Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with R_P², R_C² and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

Mobile edge computing (MEC) focuses on transferring computing resources close to the user’s device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.