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Fruit flies are one of the most harmful insect species to fruit yields. In AlertTrap, implementation of Single-Shot Multibox Detector (SSD) architecture with different state-of-the-art backbone feature extractors such as MobileNetV1 and MobileNetV2 appears to be potential solutions for the real-time detection problem. SSD-MobileNetV1 and SSD-MobileNetV2 perform well and result in AP at 0.5 of 0.957 and 1.0, respectively. You Only Look Once (YOLO) v4-tiny outperforms the SSD family with 1.0 in AP at 0.5; however, its throughput velocity is considerably slower, which shows SSD models are better candidates for real-time implementation. We also tested the models with synthetic test sets simulating expected environmental disturbances. The YOLOv4-tiny had better tolerance to these disturbances than the SSD models. The Raspberry Pi system successfully gathered environmental data and pest counts, sending them via email over 4 G. However, running the full YOLO version in real time on Raspberry Pi is not feasible, indicating the need for a lighter object detection algorithm for future research. Among model candidates, YOLOv4-tiny generally performs best, with SSD MobileNetV2 also comparable and sometimes better, especially in scenarios with synthetic disturbances. SSD models excel in processing time, enabling real-time, high-accuracy detection. TFLITE versions of SSD models also process faster than their inference graph on TPU hardware, suggesting real-time implementation on edge devices like the Google Coral Dev Board. The results demonstrate the feasibility of real-time implementation of the fruit fly detection models on edge devices with high performance. In addition, YOLOv4-tiny is shown to be the most probable candidate because YOLOv4-tiny demonstrates a robust testing performance toward citrus fruit fly detection. Nevertheless, SSD-MobileNetV2 will be the better model, considering the inference time.

Osteoarthritis is a prevalent chronic health condition that is mostly associated with the degeneration of joint cartilage due to aging or abnormal mechanical stress in the joint. Intraarticularly injected nanoparticle‐based lubricants decrease the friction between damaged cartilage surfaces, thus preventing their further degradation; however, the effectiveness of currently used nanoparticle‐based lubricants is limited by their short retention time in the joint space. To address this challenge, cationically modified biosourced phytoglycogen nanolubricants are utilized, which electrostatically bind to the cartilage components. The conjugation of the nanoparticles with red‐emissive fluorescent carbon dots enables in vivo studies of their retention in the joint. The hytoglycogen nanoconjugates exhibit high colloidal stability in physiological conditions, provide a friction coefficient of 10−3–10−2 between the sliding surfaces under physiologically relevant pressures, strongly bind to the major cartilage surface components, and show significantly prolonged retention time in the joint in vivo, with a fourfold increase in half‐life in comparison with conventionally used hyaluronic acid injectant. These properties make these functionalized phytoglycogen nanoparticles a highly promising candidate for joint lubrication. [ABSTRACT FROM AUTHOR]