ASHS 2025 Conference

The number of empty houses in South Korea is expected to surge due to rapid population decline. The problem is being overcame through remodeling and leasing in other countries. However, those businesses are not productive and sustainable. Considering climate change and the decline in the agricultural population, we would suggest a project to use empty houses for agricultural purposes. Smartfarm technology allows us to grow crops anywhere, thus farming in the apartments is possible. In particular, since most apartments in South Korea are complexed, large-scale cultivation is possible. We expect economic effect (sales revenue, local food market, job creation, real estate value, distribution process), social effect (connection between cities and rural areas, food security, local communities) and environmental effect (carbon reduction, sustainability, urban green space) from this project. Taking advantage of the characteristics, we believe that it is necessary to proactively conduct a study on a smart value chain system that connects cultivation, storage, retail, and restaurants.

Strawberry harvesting is a labor-intensive task that requires careful, selective picking to avoid damaging delicate fruit. To address labor shortages and enhance harvesting efficiency, we propose an AI-enhanced soft robotic system capable of autonomous and selective strawberry harvesting. Specifically, the system incorporates advanced computer vision algorithms, leveraging the Grounding DINO model, to detect and localize ripe strawberries with high accuracy. A compliant soft robotic gripper, guided by real-time perception, then gently harvests only the target fruits, minimizing potential damage to adjacent berries and plants. Experimental results demonstrate that the system achieves a ripe strawberry detection accuracy of 88% and a harvesting success rate of 66.67%. This integrated approach offers a scalable, precise, and labor-efficient solution for modern strawberry production.

Seed germination may be affected by various abiotic and biotic stressors, resulting in significantly reduced crop yields and resource use efficiency, thus posing challenges to food production for the growing global population. Therefore, several studies have focused on employing diverse mechanical and chemical treatments to improve seed germination. Non-thermal plasma (NTP) is an emerging technology for enhancing seed germination and plant growth. This study evaluated the effects of NTP on the germination properties of borage (Borago officinalis L.) seeds. Plasma was generated using a custom-made surface-barrier-discharge (SBD) device, operating at a frequency of 5 kHz and voltage of 1.3 kVpp. Seeds were exposed to plasma for 0.5, 1, and 2 minutes, whereas untreated seeds served as the control. Each treatment was replicated three times, with each replication divided into two subunits, containing 25 seeds per experimental unit. Germination was monitored under controlled conditions (20 °C temperature, 16 hours photoperiod, 200 µmol/m2/s PAR radiation provided by fluorescent lights and 74% relative humidity) in a PGR-15 Conviron plant growth chamber for 10 days. Germination was assessed daily, and key germination parameters were analyzed, including final germination percentage (GP), mean germination time (MGT), mean germination rate (MGR), coefficient of variation of germination time (CVt), germination index (GI), and time to 50% germination (t50). Plasma treatment for 0.5 and 1 minute significantly (p < 0.05) increased germination percentage (GP) (52 ± 5.37% and 50.67 ± 2.46%, respectively) compared to the control (34.67 ± 3.96%). MGT decreased in all plasma-treated groups, with the shortest MGT (3.94 ± 0.13 days) observed for 0.5 minutes compared to 4.91 ± 0.29 days for the control, suggesting faster germination. Plasma treatment significantly enhanced MGR, with shorter exposure time leading to accelerated germination. All plasma-treated groups reached 50% germination faster than the control, suggesting improved seed vigor. The GI of plasma-treated seeds was consistently higher than the control, indicating greater germination uniformity. These findings highlight the potential of non-thermal plasma treatment to enhance key factors for improved yield and crop resource use efficiency.

