Brief Summary of Minutes of Annual Meeting

• In Arkansas, the research and extension effort of Dr. Zhu and his team has focused on Objective 3. The major activities of this project have enabled the team to make significant progress in improving the anaerobic digestion of poultry litter with wheat straw by incorporating ferric oxide nanoparticles into the digestion process. Metallic nanoparticles, such as ferric oxide nanoparticles (FNP), have been utilized to promote methane fermentation. However, the appropriate use of ferric oxide nanoparticles in anaerobic co-digestion (Co-AD) of agricultural wastes, considering substrate characteristics as important factors, is rarely understood. The research conducted in the past year used response surface methodology and artificial neural network (ANN) to model methane yield (MY, NmL CH₄/g VS added) from batch Co-AD of poultry litter (PL) and wheat straw (WS) with FNP supplementation. A statistical central composite design was applied to the input factors of ferric oxide nanoparticle dosage (mg/L), carbon-to-nitrogen ratio (C/N), and total solids level (TS, %), with a significant second-order quadratic model generated (R² =0.9887). ANN developed a trained multilayer perceptron network with an even higher R² (0.9947). These analyses showed that all factors had a significant effect on methane yield (the significance was in the order of C/N ratio > FNP dosage > TS), with significant interactions between C/N ratio and FNP, and between C/N ratio and TS. Numerical optimization achieved a maximum methane yield of 318.4 mL CH4/g VS added under the conditions of C/N 34.65, TS 5.28%, and FNP 19.39 mg/L. The trained artificial neural network coupled with the genetic algorithm generated a similar maximum methane yield prediction, 318.3 mL CH4/g VS added, under the optimal conditions of C/N 35, TS 4.24%, and FNP 17.42 mg/L. The results can give guidance to real operations and provide support for process simulation and optimization in other scenarios.

• In Georgia (GA), Chai and his team have secured additional funding from USDA-NIFA, Georgia Research Alliance, and Egg Industry Center to work on multiple precision poultry production projects. Two select projects and corresponding achievements are shown below:
• Activity index detection: Chickens’ behaviors and activities are important information for managing animal health and welfare in commercial poultry houses. In this study, convolutional neural networks (CNNs) models were developed to monitor the chicken activity index. A dataset consisting of 1,500 top-view images was utilized to construct tracking models, with 900 images allocated for training, 300 for validation, and 300 for testing. Six different CNN models were developed, based on YOLOv5, YOLOv8, ByteTrack, DeepSORT, and StrongSORT. The final results demonstrated that the combination of YOLOv8 and DeepSORT exhibited the highest performance, achieving a Multi-Object Tracking Accuracy (MOTA) of 94%. Further application of the optimal model could facilitate the detection of abnormal behaviors such as smothering and piling, and enabled the quantification of flock activity into three levels (low, medium, and high) to evaluate footpad health states in the flock. This research underscores the application of deep learning in monitoring the poultry activity index for assessing animal health and welfare.
• Footpad dermatitis monitoring: Footpad dermatitis (FPD) is a common poultry condition that can negatively influence chickens’ production, welfare, and health. However, no automated tool for monitoring FPD in live chickens is currently available. The objective of this study was to develop and optimize deep learning models to monitor hens’ FPD scores (i.e., 0-2 scale with higher scores indicating poorer footpad conditions). A total of 700 Hy-Line W-36 hens were raised in four cage-free housing systems integrated with Electrostatic Particle Ionization and various bedding materials. A GoPro camera with an upward lens was placed inside a transparent box. Individual laying hens were placed on the top surface of the box to acquire RGB images. In addition, a thermal camera was used to record RGB and thermal images of footpads, and the images were manually scored to assess their footpad conditions. Preprocessing techniques (e.g., filtration, separation, and augmentation) were deployed to enhance dataset quality and size. Moreover, YOLOv8 models (YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x) and YOLOv7 models (YOLOv7 and YOLOv7x) were comparatively evaluated for predicting FPD scores. The results show that the YOLOv8l outperformed other models, with higher recall (96.6%), mAP@0.50 (97.0%), and F1-score (95.0%). Additionally, the YOLOv8l-FPD model exhibited a high mAP@0.50 for score 0 (98.0%), score 1 (95.0%), and score 2 (97.9%) and F1-score (95.0%) for all FPD scores. Notably, using thermal images could result in faster convergence of model training and slightly better FPD score prediction performance than RGB images. The proposed technique can be useful for non-invasive automatic FPD scoring and further improve automation levels and animal welfare in the egg industry.

