Developing an automatic warning system for anomalous chicken dispersion and movement using deep learning and machine learning.

Chicken is a major source of dietary protein worldwide. The dispersion and movement of chickens constitute vital indicators of their health and status. This is especially evident in Taiwanese native chickens (TNCs), a local variety which is high in physical activity when healthy. Conventionally, the dispersion and movement of chicken flocks are observed in patrols. However, manual patrolling is laborious and time-consuming. Moreover, frequent patrols increase the risk of carrying pathogens into chicken farms. To address these issues, this study proposes an approach to develop an automatic warning system for anomalous dispersion and movement of chicken flocks in commercial chicken farms. Embendded systems were developed to acquire videos of chickens from overhead view in a chicken house, in which approximately 20,000 TNCs were raised for a period of 10 wk. Each video was 5-min in length. The videos were transmitted to a remote cloud server and were converted into images. A You Only Look Once-version 7 tiny (YOLOv7-tiny) object detection model was trained to detect chickens in the images. The dispersion of the chicken flocks in a 5-min long video was calculated using nearest neighbor index (NNI). The movement of the chicken flocks in a 5-min long video was quantified using simple online and real-time tracking algorithm (SORT). The normal ranges (i.e., 95% confidence intervals) of chicken dispersion and movement were established using an autoregressive integrated moving average (ARIMA) model and a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model, respectively. The system allows farmers to check up on the chicken farm only when the dispersion or movement values were not in the normal ranges. Thus, labor time can be saved and the risk of carrying pathogens into chicken farms can be reduced. The trained YOLOv7-tiny model achieved an average precision of 98.2% in chicken detection. SORT achieved a multiple object tracking accuracy of 95.3%. The ARIMA and SARIMAX achieved a mean absolute percentage error 3.71% and 13.39%, respectively, in forecasting dispersion and movement. The proposed approach can serve as a solution for automatic monitoring of anomalous chicken dispersion and movement in chicken farming, alerting farmers of potential health risks and environmental hazards in chicken farms.

Towards intelligent and integrated pest management through an AIoT-based monitoring system.

BACKGROUND: Main bottleneck in facilitating integrated pest management (IPM) is the unavailability of reliable and immediate crop damage data. Without sufficient insect pest and plant disease information, farm managers are unable to make proper decisions to prevent crop damage. This work aims to present how an integrated system was able to drive farm managers towards sustainable and data-driven IPM. RESULTS: A system called Intelligent and Integrated Pest and Disease Management (I2 PDM) system was developed. Edge computing devices were developed to automatically detect and recognize major greenhouse insect pests such as thrips (Frankliniella intonsa, Thrips hawaiiensis, and Thrips tabaci), and whiteflies (Bemisia argentifolii and Trialeurodes vaporariorum), to name a few, and measure environmental conditions including temperature, humidity, and light intensity, and send data to a remote server. The system has been installed in greenhouses producing tomatoes and orchids for gathering long-term spatiotemporal insect pest count and environmental data, for as long as 1368 days. The findings demonstrated that the proposed system supported the farm managers in performing IPM-related tasks. Significant yearly reductions in insect pest count as high as 50.7% were observed on the farms. CONCLUSION: It was concluded that novel and efficient strategies can be achieved by using an intelligent IPM system, opening IPM to potential benefits that cannot be easily realized with a traditional IPM program. This is the first work that reports the development of an intelligent strategic model for IPM based on actual automatically collected long-term data. The work presented herein can help in encouraging farm managers, researchers, experts, and industries to work together in implementing sustainable and data-driven IPM. © 2022 Society of Chemical Industry.

[Construction, Expression, Purification and Antiviral Activity of Fused Interferon-BLA (IFN-BLA).].

A fusion protein, Interferon-BLA (IFN-BLA), was constructed with IFN-beta-1b and IFN-alpha-2b separating by a linker -GGGS-. The laboratory-scale expression conditions in E.coli BL21 CodonPlus (DE3)-RIL had been optimized and IFN-BLA was expressed higher than 35% of total protein in the cells mainly as inclusion body. The inclusion body of IFN-BLA was denatured and refolded by dialysis and purified by ion-exchange chromatography. The overall yield of IFN-BLA was about 45 mg/L with purity higher than 90%. Antiviral activity assay suggested that this newly fused protein may have synergetic or additive antiviral activities.