Agrivoltaic systems, which integrate photovoltaic (PV) panels with agricultural production, offer potential benefits for sustainable land use but remain underexplored in many regions. A needs assessment survey conducted among stakeholders on the Navajo Nation identified both an interest in agrivoltaics and a lack of understanding regarding its implementation and impacts. In response, a pilot study was established to evaluate the effects of PV panel shading on microclimatic conditions and radish (Raphanus sativus) productivity in a controlled small-scale agrivoltaic system. This study aimed to generate preliminary data to inform future agrivoltaic research and applications. The experiment utilized open-bottomed grow boxes filled with a 2:1 mixture of commercially available peat-perlite growing medium and organic mushroom compost, placed over field soil. Radish seeds were sown under four different PV panel treatments and a full-sunlight control, with environmental parameters—including air temperature and light intensity—monitored throughout a 46-day growth period. At harvest, crop yield and soil characteristics, including nutrient content, electrical conductivity, pH, and heavy metal concentrations, were analyzed. Morphological parameters such as total root biomass and the proportion of marketable roots (≥16 mm in diameter) were also assessed. Results indicated that PV panel shading reduced radish productivity, with shaded treatments exhibiting lower root biomass and yield compared to the full-sunlight control. These findings suggest that shading effects from PV panels may negatively impact radish growth under fall seasonal conditions. This study provides critical baseline data for optimizing agrivoltaic system designs based on crop selection and environmental conditions, contributing to broader research on sustainable agricultural practices in arid and semi-arid regions.

Major U.S. apple producing regions include Washington, Michigan, and New York for an aggregate of 10 billion pounds of apples/year. While most apple orchards in the Western region feature irrigation systems, large portions of acreage in the Midwest and Eastern regions rely on rainfall and are sensitive to lengthy dry periods during the growing season. These dry periods have been shown to cause fruit drop and drastically reduce fruit size. Apple rootstocks represent the interface between soil and scion and are a critical component of water relations in orchards. Understanding rootstock response to drought is a key component of securing apple production in vulnerable regions. In this study, we utilized a large format aeroponics system (LFAS) with integrated sensors for light, root moisture, and caliper to conduct an exploratory experiment on the utility of the LFAS to study rootstock behavior during drought in real time. The LFAS contained 12 trees of 6 rootstock genotypes grafted with ‘Honeycrisp’. Each tree was suspended into the LFAS with a collar and the roots sprayed with a pH-balanced nutrient solution at intervals of 30” on/1’30” off. Trees were grown from June-November in a heated greenhouse (24oC) with supplemental lighting. We selected three rootstocks (G.890, G.935, G.969) to monitor before, during, and after drought using LiCor-600 porometer readings. Prior to treatment, we selected four newly expanded leaves at the top of the tree and two mature leaves in the mid-section, labelling them N1-N4 (newer leaves) and O1-O2 (older leaves). Baseline porometer readings were collected at 4:30 AM and then at 12:00 PM under full sun and supplemental light. Subsequently, solution misting was shut down from 12:00 PM to 4:00 PM on November 11th, 2022. In aeroponics, solution film provided by misting and reserve moisture in the roots are all that is available for evapotranspiration. As a result, the drought treatment was almost instant. Roots appeared dry and both apical sections of the trees and mature leaves wilted. After 2 weeks, leaf damage was recorded. Post-drought, N1-N4 and O1-O2 leaves were measured again with the porometer, as well as two “Drought Damaged” leaves (DD1-DD2). Statistical analysis indicated significant Drought*Genotype effects (p < .0001). Dendrometer readings showed the differential response to drought of each rootstock in real time. This experiment demonstrated that aeroponics is a viable method for assessing drought tolerance of rootstock-scion combinations, and showed drought tolerance of the rootstocks was G.935 > G.890 > G.969.

Modern agricultural systems, notably hydroponics, utilize digital technologies and precision farming techniques for automated, sensor-driven cultivation. These systems apply real-time environmental monitoring to enhance plant growth parameters and operational efficiency. Nevertheless, sensor inaccuracies can undermine data integrity due to malfunctions or environmental influences. Thus, this is required to obtain comprehensive environmental analysis and precise data management protocols to maintain system integrity. This study assessed the sensors' reliability by evaluating algal coverage metrics in hydroponic tomato cultivation. A total of one hundred seventeen sensors designed to measure pH, temperature, humidity, and electrical conductivity (EC) were deployed within a greenhouse environment. Following a duration of three months, only 39 sensors were classified into two separate categories: 22 demonstrating considerable algal coverage (≥90%) and 17 exhibiting minimal coverage (~10%). Sensors characterized by high algal coverage recorded substantially increased substrate humidity (85.6% vs. 41.9%) and EC values (774 µS/cm vs. 331 µS/cm) in contrast to their counterparts with lowered algae coverage. However, tomato yields did not significantly change between the two categories, indicating that the plants accommodate the changing environmental conditions. These results suggest that algal coverage may serve as an indirect metric for evaluating localized environmental parameters—particularly humidity and EC levels—and may also hold potential value in assessing the reliability of sensor data.