• In Idaho, Dr. Chen and his team worked with colleagues from Washington State University and Oregon State University on developing proposal ideas that build symbiotic, climate-smart dairy and potato systems for sustainable agriculture. They also engaged in Dairy West and the Idaho Dairymen’s Association’s effort that involved scientists and industry professionals from the Pacific Northwest to collectively identify research gaps and priorities that help achieve the dairy industry’s sustainability goals. Additionally, the team contributed to the Idaho Sustainable Agriculture Initiative for Dairy (ISAID) project, a USDA Sustainable Agriculture Systems initiative, which involved principal investigators from Washington and North Carolina.

• In Minnesota, Dr. Cortus and her team have focused on technology appraisal in the past reporting year. Nutrient and carbon partitioning and performance for swine manure management technologies. Through a National Pork Board-funded project, S1074 members are synthesizing nutrient transformations and partitioning in liquid swine manure undergoing anaerobic digestion, aeration, acidification, and solid-liquid separation. The work included a literature review in 2023/2024. While these manure treatment technologies are more mature than others, there is a lack of mass balance qualification of results, particularly when treatment comparisons are performed. However, by considering mass partitioning, we are better able to stack technologies and quantify performance through other analysis methods, like life cycle analyses. This work involves Minnesota, North Carolina, and Iowa S1074 members, in conjunction with animal nutritionists, geneticists, and data scientists.

• In North Carolina, Dr. Wang-Li's group continued to analyze the air sampling data collected at NCDA Piedmont Research Station Poultry Unit and a commercial egg farm in Ohio State University, in collaboration with Dr. Lingying Zhao at The Ohio State University. This research project titled “Fate, Transport, and Transformation of Ammonia Emissions from Animal Feed Operations and Their Impacts on Air-Soil Health” aims at quantifying NH₃ and particulate NH₄⁺ dry depositions as impacted by NH₃ emissions from poultry production units and their associated impact on soil health. It will fill the knowledge gap in NH₃ deposition flux and velocities in this rural environment to help address environmental impacts and the sustainability of the poultry industry. Dr. Sharara led the coordination and delivery of a national webinar, through the Livestock and Poultry Environmental Learning Community (LPELC). The webinar, The Role of Agriculture in Atmospheric Nitrogen Deposition: Sources, Impacts, and Management, brought expertise from UW-Madison (Dr. David Gay), U.S. EPA (Jesse Bash), USDA ARS (Dr. Greg Zwick), and Dr. Sharara to provide technical specialists and consultants with a comprehensive of ammonia. Through this well-attended webinar (over 120 audience members from the U.S. and Canada), evaluations revealed a gain in knowledge of ammonia drivers, impacts, as well as interventions to reduce its release.

• In Ohio, Dr. Zhao and her team have focused on assessing existing sustainability assessment tools in the past reporting year. Through two rounds of USDA SAS proposal development efforts, Dr. Zhao has collaborated with many peer researchers and developed a conceptual structure for a sustainability assessment tool for egg production, in reference to the sustainability framework published by the U.S. Roundtable for Sustainable Poultry & Eggs (US-RSPE) in 2022. She has started to build a case scenario about a one-million-bird layer operation in Ohio.

