The classification of Stropharia rugoso-annulata is currently reliant on manual sorting, which may be subject to bias. To improve the sorting efficiency, automated sorting equipment could be used instead. However, sorting naked mushrooms in real time remains a challenging task due to the difficulty of accurately identifying, locating and sorting large quantities of them simultaneously. Models must be deployable on resource-limited devices, making it challenging to achieve both a high accuracy and speed. This paper proposes the APHS-YOLO (YOLOv8n integrated with AKConv, CSPPC and HSFPN modules) model, which is lightweight and efficient, for identifying Stropharia rugoso-annulata of different grades and seasons. This study includes a complete dataset of runners of different grades in spring and autumn. To enhance feature extraction and maintain the recognition accuracy, the new multi-module APHS-YOLO uses HSFPNs (High-Level Screening Feature Pyramid Networks) as a thin-neck structure. It combines an improved lightweight PConv (Partial Convolution)-based convolutional module, CSPPC (Integration of Cross-Stage Partial Networks and Partial Convolution), with the Arbitrary Kernel Convolution (AKConv) module. Additionally, to compensate for the accuracy loss due to lightweighting, APHS-YOLO employs a knowledge refinement technique during training. Compared to the original model, the optimized APHS-YOLO model uses 57.8% less memory and 62.5% fewer computational resources. It has an FPS (frames per second) of over 100 and even achieves 0.1% better accuracy metrics than the original model. These research results provide a valuable reference for the development of automatic sorting equipment for forest farmers.

With the recent advancement in artificial intelligence, there are new opportunities to adopt smart technologies for the sorting of materials at the beginning of the recycling value chain. An automatic bin capable of sorting the waste among paper, plastic, glass & aluminium, and residual waste was installed in public areas of Milan Malpensa airport, a context where the separate collection is challenging. First, the airport waste composition was assessed, together with the efficiency of the manual sorting performed by passengers among the conventional bins: paper, plastic, glass & aluminium, and residual waste. Then, the environmental (via the life cycle assessment - LCA) and the economic performances of the current system were compared to those of a system in which the sorting is performed by the automatic bin. Three scenarios were evaluated: i) all waste from public areas, despite being separately collected, is sent to incineration with energy recovery, due to the inadequate separation quality (S0); ii) recyclable fractions are sent to recycling according to the actual level of impurities in the bags (S0R); iii) fractions are sorted by the automatic bin and sent to recycling (S1). According to the results, the current separate collection shows a 62 % classification accuracy. Focusing on LCA, S0 causes an additional burden of 12.4 mPt (milli points) per tonne of waste. By contrast, S0R shows a benefit (-26.4 mPt/t) and S1 allows for a further 33 % increase of benefits. Moreover, the cost analysis indicates potential savings of 24.3 €/t in S1, when compared to S0.

Systems biology methods, such as transcriptomics and metabolomics, require large numbers of small model organisms, such as zebrafish embryos. Manual separation of mutant embryos from wild-type embryos is a tedious and time-consuming task that is prone to errors, especially if there are variable phenotypes of a mutant. Here we describe a zebrafish embryo sorting system with two cameras and image processing based on template-matching algorithms. In order to evaluate the system, zebrafish rx3 mutants that lack eyes due to a patterning defect in brain development were separated from their wild-type siblings. These mutants show glucocorticoid deficiency due to pituitary defects and serve as a model for human secondary adrenal insufficiencies. We show that the variable phenotypes of the mutant embryos can be safely distinguished from phenotypic wild-type zebrafish embryos and sorted from one petri dish into another petri dish or into a 96-well microtiter plate. On average, classification of a zebrafish embryo takes approximately 1 s, with a sensitivity and specificity of 87% to 95%, respectively. Other morphological phenotypes may be classified and sorted using similar techniques.