UAV Remote Sensing for Western Mayhaw Flower Intensity Assessment Presenting Author: Austin Fruge’ Co-Authors: Dr. Cengiz Koparan, Dr. Donald M Johnson, Dr. Amanda McWhirt Abstract. Western Mayhaw (Crataegus opaca) is an emerging economically important fruit in the genus Crataegus due to increased consumption, expanded marketing, and improved cultivars. Further research is needed to expand technology-driven management strategies and investigate its potential as an economical crop for rural and urban landowners in the Southeastern United States. The current methodology for estimating flowering intensity assessment in Western mayhaws is performed with visual observation in the field. However, this methodology is time-consuming, labor-intensive, and subjective. Given the need for a precise methodology for flowering intensity monitoring in Western mayhaws, we developed an open-source image-based phenotyping workflow from Unmanned Aerial Vehicle (UAV) captured images. A subset of Western mayhaw selections were evaluated for blooming intensity during the spring of 2025 in a private orchard near El Dorado, Arkansas. RGB images of Western mayhaw trees during the early flowering stage were collected using a DJI Mavic 3 Enterprise UAV mounted with an RGB digital camera. Each image was processed using an open-source image processing software to estimate the number of flowers. To evaluate the accuracy of this method, the flowering intensity was evaluated through visual flower counting and a visual scale, and compared to image-based flower estimation. Flowering intensity estimated with image segmentation showed a strong correlation with visual flower counting (r= 0.858, p < 0.001), indicating that an increase in visual flower count can be explained with segmented pixel count for any random image. Flower estimation with image segmentation is accurate and provides a standard method, however, it could be time-consuming due to the large image dataset. A semi-automated or fully automated image processing workflow could be developed to increase the efficiency of image processing.

Forecasting yield is a timely opportunity to make anticipated harvesting decisions on the grown crop and understand field variability. Such information is a remarkable contribution to the precision agriculture context. However, developing such an approach is challenging for perennial crops such as pecan. These crops present slight canopy changes, which often do not reflect the upcoming yield. Consequently, waiting for the harvest date is the only approach to obtain yield data. Conversely, the advent of image-based data and artificial intelligence techniques has proven their applicability in addressing this issue. Therefore, our objective was to analyze whether UAV multispectral images and AI-based data analysis are suitable for developing forecasting models for yield in pecan trees. Hence, we began collecting multispectral images approximately five months before the harvesting date. Each data collection date had an interval of fifteen days, totaling ten multispectral image sets. Subsequently, we processed the images to generate ten orthomosaics (one for each date). The orthomosaics were used to calculate numerous vegetation indices, texture data, and the canopy area to be used as inputs for the forecasting models. At the harvest date, we measured the yield of 78 individual plants across two pecan fields. Before developing the forecasting models, we performed a correlation analysis to better understand the relationship between the image data and yield. Afterward, we developed the forecasting models using machine learning algorithms, namely, multiple linear regression, decision tree, support vector machine, and random forest. The dataset was split into 70% (n = 55) for training and 30% (n = 23) for testing. The training dataset was used to train the forecasting models, while the testing dataset was used to assess the models’ effectiveness regarding precision (coefficient of determination, R²) and accuracy (mean absolute error, MAE; and root mean squared error, RMSE). All the models produced interesting results and could be implemented to forecast yield in pecan trees. However, random forest outperformed the others (high precision and accuracy) and, therefore, was the remaining model for this study. Forecasting yield in pecan trees presented increased effectiveness, improving the models’ performance early on and establishing higher accuracies closer to the harvesting date. We also performed a feature importance analysis, where predominantly the texture data contributed better to the models’ performance. Certainly, our findings are timely and support pecan growers and stakeholders in making better decisions for harvesting with anticipated and accurate yield data without waiting for the harvesting date.