• In South Dakota, Dr. Yang and his team collaborated with Dr. Erin Cortus at the University of Minnesota on the 2024 Minnkota Annual Meeting in April 2024. Minnkota is an association of university extension specialists, governmental agencies, animal producers, equipment suppliers, and barn builders dedicated to addressing sustainability issues in animal production facilities. This meeting was held on the South Dakota State University campus in conjunction with the 2024 ASABE North Central Regional Meeting. The team also collaborated with Dr. Yuanhui Zhang at the University of Illinois at Urbana-Champaign on a swine air quality project funded by the Foundation for Food and Agriculture Research. Through this project, they have reached out to 12 commercial swine farms to explore potential opportunities for field monitoring and data sharing. This project aims to enhance our understanding of particulate matter emitted from swine production facilities, including its characteristics, sources, and potential safety and health risks, to help address sustainability challenges facing the swine industry. Additionally, the team collaborated with the North Dakota Pork Council and the North Dakota Livestock Alliance to develop a grant proposal for an odor footprint tool for the state. This tool will establish odor setback distances between animal production facilities and their neighbors. This information is crucial for planning and zoning new or expanding facilities, thereby supporting the continued growth of North Dakota's animal industry. Within the state of South Dakota, Dr. Yang served on an Ag Cybersecurity Curriculum development team within the SDSU Extension program. A partial goal of this work is to facilitate the implementation of cybersecure precision livestock farming technologies among animal producers. These technologies often result in smaller environmental footprints. Out-of-state collaborators of this effort included the extension agents from the University of Idaho.

