Eggs are a rich food source of proteins, fats, vitamins, minerals, and other nutrients. However, the egg industry faces some challenges such as microbial invasion due to environmental factors, leading to damage and reduced usability. Therefore, detecting the freshness of raw eggs using various technologies, including traditional and non-destructive methods, can overcome these challenges. As the traditional methods of assessing egg freshness are often subjective and time-consuming, modern non-destructive technologies, including near-infrared (NIR) spectroscopy, Raman spectroscopy, fluorescence spectroscopy, computer vision (color imaging), hyperspectral imaging, electronic noses, and nuclear magnetic resonance, have offered objective and rapid results to address these limitations. The current review summarizes and discusses the recent advances and developments in applying non-destructive technologies for detecting raw egg freshness. Some of these technologies such as NIR spectroscopy, computer vision, and hyperspectral imaging have achieved an accuracy of more than 96% in detecting egg freshness. Therefore, this review provides an overview of the current trends in the state-of-the-art non-destructive technologies recently utilized in detecting the freshness of raw eggs. This review can contribute significantly to the field of emerging technologies in this research track and pique the interests of both food scientists and industry professionals.

Cassava is a staple crop in developing countries because its starchy roots provide essential dietary carbohydrates. The aim of this research was to conduct a comprehensive inquiry and scientific evaluation of the nutritional value of cassava tubers. Eight nutritional characteristics were examined in native and imported cassava variants: starch, reduced sugar, anthocyanins, protein, dietary fiber, quinic acid, vitamin C, and dry matter content. Principal component analysis (PCA) was conducted to minimize the dimensionality of the nutritional markers. A scientific assessment technique was developed to calculate a composite score for the various cassava samples. Analysis of the data revealed noticeable variance among the samples’ nutritional indicators, suggesting varying degrees of association. Starch had a substantial positive link with lower sugar, protein, and dry matter content (p < 0.01). Anthocyanins and quinic acid interacted favorably (p < 0.05), and a positive link between protein and dry matter content was observed (p < 0.05); however, protein and dietary fiber interacted negatively (p < 0.05). The contribution rate of the top three PCA factors was over 76%, demonstrating that these factors incorporated the primary information acquired from the eight original nutritional indices, while maintaining excellent representativeness and impartiality. The experimental results showed a preliminary nutritional grade for 22 cassava tuber samples. The top five types were Guangxi Muci, Gui Cassava 4, Glutinous Rice Cassava, Huifeng 60, and Dongguan Hongwei. In the cluster analysis, the levels of similarity between the data showed that the 22 types of cassava tubers could be grouped into five categories, each with their own set of nutrients. This study promotes the directed breeding of cassava species and offers a theoretical foundation for creating and using various cassava varieties. Furthermore, this work lays the groundwork for a systematic and dependable technique for the quality assessment, comprehensive evaluation, and reasonable classification of cassava species and similar crops.

This study investigates the geometrical and mechanical properties of two cassava varieties: ‘Newly Elected No. 48’ and ‘Jin Yue HRMUS’. We analyzed various properties, including width, length, geometric diameter, thickness, compression firmness, shape ratio, surface area, shear strength, rupture force, and shear rupture force (SRF), at different moisture content (MC) levels ranging from 35.0 to 80.0% wet basis. Our findings indicate that all mechanical properties of cassava (MPC) were significantly influenced by moisture content within this range. Specifically, as moisture content decreases, the compression rupture force (CRF) increases linearly in the vertical direction, while a quadratic relationship is observed in the horizontal direction, characterized by an initial increase followed by a decrease. Additionally, a quadratic relationship was established between moisture content and firmness. In contrast, shear fracture force and strength exhibited a quadratic relationship that decreased initially and then increased with rising moisture content. Furthermore, both compression and shear fracture forces were significantly higher in the vertical loading direction compared to the horizontal direction. Notably, the ‘Jin Yue HRMUS’ variety demonstrated greater mechanical strength than the ‘Newly Elected No. 48’. These findings provide valuable insights for developing, optimizing, and evaluating cassava harvesting and processing machinery.

Egg weight monitoring provides critical data for calculating the feed-to-egg ratio, and improving poultry farming efficiency. Installing a computer vision monitoring system in egg collection systems enables efficient and low-cost automated egg weight measurement. However, its accuracy is compromised by egg clustering during transportation and low-contrast edges, which limits the widespread adoption of such methods. To address this, we propose an egg measurement method based on a computer vision and multi-feature extraction and regression approach. The proposed pipeline integrates two artificial neural networks: Central differential-EfficientViT YOLO (CEV-YOLO) and Egg Weight Measurement Network (EWM-Net). CEV-YOLO is an enhanced version of YOLOv11, incorporating central differential convolution (CDC) and efficient Vision Transformer (EfficientViT), enabling accurate pixel-level egg segmentation in the presence of occlusions and low-contrast edges. EWM-Net is a custom-designed neural network that utilizes the segmented egg masks to perform advanced feature extraction and precise weight estimation. Experimental results show that CEV-YOLO outperforms other YOLO-based models in egg segmentation, with a precision of 98.9%, a recall of 97.5%, and an Average Precision (AP) at an Intersection over Union (IoU) threshold of 0.9 (AP90) of 89.8%. EWM-Net achieves a mean absolute error (MAE) of 0.88 g and an R² of 0.926 in egg weight measurement, outperforming six mainstream regression models. This study provides a practical and automated solution for precise egg weight measurement in practical production scenarios, which is expected to improve the accuracy and efficiency of feed-to-egg ratio measurement in laying hen farms.

This study investigates the collision model of cassava seed stems in precision planters. Utilizing a physical property analyzer and a custom test platform based on collision dynamics principles, we measured and analyzed the forces and recovery coefficients of seed stem collisions. Mixed orthogonal and one-way tests were conducted to identify the main factors affecting the collision recovery coefficient of seed stems, including collision contact material, drop height, seed stem mass, moisture content, drop direction, and seed stem variety. The results from the orthogonal tests indicated that the factors influencing the collision recovery coefficient were ranked as follows: collision contact material > drop height > seed stem mass > moisture content > drop direction > seed stem variety. Notably, the effects of impact contact material, drop height, stem mass, and moisture content were significant, while the effects of drop direction and seed stem variety were relatively insignificant. The one-way test results revealed that the collision recovery coefficients for cassava seed stems with structural steel Q235, rubber sheet, seed stems, and sandy loam soil decreased progressively, with values for SC205 being 0.8172, 0.6975, 0.6649, and 0.6341, respectively, and values for GR4 being 0.7796, 0.7132, 0.6913, and 0.6134, respectively. Furthermore, as drop height increased, the collision recovery coefficient of cassava seed stems decreased; similarly, higher stem mass and moisture content correlated with lower coefficients. To minimize impact during critical stages of cassava planting, transportation, and processing, materials with lower recovery coefficients should be prioritized in equipment design. Incorporating rubber coatings can effectively mitigate collision effects in components such as seed supply and planting mechanisms. These findings provide valuable insights for designing and enhancing key mechanical features in machinery used for planting, transporting, and processing cassava.