Jinming Pan

Embryonic environmental conditions can influence post-hatch physiology in poultry. This study evaluated whether green-light incubation modifies post-hatch redox homeostasis, peripheral endocrine responses, and hypothalamic proteomic remodeling in Roman Pink and Hy-Line Brown chickens. Yolk hormone profiles were screened in 16 breeder strains, revealing clear strain-specific differences in serotonin (5-HT), dopamine (DA), testosterone (TES), and corticosterone (CORT). Roman Pink and Hy-Line Brown chickens were selected for further experimentation, as they showed contrasting yolk endocrine status. A total of 296 eggs from each strain (148 eggs per treatment having four replicates of 37) were incubated under light-emitting diode (LED) green-light or control incubation. Post-hatch antioxidant status, humoral immune response, endocrine indices and hypothalamic proteomes were evaluated. Strain- and age-dependent post-hatch changes in antioxidant status and endocrine profiles were noticed. Green-light incubation enhanced total antioxidant capacity by enhancing total antioxidant capacity (T-AOC), catalase (CAT) and superoxide dismutase (SOD) activity in both strains, and reduction of CORT and TES was observed in Roman Pink chickens (p < 0.05). Proteomic analysis indicated regulation of mitochondrial oxidative phosphorylation, peroxisomal metabolism, antioxidant-related pathways, and monoamine-associated proteins including tryptophan hydroxylase 2 (TPH2), solute carrier family 6 member 4 (SLC6A4), and catechol-O-methyltransferase (COMT). Correlation analyses further linked mitochondrial and antioxidant-related proteins with physiological redox and endocrine indices. These findings suggest that embryonic green-light exposure influences post-hatch antioxidant status, hepatic endocrine profiles, and hypothalamic protein abundance in a strain-dependent manner.

To address the issues of poor seed filling efficiency, low qualified seeding index, and high missed-seeding index in cassava precision planters, this study developed a dual-chamber pre-cut cassava stem filling mechanism. The structure and working principles were analyzed, identifying key factors affecting performance. By employing the discrete element method (DEM) to simulate the interaction between cassava seed stems and the filling mechanism, using statistical analysis to process experimental data, and adopting kinematic and mechanical equilibrium modeling for theoretical analysis, the structure and dimensions of the seed scoop were ultimately optimized. Subsequently, we evaluated the effects of the seed scoop speed in the first filling zone, the seed filling speed ratio, and the seed stem population thickness on performance through multi-factor simulation experiments. Based on the NSGA-II algorithm and the analytic hierarchy process, the optimal parameters were determined as follows: a seed filling speed ratio of 0.78, a seed scoop speed (first filling zone) of 0.6 m/s, and a seed stem population thickness of 290–320 mm. Bench tests under these conditions yielded a 95.31% qualified filling rate (ratio of single-segment cassava stem captured by seed scoop to total stems), 1.89% missed-filling rate (ratio of cassava stem not captured by seed scoop to total stems), and 2.80% double-filling rate (ratio of multi-segment cassava stem captured by seed scoop to total stems). Variety adaptability tests confirmed the mechanism’s robustness for precision planting. These findings offer theoretical guidance for precision planting of stalk-type crops.

The poultry industry faces growing demands for automated, high-precision egg grading systems to ensure quality control and meet market standards. Traditional methods relying on manual inspection are time-consuming, subjective, and prone to human error. Although state-of-the-art YOLO architectures have achieved high accuracy in object detection, their limited interpretability constrains adoption in agricultural contexts where transparency and biological relevance are critical. This study presents an interpretable deep learning framework based on YOLOv12 architecture for real-time egg size detection and classification. Using a dataset of 844 egg images, the performance of YOLO models (v7–v12) is compared, with YOLOv12-X achieving a mean Average Precision (mAP) of 99.4%. To enhance transparency in automated decision-making, Explainable AI (XAI) techniques, including Grad-CAM and EigenCAM, are integrated to visualize the model’s focus regions during classification. This approach not only validates the model’s alignment with biologically relevant features, such as egg contours, but also provides actionable insights for stakeholders in agriculture and food processing. This work bridges the gap between high-accuracy detection and interpretability, paving the way for smarter, more trustworthy automation in the poultry supply chain.

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 review summarizes advances in in situ formed hydrogels for soft bioelectronics, highlighting formation mechanisms, application methods, functions with design considerations, emerging applications, and future perspectives for the field.

This study examines the techno-economic feasibility of hybrid energy systems that utilize renewable sources to meet the electrical demands of a livestock and poultry farm in the New Valley Governorate of Egypt. In this study, HOMER Pro was used to explore different hybrid energy system setups that include solar panels, wind turbines, biomass generators, and battery storage. The simulations were based on accurate local weather data and real hourly electricity demand at the site. The photovoltaic/wind/biomass/battery system was the greatest economical option, with a net present cost (NPC) of 9.62 million USD and a levelized cost of energy (LCOE) of 0.716 USD/kWh. Solar energy provided the majority of the farm’s electricity (82%), while wind and biomass made smaller but important contributions. Sensitivity analyses showed that even modest improvements, like reducing capital costs or slightly increasing renewable resource availability, could make these systems even more affordable. The findings highlight how thoughtful combinations of renewable sources can offer reliable, sustainable, and practical energy solutions for agriculture in remote areas.

Sensor array offers significant potential for rapid, high‐throughput antibiotic detection. However, cross‐reactivity‐based sensor arrays often lack accuracy, despite comprehensive data analysis; while traditional high‐affinity‐based sensors based on antibodies/aptamers frequently suffer from complicated design and poor robustness. Here, a filterable paper‐based fluorescent metal–organic frameworks (MOFs) sensor array is developed for one‐to‐one recognition and quantification of multiple antibiotics. Three representative MOFs are designed to exceptional affinity and specificity for the target antibiotic. A filtration‐assisted detection enhances sensitivity, achieving parts‐per‐billion (ppb)‐level detection in mixed solutions. The proposed approach integrates recognition and signal generation, streamlined 10‐min process. The robustness of the MOFs also enables direct detection in raw samples containing organic solvents, which is not achievable by conventional methods. Notably, the sensor array can be easily incorporated into a smartphone‐based portable device, coupled with a user‐friendly image analysis applet for one‐step extraction and quantitative detection in chicken samples. Leveraging MOFs’ versatility, this method can be extended to simultaneously detect a broad range of antibiotics, offering the potential for universal, high‐throughput accurate detection of various chemical targets.