Huanglongbing (HLB), also known as citrus greening disease, is a devastating disease of citrus. However, there is no known cure so far. Recently, under Section 24(c) of the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), a special local need label was approved that allows the trunk injection of antimicrobials such as oxytetracycline (OTC) for HLB management in Florida. The objectives of this study were to use UAS-based remote sensing to assess the effectiveness of OTC on the HLB-affected citrus trees in Texas and to differentiate the levels of HLB severity and canopy health. We also leveraged UAS-based features, along with machine learning, for HLB severity classification. The results show that UAS-based vegetation indices (VIs) were not sufficiently able to differentiate the effects of OTC treatments of HLB-affected citrus in Texas. Yet, several UAS-based features were able to determine the severity levels of HLB and canopy parameters. Among several UAS-based features, the red-edge chlorophyll index (CI) was outstanding in distinguishing HLB severity levels and canopy color, while canopy cover (CC) was the best indicator in recognizing the different levels of canopy density. For HLB severity classification, a fusion of VIs and textural features (TFs) showed the highest accuracy for all models. Furthermore, random forest and eXtreme gradient boosting were promising algorithms in classifying the levels of HLB severity. Our results highlight the potential of using UAS-based features in assessing the severity of HLB-affected citrus.

Purpose: Automated plot extraction in agronomic research field trials is essential for high-throughput phenotyping and precision agriculture. Accurate delineation of plot boundaries enables reliable crop type classification, yield estimation, and crop health monitoring. However, traditional plot extraction methods rely heavily on manual digitization, which is time-consuming, labor-intensive, and prone to inconsistencies. This study aims to develop a Segment Anything Model (SAM)-based framework that automates plot extraction while maintaining high accuracy across diverse agricultural field conditions. Methods: The proposed framework consists of mask generation, plot orientation estimation, and plot refinement. SAM is leveraged to generate plot masks, which are subsequently filtered and refined to ensure precise boundary delineation. The method is designed to function without the need for model training or fine-tuning, making it highly adaptable across different datasets. Results: The framework was validated on five datasets, demonstrating robust performance under varying field conditions. The pixel-based evaluation yielded an average F1 score of 89.54%. For polygon-based evaluation, the framework achieved 99.71% precision at IoU=50% and an average precision of 68.51% across IoU thresholds from 50 to 95%, confirming its ability to accurately extract plot boundaries. A Canopeo-based regression analysis further demonstrated that the extracted plots provide more reliable phenotypic estimates compared to manually digitized ground reference data. Conclusions: The proposed framework significantly reduces manual effort while ensuring high precision and scalability for large-scale phenotyping applications. By relying solely on RGB imagery and zero-shot segmentation, it enhances accessibility for real-world agricultural research. Future work will focus on extending the framework to irregular plot structures, diverse crop types, and computational optimizations for large-scale implementation.

ORCiD: [https://orcid.org/0000-0001-6665-6094] Plant phenotyping has been an essential aspect of crop science analytics that is saddled with tasks such as providing critical information about plants' genetics, traits, productivity, and other intricate details to gain insights about their survival under certain conditions and in a specific environment for various analyses. Various methods have been quantifying this information using various models culled from several kinds of datasets. In this study, we extract various phenotypic information about the soybean using UAS-based images captured over the growing fields within the selected experiment field. The DJI M300 unmanned aerial systems were equipped with the Zenmuse P1 and L1 sensors; both used to capture RGB and LiDAR images. In addition, the DJI P4 multispectral UAS was also used to collect multispectral information over these fields at various date intervals. The data captured is being processed using custom-developed algorithms and automated workflows to obtain biomass, vegetation indices, canopy cover, canopy height, and canopy volume. These indices would show variations in the traits of the crop under study as related to the soybean. This phenotypic information would be compared against the field measurements for validation.

In the quest to enhance citrus yield estimation, this study leverages Multi-Temporal Unoccupied Aerial Systems (UAS) datasets to develop a refined tree-level yield estimation model. Conducted within a 2.22-acre orchard in Weslaco, South Texas, the research integrates data from 56 citrus trees, utilizing RGB and Multispectral UAS imagery collected over critical growth months from June to December (excluding July and August) 2022. The methodology encompasses the collection of UAS data processing using some custom-developed algorithms and automated workflows and the novel application of machine learning algorithms-namely, multiple linear regression, gradient boosting regression, and random forest regression. The study innovatively extracts 11 key phenotypic features, combining tree canopy structural and spectral information to estimate yield with increased accuracy. A significant advancement is proposed in the form of an improved individual tree boundary delineation method. This method addresses the inaccuracies of previous solid shape-based approaches, contributing to more precise feature calculation and improved model performance. Our experimental results indicate the single month whose data is particularly predictive, with the Random Forest model demonstrating robustness and consistency across temporal datasets. The multi-temporal approach confirms that comprehensive data integration yields superior estimation models. This ongoing research promises to bridge the yield estimation gap and set a new standard in precision agriculture methodologies.