Non-destructive estimation models often require cultivar-specific calibrations due to spectral differences arising from genetic variability. Integrating diverse cultivars into a single model can reduce costs and simplify data collection. However, in persimmons, the abundant and variable proanthocyanidins in the fruit overlap with spectral regions used for sugar estimation, rendering accurate prediction with a single model challenging. In this study, we attempted sugar estimation in diverse persimmon cultivars using near-infrared (NIR) spectroscopy and hyperspectral imaging. A total of 989 spectral measurements were acquired from 34 persimmon cultivars. Regression models employing various pre-processing and modeling techniques achieved a maximum R² of 0.786, indicating the feasibility of modeling sugar content across diverse cultivars with a unified approach. Furthermore, by designing a cover for the NIR sensor and combining it with SNV pre-processing, we demonstrated that stable spectra for sugar estimation can be obtained under outdoor conditions. With further improvements in accuracy, this approach is expected to facilitate rapid fruit quality evaluation and contribute to optimized production.

Combining green roofs with solar modules can protect plants and produce energy in cities. Growing crops in this system is called rooftop agrivoltaics (RAV) and can complement current urban agriculture efforts. We evaluated a group of five leafy green crops (arugula, kale, lettuce, spinach, and Swiss chard) under different solar modules over two years at two locations. Data measurements were taken for fresh and dry weight (FW, DW) stomatal conductance (SC), plant size at harvest (PSH), and microclimate data. Treatments included a polycrystalline opaque silicon module, a cadmium telluride (CdTe) frameless opaque module, a 40% semi-transparent CdTe module, and a full sun control. Four of the five leafy greens produced higher FW and DW under the 40% semi-transparent modules compared to other treatments and the full sun control, except spinach. Most species also produced larger PSH under the PV module treatments compared to the full sun control. Leafy greens under the module treatments resulted in lower SC, however, lettuce and Swiss chard grown under the semi-transparent module treatment produced higher SC compared to all other treatments. This research shows that incorporating photovoltaics on rooftop gardens influences the yield and stomatal conductance of select leafy green crops. While FW and DW mostly decreased under the deep shade treatments (opaque module, frameless module, and bifacial module) SC decreased, possibly due to less solar radiation on the leafy greens, reducing water use. Understanding the growth characteristics and growing environment of high value crops like leafy greens will increase understanding of what food crops are suitable for RAV systems.

Real-time monitoring of crop health is pivotal for advancing precision agriculture, enabling timely interventions to mitigate abiotic stress impacts. This study presents a novel and non-destructive approach for detecting salt stress in hydroponically grown arugula (Eruca sativa L.) using low-cost MQ gas sensors. Arugula seedlings, 11 days post-germination, were transplanted to a deep-water culture (DWC) hydroponic system in the greenhouse facility of The Pennsylvania State University. Salt stress was induced 9 days after planting by supplementing a modified Hoagland nutrient solution with sodium chloride (NaCl) at three concentrations: 0 mM (control), 40 mM, and 80 mM. Electrical conductivity (EC), pH and temperature parameters were regularly monitored during the cultivation period. Three MQ gas sensors—MQ2, MQ135, and MQ137—were integrated into a dome-shaped enclosure positioned over individual net pots, each containing four plants. To achieve a comprehensive volatile organic compound (VOC) profile, sensor units were strategically positioned on multiple plants within each treatment group. A total of 144 plants per treatment were cultivated, and two sets of sensor units recorded VOC emissions for 8 consecutive days. Salt stress significantly influenced plant growth, with fresh weight (FW) and leaf area decreasing as salinity increased. The 80 mM treatment exhibited the lowest FW and leaf area (61.69 ± 2.7 g, p-value = 0.015; 1434.25 ± 58 cm², p-value = 0.003), followed by the 40 mM treatment. All three MQ sensor responses revealed distinct VOC emission patterns correlating with salt stress levels. These sensor outputs were leveraged to train three machine learning models—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—to classify stress-induced VOC signatures. Among these, the cubic KNN model demonstrated superior predictive performance, achieving 98.73% accuracy, 98.74% precision, 98.73% recall, and an F1 score of 0.98 for the test dataset. These findings underscore the potential of cost-effective MQ gas sensors for real-time, non-invasive stress detection in crops, offering a promising tool for precision agriculture and early stress diagnosis. The integration of VOC sensing with machine learning models provides a scalable solution for enhancing crop management strategies in controlled environments.

