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Constrained by the performance of edge devices and real time (RT) processing technology, the existing hyperspectral target detection algorithms often struggle to rapidly distinguish targets from complex background pixels during real-time detection. To address this issue, this article proposes a new real-time cloud-edge selective background energy constrained (CE-SBEC) hyperspectral target detection algorithm. This algorithm aims to obtain detection results in real-time after capturing new data. Moreover, it conducts in-depth analysis based on existing detection results and updates the algorithm’s internal data to enhance its capabilities in terms of global background annihilation (GBA) and complex background suppression (CBS). Consequently, it improves the accuracy of subsequent real-time detection results. To enhance the resource utilization, this article deploys various task nodes of the algorithm separately on both the cloud and the edge, enabling collaborative execution of the CE-SBEC algorithm. In our context, edge devices are airborne equipment designed for the rapid acquisition and processing of data at the site of data collection, while cloud computing devices refer to high-performance computing clusters situated at a significant distance from the data collection site. Experimental results demonstrate that compared with existing detection algorithms, our newly proposed method achieves more accurate detection results while ensuring real-time performance.

Presently, there has been a noticeable rise in the consumption of poultry meat within the general population, particularly focusing on poultry sourced from alternative rearing systems as opposed to intensive ones. This study evaluated the impact of incorporating house cricket (Acheta domesticus,AD) larvae meal into the diet of 2 slow-growing chicken strains on their carcass traits and meat quality. A total of 256 one-day-old male chicks were utilized, with 128 belonging to the Red (R) strain and 128 to the Naked Neck (NN) strain. Chicks from each strain were randomly assigned to 16 replicates of 8 chicks each. Eight replicates were allocated to 2 distinct treatments (n=64chicks/treatment) based on the diet employed: the control treatments for the Red (R-C) and Naked Neck (NN-C) strains were fed soybean meal, while the other 2 treatments were fed AD meal (R-AD and NN-AD). Three different rations were used throughout the growth cycle of the animals, tailored to meet the nutritional needs of the birds. All chickens were slaughtered at 95 d of age, and eight chickens per treatment were randomly chosen to assess carcass traits and meat quality. Near-infrared reflectance spectroscopy (NIRS) was employed to classify meat based on the feed and the chick strain, achieving a correct discrimination of 100% of the samples. The carcass traits exhibited significant alterations due to the inclusion of insect larval meal in the diet, resulting in reduced values across all parameters for chickens consuming AD meal, irrespective of genotype. The pH and cooking losses were generally higher for chicks fed cricket meal, as occurred with moisture. However, protein content was lower in the meat of chicks consuming cricket meal. Fat content showed lower values for NN chickens. There were minimal differences in the fatty acid profile, with lower C18:3n-3 values for chickens fed cricket meal. Therefore, including Acheta domesticusmeal in the diets of slow-growing chick strains is a viable alternative to replace soybean meal. It provides meat quality characteristics comparable to those obtained in conventional systems.