To address several challenges, including low efficiency, significant damage, and high costs, associated with the manual harvesting of Agaricus bisporus, in this study, a machine vision-based intelligent harvesting device was designed according to its agronomic characteristics and morphological features. This device mainly comprised a frame, camera, truss-type robotic arm, flexible manipulator, and control system. The FES-YOLOv5s deep learning target detection model was used to accurately identify and locate Agaricus bisporus. The harvesting control system, using a Jetson Orin Nano as the main controller, adopted an S-curve acceleration and deceleration motor control algorithm. This algorithm controlled the robotic arm and the flexible manipulator to harvest Agaricus bisporus based on the identification and positioning results. To confirm the impact of vibration on the harvesting process, a stepper motor drive test was conducted using both trapezoidal and S-curve acceleration and deceleration motor control algorithms. The test results showed that the S-curve acceleration and deceleration motor control algorithm exhibited excellent performance in vibration reduction and repeat positioning accuracy. The recognition efficiency and harvesting effectiveness of the intelligent harvesting device were tested using recognition accuracy, harvesting success rate, and damage rate as evaluation metrics. The results showed that the Agaricus bisporus recognition algorithm achieved an average recognition accuracy of 96.72%, with an average missed detection rate of 2.13% and a false detection rate of 1.72%. The harvesting success rate of the intelligent harvesting device was 94.95%, with an average damage rate of 2.67% and an average harvesting yield rate of 87.38%. These results meet the requirements for the intelligent harvesting of Agaricus bisporus and provide insight into the development of intelligent harvesting robots in the industrial production of Agaricus bisporus.

Peony mead, an emerging fermented beverage, has attracted attention because of its unique flavor and health benefits. The dynamic changes in sensory quality and the molecular mechanisms involved during post-fermentation are still unclear, limiting its industrial production. In this study, GC-IMS (gas chromatography-ion mobility spectrometry) and UHPLC-MS/MS (ultrahigh-performance liquid chromatography–tandem mass spectrometry) were employed to systematically analyze the variations in aroma and quality of peony mead across aging stages. During the aging process, titratable acid content increased significantly, while soluble solids and reducing sugars decreased. Total phenol content initially rose but subsequently declined. Sensory analysis demonstrated that the sweet–acid balance and polyphenol content were critical in shaping the sensory characteristics of the product. Seventeen key volatile metabolites were identified via GC-IMS, with the 2-methyl-1-propanol dimer/polymer and 3-methyl-1-butanol dimer/polymer serving as potential characteristic markers. These key volatile metabolites underwent physicochemical reactions, yielding complex and coordinated aroma characteristics. UHPLC–MS/MS analysis revealed that nonvolatile metabolites changed significantly, which were driven by nonenzymatic reactions such as redox reactions, hydrolysis, and condensation. In addition, correlation analysis identified mechanisms by which key metabolites potentially contributed to sensory properties such as floral aroma, fruit fragrance, sweetness, sourness, etc. This study provided insights into quality changes during aging and supported the development of high-quality fermented beverages.

The Auricularia auricula harvester has low harvesting productivity, low qualification rate, and high breakage rate, and the lack of research on key operating parameters has become a bottleneck restricting the large-scale development industry. In view of the difficulty of industrialization and promotion of Auricularia auricula harvesting equipment, this paper combines the research on the physical properties of Auricularia auricula, adopts a quadratic orthogonal rotation combination design test and response surface analysis method to carry out the optimization and test of the key parameters of the harvesting equipment of Auricularia auricula, including the speed of the tray fungus stick (A), the frequency of the harvesting knife (B), and the distance of the harvesting knife (C). Design-Expert software was used to analyse the data and investigate the influence of each parameter and its interaction on the harvesting productivity, qualified rate, and breakage rate. The test results show that the factors affecting the harvesting productivity are in order: (A) > (B) > (C); the factors affecting the harvest qualification rate are in order: (C) > (B) > (A); and the factors affecting the harvesting damage rate are in order: (C) > (B) > (A). The multi-objective optimization results show that the optimal parameter combination of the key parameters of the Auricularia auricula harvesting equipment operation is that the tray speed is 308.17 r/min, the harvesting knife frequency is 10.78 Hz, the spacing between the harvesting knives is 84.67 mm, the productivity is 363 bags/h, the pass rate is 94.26%, and the breakage rate is 2.40%. The field production application under this optimized parameter combination shows that the productivity of the Auricularia auricula mechanized harvester is 346 bars/h, the qualified rate is 91.43%, and the breakage rate is 3.19%. This study can provide a reference for improving the quality of Auricularia auricula harvesting equipment.