Land use is a major concern for our society, which impacts governmental regulations, industry and agriculture, and individual property owners. Agrivoltaics (APV) includes the combination of agriculture and photovoltaics energy production on a single plot of land. APV has been proposed as a way to integrate agriculture into large-scale photovoltaic arrays or integrate energy production into established agricultural operations. The objectives of this project were to establish replicated APV research trials with fixed vertical panel photovoltaic arrays and investigate the feasibility of growing various warm- and cool-season crops between them. Warm-season crops included tomato, watermelon, bush bean, and zucchini. Cool-season crops included fall-planted lettuce and beets as well as spring-planted spinach and lettuce. Vertical panels were constructed with four replications that examined systems effects of APV compared to the open-field. A split-plot randomized complete block design was utilized, whereby main plots included system and sub-plots were the crops. For each crop, yield, marketability, quality, and economic data were collected. Crop quality parameters tested included: visual quality, color, firmness, titratable acidity, and total soluble solids. Additionally, PAR sensors were located within crop rows to characterize light availability at the replicated site as well as at another solar array. Sensors were placed approximately 2.5’ above the ground surface to generally mimic mature plant canopy height, transversely across the expected light-treatment area. From the first year of study, similarities among the two systems (APV and open-field) were more numerous than significant differences. Only one statistically-significant treatment effect was found on crop yield, among one of two lettuce varieties grown in the fall (P

Blueberries are extremely susceptible to drought due to their shallow root systems and limited water regulation capabilities. Climate change exacerbates drought stress in major blueberry production regions, which affect key physiological traits, such as leaf water content (LWC), photosynthesis (A), stomatal conductance (gs), electron transport rate (ETR), photosystem II efficiency (φPSII) and transpiration rate (E). Current phenotyping methods for measuring these physiological traits are time-consuming and labor-intensive as well as limited by the need for specialized equipment. To address this, a high-throughput phenotyping (HTPP) platform integrated with hyperspectral camera and a novel graph convolutional network (GCN)-based model, Plant-GCN, was developed to predict physiological traits of blueberry plants under drought stress. Spectral reflectance obtained from the hyperspectral images were transformed into a graph representation, with each plant represented as a node, spectral reflectance as node features, and edges defined by spectral similarities. The Plant-GCN model utilizes graph convolutional layers that aggregate information from neighboring nodes, effectively capturing complex interactions in the spectral signature and enhancing the prediction of physiological traits. Plant-GCN achieved a coefficient of determination (R²) of 0.89 for LWC, 0.94 for A, 0.89 for gs, 0.92 for ETR, 0.93 for φPSII and 0.89 for E on the test dataset. The performance of the proposed Plant-GCN model was compared with multilayer perceptron (MLP), partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF), and it consistently outperformed all these models as well as data published in other reports. The high-throughput phenotyping system enabled efficient large-scale data collection, while the Plant-GCN model captured long-range spectral relationships significantly improved the prediction of physiological traits. The high predictability of the models could facilitate the screening of blue-berry cultivars for the specified traits allowing the selection and breeding of new drought tolerant cultivars in the future.

Bacterial leaf scorch is a harmful disease for pecan production, which can cause premature canopy defoliation, reduced kernel weight, and significant yield losses. The disease can cause a 10-13% reduction in shell nut weight and a 14-19% decrease in kernel weight, adversely affecting the quality and quantity of pecan yields. Early detection and precise management are important for minimizing economic losses and sustainable disease management. Current methods, such as manual scouting and conventional imaging, are inadequate for pecan orchards due to the trees' height and their inability to capture temporal changes or disease transmission patterns. Spatio-temporal modeling is a useful technique that enables tracking disease spread across time and location, identifying hotspots and transmission patterns to prioritize areas needing urgent intervention. This study aimed to develop a spatio-temporal model to visualize and evaluate the progression of bacterial leaf scorch disease in pecan orchards. High-resolution multispectral images of pecan trees were collected using a drone- mounted multispectral camera. A 3D point cloud was generated using Pix4D to create a reference NIR point cloud, and other vegetation indices point clouds were then aligned to provide sufficient data for model training. Ten vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Red Edge Index (NDRE), Modified Red Edge Simple Ratio (MRESR), Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Atmospherically Resistant Vegetation Index (ARVI), Chlorophyll Index - Red Edge (CI_RE), and Simple Ratio Index (SR), were used to evaluate disease sensitivity. Individual tree canopies were segmented using the 3D DBSCAN algorithm for each index. A point transformer deep learning model was trained with 3D vegetation indices of each tree with the ground truth to develop the prediction ability of the model for classifying individual each tree disease severity level. The early results indicate that the model can classify disease severity in the 3D point clouds, capturing the disease stress in the individual tree. Additionally, integrating a temporal embedding layer into the trained model enables the transformer block to track changes in vegetation indices and quantify disease progression over time. The findings of this study facilitate the monitoring of disease progression and support site-specific management decisions, thereby enhancing the sustainability of pecan production.
Keywords: Artificial intelligence, Spatio-temporal modeling, Multispectral imaging, Spectral analysis, Drone-based Scouting.

