Advancing Precision Agriculture Through AI and Statistical Modeling: Transforming Crop and Livestock Management

This thesis explores the application of Artificial Intelligence (AI), machine learning (ML), and statistical analysis to enhance agricultural practices, focusing on both livestock man- agement and plant biology. The first part investigates automated weight prediction of beef cattle using computer vision techniques, including YOLOv9 and InternImage with Cas- cade R-CNN for precise image segmentation. Advanced feature extraction methods utilizing ResNet, DenseNet, and ResNeXt are employed to develop ML and deep learning (DL) mod- els, providing a non-invasive alternative to traditional weight measurement techniques. The second part examines the regulation of the auxin response in Arabidopsis plants, focusing on epistatic interactions among auxin receptors. Through experimental assays and com- putational modeling, the study reveals synergistic effects that influence plant growth and development. The third part of the thesis characterizes the transcriptional specificity medi- ated by plant hormones using comprehensive data analysis, uncovering key insights into the gene regulation mechanisms influenced by auxin. Overall, the research integrates AI, ML, DL, and statistical methods to address critical challenges in agriculture and plant science, demonstrating improved predictive accuracy, enhanced understanding of hormonal signaling, and potential advancements in crop productivity and livestock management