1. Xiao, Y., Tian, Y., Xu, W., & Zhu, J. (2024). Photodegradation of microplastics through nanomaterials: Insights into photocatalysts modification and detailed mechanisms. Materials, 17(11), 2755. https://doi.org/10.3390/ma17112755
2. Bist, R. B., Yang, X., Subedi, S., Bist, K., Paneru, B., Li, G., & Chai, L. (2024). An automatic method for scoring poultry footpad dermatitis with deep learning and thermal imaging. Computers and Electronics in Agriculture, 226, 109481. https://doi.org/10.1016/j.compag.2024.109481
3. Yang, X., Dai, H., Wu, Z., Bist, R. B., Subedi, S., Sun, J., Lu, G., Li, C., Liu, T., & Chai, L. (2024). An innovative segment anything models for precision poultry monitoring. Computers and Electronics in Agriculture, 222, 109045. https://doi.org/10.1016/j.compag.2024.109045
4. Yang, X., Bist, R. B., Liu, T., Applegate, T., Ritz, C., Kim, W., & Chai, L. (2024). Computer vision-based cybernetics systems for promoting modern poultry farming: A critical review. Computers and Electronics in Agriculture, 225, 109339. https://doi.org/10.1016/j.compag.2024.109339
5. Yang, X., Bist, R. B., Subedi, S., Wu, Z., Liu, T., Paneru, B., & Chai, L. (2024). A machine vision system for monitoring wild birds on poultry farms to prevent avian influenza. AgriEngineering, 6(4), 3704–3718. https://doi.org/10.3390/agriengineering6040219
6. Saeidifar, M., Li, G., Chai, L., Bist, R. B., Rasheed, K. M., Lu, J., Banakar, A., Liu, T., & Yang, X. (2024). Zero-shot image segmentation for monitoring thermal conditions of individual cage-free laying hens. Computers and Electronics in Agriculture, 226, 109436. https://doi.org/10.1016/j.compag.2024.109436
7. Yang, X., Bist, R., Paneru, B., & Chai, L. (2024). Monitoring activity index and behaviors of cage-free hens with advanced deep learning technologies. Poultry Science, 103(11), 104193. https://doi.org/10.1016/j.psj.2024.104193
8. Yang, X., Bist, R. B., Paneru, B., & Chai, L. (2024). Deep learning methods for tracking the locomotion of individual chickens. Animals, 14(6), 911. https://doi.org/10.3390/ani14060911
9. Paneru, B., Bist, R. B., Yang, X., & Chai, L. (2024). Tracking dustbathing behavior of cage-free hens with machine vision technologies. Poultry Science. Advance online publication. https://doi.org/10.1016/j.psj.2024.104289
10. Bist, R. B., Poudel, K., Yang, X., Paneru, B., Mani, S., Wang, D., & Chai, L. (2024). Sustainable poultry farming practices: A critical review of current strategies and future prospects. Poultry Science. Advance online publication. https://doi.org/10.1016/j.psj.2024.104295
11. Bist, R. B., Yang, X., Subedi, S., Paneru, B., & Chai, L. (2024). Enhancing dust control for cage-free hens with electrostatic particle charging systems at varying installation heights and operation durations. AgriEngineering, 6(2), 1747–1759. https://doi.org/10.3390/agriengineering6020100
12. Bist, R. B., Yang, X., & Subedi, S., & Chai, L. (2024). Automatic detection of bumblefoot in cage-free hens using computer vision technologies. Poultry Science. Advance online publication. https://doi.org/10.1016/j.psj.2024.103780
13. Bist, R. B., Yang, X., Subedi, S., Ritz, C. W., Kim, W. K., & Chai, L. (2024). Electrostatic particle ionization for suppressing air pollutants in cage-free layer facilities. Poultry Science. Advance online publication. https://doi.org/10.1016/j.psj.2024.103494
14. Paneru, B., Bist, R., Yang, X., & Chai, L. (2024). Tracking perching behavior of cage-free laying hens with deep learning technologies. Poultry Science, 103(12), 104281. https://doi.org/10.1016/j.psj.2024.104281