Precise and accurate quantification of blackberry flowers are essential for yield forecasting, phenotypic assessment, and enhancing management techniques in precision agriculture. Threshold segmentation of images for blackberry feature quantification could be challenging due to shadows and background variability, while manual flower estimation is subjective and time-consuming. The objective of this research was to apply artificial intelligence and computer vision to identify and quantify blackberry flowers from Unmanned Aerial Vehicle (UAV) remote sensing. A computer vision algorithm You Only Look Once (YOLO) was trained with 1142 image datasets of blackberry flowers to develop an image processing workflow to quantify UAV captured images. A performance analysis was conducted with YOLO variants (YOLOv8s–YOLO12s) for quantifying blackberry flowers. YOLOv10s achieved the best performance with a mAP@0.5 of 58%, precision of 60%, and recall of 58%. Input resolution had a notable impact, performing better at 1024×1024 pixels (mAP@0.5 = 55%) than at 640×640 (mAP@0.5 = 30%). Increasing the training dataset from 250 to 1,142 images progressively improved detection accuracy, highlighting the value of data volume for model generalization. Additionally, flower counts predicted by YOLOv10s showed a strong correlation with flower-to-vegetation ratio (FVR; r = 0.71, p < .001), supporting FVR as a practical proxy for estimating floral density in the field. A contribution to computer assisted agriculture integration in the blackberry industry has been made by investigating the performance of computer vision algorithms on blackberry flower detection. Challenges such as small and uneven sized flowers, overlapping occlusion, and plot-wise analysis must be further investigated. Keywords. UAV, automation, blackberry, phenotyping, artificial intelligence.

A forum for discussion of potential collaborations with regards to technology in horticulture – i.e. biotechnology, UAVs, cameras, sensors, artificial intelligence, etc.

The Technology Applications in Horticulture Interest Group will meet at this session.

Luke Concollato will provide an introduction to new, intuitive platform for digital phenotyping and integration of environmental data. Luke Concollato will introduce the program and give instructions on how this platform can be used for specified organization of data and ultimately, breakthroughs in plant science.

This is a pre-recorded Zoom webinar that will be shown at the Hort Theater.

Vidalia sweet onion (Allium cepa spp.), holds significant economic and cultural value in the Vidalia region, known for its sweet flavor and low pungency. The success of marketable bulb production is closely linked to the early crop stages, particularly seedling production and field-quality transplanting. Due to the limited availability of mechanical transplanters, transplanting is predominantly performed manually, resulting in variability in planting quality. A major issue is the presence of seedlings lying on the ground, which significantly reduces survival rates and leads to undersized bulbs. Assessing the number of such seedlings manually is labor-intensive and inefficient. Therefore, this study aimed to develop a rapid and accurate method to quantify the number of seedlings lying on the ground across the field using a unmanned aerial vehicle (UAV) imagery and the YOLOv8 deep learning model. On January 6, 2025, aerial RGB images were captured using a UAV over an experimental onion field located at the UGA Vidalia Onion and Vegetable Research Center in Lyons, GA. The seedlings were transplanted on December 15, 2024, under three treatment conditions: 100% properly transplanted plants, 100% lying on the ground, and 25% lying on the ground. The objective of this study was to detect and quantify the percentage of plants lying on the ground within each plot using computer vision techniques. The images were manually annotated and used to train a YOLOv8 object detection model. The dataset was split into 70% for training, 15% for validation, and 15% for testing. Model performance was evaluated using standard YOLOv8 accuracy metrics, including precision, recall, and F1-score. The YOLOv8 model demonstrated strong performance in detecting onion seedlings lying on the ground. It achieved moderate to high precision and recall, as well as a high F1-score on the test dataset. When applied to the annotated aerial images, the model accurately quantified the percentage of fallen seedlings within each treatment. In the 100% properly transplanted treatment, a small number of seedlings were falsely identified as lying on the ground. These results confirm the model's ability to reliably distinguish between transplant quality levels using UAV imagery. This approach offers a scalable solution for monitoring transplant quality and identifying problematic planting zones.

