MONITORING THROUGH DEEP LEARNING: POULTRY ANIMAL FEEDING OPERATION DETECTION AND WATER DEMAND ESTIMATIONS IN TENNESSEE

Understanding the scale and distribution of poultry production facilities is essential for managing agricultural water use, environmental risk, and infrastructure planning, particularly in regions with limited regulatory oversight. This thesis presents a novel, remote sensing-based framework to geolocate and quantify poultry barns across four major production counties in Tennessee: Bradley, Bedford, Weakley, and Henry. Using high-resolution USDA NAIP imagery and a DeepLabV3 image segmentation model, over 1,380 poultry barns were detected with high spatial accuracy (87.8% IoU; 92.6% F1/Dice), including 518 in Bradley, 374 in Bedford, 356 in Weakley, and 133 in Henry. A post-processing pipeline removed false positives and filtered detections to match known barn size profiles, enabling the creation of a new spatial database of poultry infrastructure. Clustering analysis using the DBSCAN algorithm revealed 27 distinct barn clusters and 442 individual unclustered detections. High-density clusters, such as Cluster 11 in Bradley County (98 barns) and Cluster 26 in Bedford (76 barns), emerged as key poultry production zones. These clusters were used as the foundation for estimating localized agricultural water use.

Annual water consumption was calculated using experimentally validated data from Tennessee broiler operations and standard parameters from the National Chicken Council. A typical 43,325 ft² broiler barn was estimated to support 240,000 birds annually and use approximately 878,000 gallons of water. Extrapolated across all detected barns, total poultry-related water use exceeded 1 billion gallons annually, comparable to the residential water demand of a city like Cookeville, TN (~36,000 people). Comparison with USGS livestock water withdrawal data and TDEC CAFO permits revealed discrepancies, especially in counties where poultry barns lacked permits or where water use estimates included other livestock types. These findings expose the shortcomings of existing livestock reporting systems and emphasize the importance of detailed, species-specific spatial monitoring. This study delivers a transferable geospatial framework for identifying agricultural operations and forecasting water use. With continued refinement, it offers a powerful tool for environmental oversight, infrastructure planning, and sustainable livestock management across the southeastern U.S.