An intelligent machine vision-based smartphone app for beef quality evaluation

Wei

et al. 2021

Adv Funct Materials

124

119

Adaptation is the most common and basic feature of living systems, which gives species or individuals a survival advantage. In particular, visual adaptation can enable organisms with a clearer understanding of the real world, thereby avoiding potential harm, which is vital for the life activities of organisms. However, current adaptive devices based on logic circuits are still facing the great challenges for large-scale integration and limited bionic functions. Therefore, the hardware impleofmentation of biological visual adaptability through the emerging photoelectric devices may provide a great opportunity for the bionic systems facing complex environments. Here, a novel adaptive device based on a mixed-dimensional van der Waals heterostructure is fabricated by using a gate-modulated 0D-CsPbBr 3-quantum-dots/2D-MoS 2 heterostructure. The device has superior electric adaptabilities and excellent optical absorption abilities owing to its special energy-band structure. The key characteristics of biological adaptation, such as accuracy, sensitivity, inactivation, and desensitization behaviors, are successfully emulated in the device based on the unique trapping-detrapping mechanism. Most importantly, with a photoelectric synergy approach, the fascinating visual adaptation function based on an environment-adjustable threshold is finally demonstrated. These results indicate that the proposed device may be very promising for the future applications of artificial visual systems and intelligent bionic robots.

Section: (11 Of 12)mentioning

confidence: 99%

Photoelectric Visual Adaptation Based on 0D‐CsPbBr₃‐Quantum‐Dots/2D‐MoS₂ Mixed‐Dimensional Heterojunction Transistor

Wei

et al. 2021

Adv Funct Materials

124

119

“…In this scope, ATMEL microcontrollers and those compatible with its development environment (IDE) are relevant [44]. Its features (free philosophy) together with low cost and size have favoured a gradual incorporation into the food traceability sector [45], combined with Android devices for meat quality control of beef [46] or for the foam quality of sparkling wines [47]. Table 2 details the most widely distributed boards integrating this open source philosophy and offering an intelligent node between temperature data reception, management, and cloud interaction as they include modules for wireless communication.…”

Section: Related Work and Technical Backgroundmentioning

confidence: 99%

Monitoring of Temperature in Retail Refrigerated Cabinets Applying IoT Over Open-Source Hardware and Software

Ramírez-Faz

Fernández-Ahumada

et al. 2020

Sensors

The control of refrigeration in the food chain is fundamental at all stages, with special emphasis on the retail stage. The implementation of information and communication technologies (IoT, open-source hardware and software, cloud computing, etc.) is representing a revolution in the operational paradigm of food control. This paper presents a low-cost IoT solution, based on free hardware and software, for monitoring the temperature in refrigerated retail cabinets. Specifically, the use of the ESP-8266-Wi-Fi microcontroller with DS18B20 temperature sensors is proposed. The ThingSpeak IoT platform is used to store and process data in the cloud. The solution presented is robust, affordable, and flexible, allowing to extend the scope of supervising other relevant parameters in the operating process (light control, energy efficiency, consumer presence, etc.).

“…Hosseinpour, Ilkhchi, and Aghbashlo [27] presented an application based on ANN embedded in a smartphone to classify beef's freshness based on texture. One hundred and sixty-seven meat samples were captured in a real environment and underwent preprocessing to define the region of interest.…”

Section: Introductionmentioning

confidence: 99%

Computer Vision and Machine Learning for Tuna and Salmon Meat Classification

2021

Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.