This study developed an AI-powered image preprocessing tool to analyze daylily flower traits, providing a user-friendly and feature-rich solution for accurate image segmentation- a process that separates the flower from the background in photographs. The tool's design combines a pre-trained TRACER convolutional neural network (CNN) with an EfficientNet backbone to ensure robust and precise segmentation. It features a user-friendly graphical interface built with PyQt that facilitates seamless image uploads, real-time visualization of segmentation results, and customization options. Furthermore, a built-in manual refinement pencil function allows for precise corrections to automated masks, ensuring accuracy when needed. It supports multiple output formats, including masks and annotated images, enabling easy integration into subsequent analyses. A dataset comprising 1,450 daylily color images was employed for training and evaluating the tool. A comparative analysis of four deep learning models (TRACER, BASNet, U2 Net, and DeepLabV3), using metrics such as Accuracy, Precision, Recall, Dice Score, Jaccard Index, and XOR Error, highlighted the superior performance of the TRACER model in terms of accuracy and reliability. The findings show that the tool successfully separates flowers from intricate backgrounds, effectively addressing issues such as petal overlap, various phenotypes, and environmental variations. This automation tool has the potential to replace the labor-intensive and error-prone manual segmentation process, which has posed a significant limitation in AI applications for daylily research, including accurate trait prediction and classification.

Broadcast herbicide applications across fallow fields with sparse weed populations can lead to substantial chemical waste, increased costs, and environmental impacts. To address this issue, we implemented and evaluated a low-cost, vision-based targeted herbicide spraying system leveraging the Open Weed Locator (OWL) methodology. OWL utilizes simple RGB cameras and image-processing algorithms to detect green vegetation (weeds) against soil backgrounds in real-time, triggering targeted herbicide application only where weeds are detected. The primary objective of this study is to assess the practical effectiveness of this technology in field conditions, focusing on use-cases relevant to fallow weed management scenarios. Field trials are planned to quantify the system’s performance by evaluating weed detection accuracy through metrics such as recall and precision. Additionally, we will measure the percentage of herbicide chemical savings compared to conventional broadcast methods. Preliminary analyses suggest that this targeted approach significantly reduces chemical input. Detailed evaluations from forthcoming field trials will provide essential data to guide future improvements and support broader adoption of affordable, precision weed management tools.

Accurate plant phenotyping is essential for crop improvement but remains a major challenge, especially when tracking both above- and below-ground traits over time. Traditionally, these traits are measured manually and destructively, limiting data quantity and quality. At the Texas A

Hemp (Cannabis sativa L.) is a remarkably versatile crop with extensive applications in food, fiber, and medicine, offering environmentally sustainable and highly productive raw materials across various industries. Historically cultivated primarily for fiber, modern dual-purpose hemp varieties now present enhanced economic opportunities by enabling the simultaneous harvesting of seeds for grain and stems for fiber. Nitrogen (N) fertilization significantly influences key growth parameters, including plant height, stem diameter, biomass accumulation, and seed yield. However, conventional nitrogen assessment methods are invasive and labor-intensive. To address these challenges, multispectral drone imaging has emerged as a non-destructive alternative, leveraging correlations between nitrogen levels and leaf chlorophyll content to enable rapid monitoring of critical physiological indicators such as assimilation rates, stomatal conductance, and transpiration rates. In 2024, an experiment was conducted at North Carolina A0.70), chlorophyll content and BNDVI (R² = 0.55), stomatal conductance and NDVI (R² = 0.82), and transpiration rate and MCARI (R² = 0.56). In contrast, negative correlations were observed with SIPI2 (R² = 0.69), TGI (R² = 0.39), and additional SIPI2 indices (R² = 0.54 and R² = 0.39, respectively). This study highlights the potential of integrating drone-based remote sensing and machine learning for efficient, non-destructive monitoring in hemp cultivation. By advancing precision agriculture practices, these technologies offer promising pathways to enhance productivity, optimize nitrogen management, and promote sustainability in hemp cultivation.