15. Bist, R. B., Yang, X., Subedi, S., Paneru, B., & Chai, L. (2024). An integrated engineering method for improving air quality of cage-free hen housing. AgriEngineering, 6(3), 2795–2810. https://doi.org/10.3390/agriengineering6030178
16. Saeidifar, M., Li, G., Lu, J., Chai, L., Bist, R., & Yang, X. (2024). Automatic segmentation of birds using a combination of object detection and foundation image segmentation models. International Journal of Advances in Electronics and Computer Science, 11(7), 2394–2835.
17. Das, A. K.  and L. Chen. 2024. A Review on Electrochemical Advanced Oxidation Treatment of Dairy Wastewater. Environments 2024, 11(6), 124; https://doi.org/10.3390/environments11060124
18. Sapkota, S., A. Reza, and L. Chen. 2024. Optimization of Ammonia Nitrogen Removal and Recovery from Raw Liquid Dairy Manure Using Vacuum Thermal Stripping and Acid Absorption Process: A Modeling Approach Using Response Surface Methodology. Nitrogen 2024, 5(2), 409-425: https://doi.org/10.3390/nitrogen5020026
19. Reza, A., L. Chen. and X. Mao. 2024. Response surface methodology for process optimization in livestock wastewater treatment: A review. Heliyon (DOI: https://doi.org/10.1016/j.heliyon.2024.e30326)
20. Das, A.K., A. Reza, and L. Chen. 2024. Optimization of pollutants removal from anaerobically digested dairy wastewater by electro-oxidation process: a response surface methodology modeling and validation. Journal of Applied Electrochemistry (DOI: 10.1007/s10800-024-02113-z)
21. Xiao, Y., Tian, Y., Xiong, H., Shi, A., & Zhu, J. (2024). Compact solar-powered plasma water generator: Enhanced on-site aged seed germination with the corona dielectric barrier discharger. Frontiers of Agricultural Science and Engineering. https://doi.org/10.15302/J-FASE-2024573
22. Zhan, Y., & Zhu, J. (2024). Response surface methodology and artificial neural network-genetic algorithm for modeling and optimization of bioenergy production from biochar-improved anaerobic digestion. Applied Energy, 355, 122336. https://doi.org/10.1016/j.apenergy.2023.122336
23. Yin, Y., Qi, X., Gao, L., Lu, X., Yang, X., Xiao, K., Liu, Y., Qiu, Y., Huang, X., & Liang, P. (2024). Quantifying methane influx from sewer into wastewater treatment processes. Environmental Science & Technology, 58, 9582–9590.
24. Rubel, R. I., Wei, L., Alanazi, S., Aldekhail, A., Cidreira, A. M., Yang, X., Wasti, S., Bhagia, S., & Zhao, X. (2024). Biochar-compost-based controlled-release nitrogen fertilizer intended for an active microbial community. Frontiers of Agricultural Science and Engineering, 11(2), 2376366.
25. Uguz, S., Anderson, G., Yang, X., Simsek, E., Osabutey, A., & Min, K. (2024). Microalgae cultivation using ammonia and carbon dioxide concentrations typical of pig barns. Environmental Technology, 45, 5899–5911.
26. Rubel, R. I., Wei, L., Wu, Y., Brozel, V., Gupta, S., Alanazi, S., Ameer, S., Sobhan, A., Das, B., Osabutey, A., & Yang, X. (2024). Greenhouse evaluation of biochar-based controlled-release nitrogen fertilizer in corn production. Agricultural Research, 13(1), 113–123.
27. Haleem, N., Kumar, P., Uguz, S., Jamal, Y., McMaine, J., & Yang, X. (2023). Viability of artificial rain for air pollution control: Insights from natural rains and roadside sprinkling. Atmosphere, 14(12), 1714.
28. He, Y., Gong, A., Osabutey, A., Gao, T., Haleem, N., Yang, X., & Liang, P. (2023). Emerging electro-driven technologies for phosphorus enrichment and recovery from wastewater: A review. Water Research, 246, 120699

