of healthy individuals and in the simulated breath of patients with diabetes. Detailed gas‐sensing measurements reveal that the enhanced acetone sensing performance is attributed to the increased catalytic activity of materials for oxidation of acetone by the Mars–van Krevelen, Langmuir–Hinshelwood, and Eley–Rideal mechanisms. This study provides new insights into the fabrication of high‐performance metal oxide‐based gas sensors by improving the catalytic activity of sensing materials. [ABSTRACT FROM AUTHOR]

• A set of far-close-range stereoscopic vision system was constructed to detect grape ear and grape stem. • The identification and localization algorithms of grape centroid and point-cutting of grape stems were proposed. • The calculation model of the point-cutting of grape stems was established. The point-cutting localization had the success rate of 92% on sunny day, 82% on sunny backlight and 86% on overcast. • The close-range vision system may add positioning compensation to the binocular vision system. • The grape-picking robot can complete the table grape-picking task within an average time of 53.4 s. To improve the picking success rate of table grapes in the process of automatic picking, it is important to accurately locate the point picking of grape ears and the cutting point of grape stems. In this research, a set of far-close-range stereoscopic vision systems was constructed to detect grape ears and grape stems. First, identification algorithms for grape ear centroid were proposed. A series of image operations were performed to complete the recognition and feature extraction of far-range grapes, including median filtering, threshold segmentation, morphological operation, executable region marking, and the extraction of the target centroid. Second, a calculation model of the cutting point of grape stems was established. The selected region of interest (ROI) of the close-range grape stem was determined to carry out edge detection and use a cumulative probability Hough transform. The straight line detection and cutting point positioning of the close-range grape stem were completed. Finally, based on LabVIEW software, the measurement and control system of the grape-picking robot was developed. Experiments verified that the identification of grape stems and localization algorithm of the cutting point had higher reliability, with success rates of 92% on sunny days, 82% during sunny backlight and 86% for overcast conditions. The grape-picking tests showed that the grape-picking robot can complete the table grape-picking task within an average time of 53.4 s under the given computer configuration. [ABSTRACT FROM AUTHOR]