Nitrogen (N) management is a major challenge in organic vegetable production, aiming to supply sufficient N for optimal yield and quality while minimizing N losses. These challenges are exacerbated by uncertain mineralization patterns of different organic fertilizer products and season-specific impacts on soil N availability. Monitoring plant tissue N dynamics throughout the production season can provide meaningful information regarding fertility management, but sampling plant tissue can be labor-intensive and costly, and lab analysis may be time-consuming. Considering that N from organic nutrient sources is generally not immediately available for crop uptake, the need for reliable tools to rapidly monitor plant N status is paramount in improving N use efficiency, particularly in organic systems. In this study, a hyperspectral imaging approach was explored. Celery samples were collected from two research trials repeated in two production seasons at midseason (approximately 70 days after transplanting; DAT) and final harvests (approximately 110 DAT). One experiment focused on integrated nutrient management practices, comparing celery grown following a sunn hemp cover crop to a weedy fallow (whole plots), and evaluating impacts of composts (subplots) including an unamended control, yard waste compost, vermicompost, and a mixed compost treatment. The other experiment compared ratios of preplant N:in-season N application from 0-100% preplant N (whole plots) under two organic preplant fertilizers (subplots) contrasting in composition and anticipated N mineralization. At each harvest, six plants from each experimental unit were weighed and allocated into representative portions for crop quality analyses on fresh and dry bases. A spectroradiometer with a leaf clip reflectance probe was used to collect leaf reflectance spectra (350-2500 nm) from approximately eight leaves from each experimental unit. Spectra were normalized to a mean of zero and a standard deviation of one across wavelengths. To ensure balanced representation in training and validation, the data were stratified by harvest timing, trial, and year, and randomly split following 80% calibration and 20% external validation distribution. The final model used 19 latent components, explained 76.4% of the variation within external validation data, and had a root mean square error of 0.36. The model can be categorized as providing “approximate quantitative predictions,” and total N content from dried aboveground biomass in the original dataset spans from 1.0 - 4.3 g N/100 g DW. Building robust models using hyperspectral data to predict crop N status under diverse production practices and environmental conditions is an area deserving of continued research in organic vegetable production.

Disaster events such as hurricanes strongly affect pecan crop production due to fallen trees, which generate a significant impact on production areas. An appropriate method to monitor these effects is by counting the fallen trees. This monitoring is commonly performed by a field team. Although accurate, this approach is time-consuming, costly, and non-scalable. Consequently, an image-based approach emerges as a timely opportunity. For instance, aerial images captured by unmanned aerial vehicles (UAVs) present high spatial resolution and coverage, contributing to tree identification. However, relying solely on images remains a laborious and time-consuming task. Consequently, the development of object detection models to automatically identify and count fallen trees emerges as an unprecedented and dynamic method. Therefore, we aimed to integrate UAV image technology and the YOLOv12 detection model for detecting fallen trees in pecan fields. On September 27th, 2024, the hurricane Helene crossed the State of Georgia, USA. On October 7th, 2024, we captured RGB images using a multirotor UAV. We captured high-resolution (~3 cm/px) in eight pecan fields. The images were processed to generate one orthomosaic for each field. Consequently, we counted the fallen trees using assisted image processing for ground truth data. Together, the pecan fields presented more than 200 fallen trees. Subsequently, we trained the YOLOv12 model to automatically identify the fallen trees. Initially, we manually labeled all the fallen trees as “Fallen”. Afterward, we split our dataset into training (70%), validation (15%), and testing (15%). To enhance model performance and generalizability, we implemented data augmentation for the training dataset. As a result, the model achieved identification metrics of fallen trees higher than 70%. Certainly, this approach presents a great opportunity to measure the effects caused by hurricanes, giving farmers the ability to make faster and better decisions regarding their fields. Ultimately, these findings support precision agriculture practices and introduce pecan crops into this context, further strengthening the introduction of technologies into the world of specialty crops.