    Conference Proceedings

     

    1. Das, A. K., A. Reza, and L. Chen. 2024. Pollutants removal from anaerobically digested dairy wastewater by electro-oxidation process: A RSM optimization and modeling, ASABE AIM 2024, Anaheim, CA. July 28-31, 2024.
    2. Das, A. K. and L. Chen. 2024. Ammonia removal from dairy waste stream using combined chemical coagulation and photoelectron-Fenton process: A GRA-Taguchi, RSM, and ANN based optimization and modeling, ASABE AIM 2024, Anaheim, CA. July 28-31, 2024.
    3. Mohammad Nazrul, Islam, L. Chen, and B. Brian He. 2024. Mitigating phosphorus runoff risk and enhancing bioavailability in dairy manure via hydrothermal carbonization with CaO addition, ASABE AIM 2024, Anaheim, CA. July 28-31, 2024.
    4. Chen, L. and A. Reza. 2024. Ammonia removal and recovery from anaerobically digested liquid dairy manure using vacuum thermal stripping-acid absorption process. 64th Idaho Academy of Science and Engineering Symposium-17th Intermountain Conference on the Environment Joint Symposium-Sustainability & The Earth’s Climate, Pocatello, ID. April 5-6, 2024.
    5. Chen, L. and Reza, A. 2023. Ammonia removal and recovery from anaerobically digested liquid dairy manure using vacuum thermal stripping-acid absorption process: a GRA-Taguchi, RSM, and RSM-ANN based optimization and modeling. 2023 Northwest Bioenergy Summit, Kennewick, WA. October 10-12, 2023.
    6. Xiao, Y. and Zhu, J. 2024. Enhanced seed germination with solar-powered plasma water generator. ASABE 117th Annual International Meeting. Paper#: 2400189. Anaheim, CA. July 28-31, 2024.
    7. Cherotich, S., Wang-Li, L., Anderson, K., Classen, J., & Shi, W. (2024, July 28–31). Ammonia concentrations, deposition, and soil properties as impacted by the deposition in the near fields of a poultry production facility (Presentation No. 2400324). Paper presented at the 2024 ASABE Annual International Meeting (AIM), Anaheim, CA.
    8. Li, P., Herkins, M., Knight, R., Zhao, L., Akter, S., & Wang-Li, L. (2024, July 28–31). Comparison of three on-field measurement methods for low-level ammonia concentrations at ambient locations of a poultry layer production facility. Paper presented at the 2024 ASABE Annual International Meeting (AIM), Anaheim, CA.
    9. Zhu, H., E. Ozkan, J. Theodoro, H. Jeon, J. Campos, and L.Y. Zhao. 2024. Modified Design of Open-Circuit, Centrifugal-Fan Driven Wind Tunnel to Produce Uniform Laminar Air Flows. Presentation (No. 2401309) at 2024 ASABE Annual International Meeting, Anaheim, CA, July 28-31, 2024.
    10. Li, P., M. Herkins, R. Knight, L.Y. Zhao, S. Akter, L. Wang-Li, J.Q. Ni, and A. Heber. 2024. Comparison of three on-field measurement methods for low-level ammonia concentrations at ambient locations of a poultry layer production facility. Presentation (No. 2401175) at 2024 ASABE Annual International Meeting, Anaheim, CA, July 28-31, 2024.
    11. Geng, Y., D. Jepsen, L.Y. Zhao, T. Reponen. 2024. Assessing the protection provided by the N95 filtering facepiece respirators in grain dust environments: A case study of Ohio farmers.  Presentation (No. 2400691) at 2024 ASABE Annual International Meeting, Anaheim, CA, July 28-31, 2024.
    12. Herkins M., R. Knight, X. Tong, L.Y. Zhao, T. Yazbeck, J. Missik, G. Bohrer. 2023. Validating an ammonia dispersion model near a commercial poultry facility using AERMOD. Presentation at 2023 ASABE Annual International Meeting, Omaha, Nebraska, July 8-12, 2023. 
    13. Haleem, N., Yuan, J., Uguz, S., Ucok, S., Gu, Z., Yang, X. (2024). DC-assisted flocculation of Scenedesmus dimorphus. In American Society of Agricultural and Biological Engineers (ASABE) 2024 Annual Meeting, Anaheim, CA.
    14. Kumar, P., Tiwari, S., Uguz, S., Yang, X. (2024). Microbial composition of swine barn bioaerosol using next-generation sequencing. In ASABE 2024 Annual Meeting, Anaheim, CA.
    15. Uguz, S., Kumar, P., Tiwari, S., Yang, X. (2024). Assessment of low-cost PM sensors for their applicability in swine barns. In ASABE 2024 Annual Meeting, Anaheim, CA.
    16. Alahe, M.A., Kemeshi, J., Chang, Y., Won, K., Yang, X., Sher, M. (2024). Securing agricultural data with encryption algorithms on embedded GPU-based edge computing devices. In 16th International Conference on Precision Agriculture, Manhattan, KS.
    17. Kumar, P., Tiwari, S., Haleem, N., Uguz, S., Yang, X. (2024). Bioaerosols downwind from animal production facilities: A landscape analysis of existing knowledge. In 2024 ASABE North Central Regional Section Meeting, Brookings, SD.
    18. Haleem, N., Yang, X., Yuan, J. (2024). DC-assisted flocculation of Scenedesmus dimorphus. In 2024 ASABE North Central Regional Section Meeting, Brookings, SD.
    19. Uguz, S., Yang, X., Anderson, G. (2024). Biological treatment of air pollutants from animal feeding operations using photobioreactor systems. In 2024 ASABE North Central Regional Section Meeting, Brookings, SD.
    20. Cheng, R., Liyanage, D., Mahdaviarab, A., Pahlavanyali, K., Zhang, Y., Wang, X., and Liu, Z. Enhancing Animal/Food Waste Management through Composting: A Comparative Analysis of Quality Improvement with Biochar/Additive. 2024. ASABE Annual International Meeting, Anaheim, CA.
    21. Mahdaviarab, A., Cheng, R., Pahlavanyali, K., Wang, X., and Liu, Z. Adoption of Biogas Production from Animal Waste: Case Studies of Texas Farms. 2024. ASABE Annual International Meeting, Anaheim, CA.
    22. Mahdaviarab, A., Cheng, R., Liyanage, D., Kincaid, N., Zhou, R., Li, Y., Wang, X., and Liu, Z. Identifying and Characterizing Animal Wastewater Lagoons via Satellite Remote Sensing. 2024. ASABE Annual International Meeting, Anaheim, CA.
    23. Liyanage, D., Mahdaviarab, A., Zhou, R., Wang, X., and Liu, Z. Virtual Reality Videos for Delivery of Extension Educational Materials on Manure and Mortality Management. 2024. ASABE Annual International Meeting, Anaheim, CA.
    24. Mahdaviarab, A., Pahlavanyali, K., Cheng, R., Wang, X., and Liu, Z. Estimation of Dairy Lagoon Water Quality Using Satellite Images. 2024. Data-Driven Intelligent Agricultural System Symposium, College Station, TX.
    25. Pahlavanyali, K., Cheng, R., Mahdaviarab, A., Galvan, L., Wang, X., and Liu, Z. Utilizing Black Soldier Fly (Hermetia illucens) Larvae for Optimizing Dairy Waste Management. 2024. ASABE Annual International Meeting, Anaheim, CA.
    26. Zhang, Y., Cheng, R., Mahdaviarab, A., Wang, X., and Liu, Z. Accurate and Robust Biochar Yield and Composition Prediction via ResNet-Based Autoencoder. 2024. ASABE Annual International Meeting, Anaheim, CA.
    27. Zhou, R., Cheng, R., Liyanage, D., Mahdaviarab, A., Wang, X., and Liu, Z. Photocatalytic Degradation of Organic Pollutants in Agricultural Wastewater by Novel Two-Dimensional Material. 2024. ASABE Annual International Meeting, Anaheim, CA.