Proceedings
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| Filter results9 paper(s) found. |
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1. Consequences of Spatial Variability in the Field on the Uniformity of Seed Quality in Barley Seed CropsSpatial variation is known to affect cereal growth and yield but consequences for seed quality are less well-known. Intra-field spatial variation occurs in soil and environmental variables and these are expected to affect the crop. The objective of this paper was to identify the spatial variation in barley seed quality and to investigate its association with environmental factors and the spatial scale over which this correlation occurs. Two uniformly-managed, commercial fields of winter... S. Hama rash, A.J. Murdoch |
2. Predicting the Spatial Distribution of Aflatoxin Hotspots in Peanut Fields Using DSSAT CSM-CROPGRO-PEANUT-AFLATOXINAflatoxin contamination in peanuts (Arachis hypogaea L.) is a persistent concern due to its detrimental effects on both profitability and public health. Several plant stress-inducing factors, including high soil temperatures and low soil moisture, have been associated with aflatoxin contamination levels. Understanding the correlation between stress-inducing factors and contamination levels is essential for implementing effective management strategies. This study uses the DSSAT CSM-CROPGRO-Peanut-Aflatoxin... S. Maktabi, G. Vellidis, G. Hoogenboom, K. Boote, C. Pilcon, J. Fountain, M. Sysskind, S. Kukal |
3. Deep Reinforcement Learning Based Robotic Arm Control for Autonomous HarvestingInverse Kinematics (IK) is a traditional method used for robotic arm manipulation, relying heavily on precise calibration and huge computational demands for arms with higher Degrees of Freedom (DoF). In contrast, Deep Reinforcement Learning (DRL) is an innovative approach to manipulation that exhibits greater tolerance for calibration inaccuracies. It trains using noise added to joint angles, allowing it to learn how to compute accurate trajectories even with inaccuracies in the joint angles.... C. Huang |
4. A Physics-informed Neural Network Approach for Simulating Laminar FlowEfficient and accurate modeling in agricultural fields is critical for advancing precision agriculture. These simulations, often involving the prediction of airflow, temperature, and humidity distributions, directly support decisions related to crop management, greenhouse climate control, and irrigation strategies. Computational Fluid Dynamics (CFD) has been a primary tool for decades, offering reliable and high-fidelity simulations through established numerical methods such as the finite-difference... C. Huang |
5. Disease Symptom Recognition and Severity Assessment for Phalaenopsis OrchidsTraditional disease assessment relies on manual visual inspection, which is subjective and often leads to inconsistent results due to variations in human judgment. To address these challenges, this study proposes an automated approach for disease classification and severity grading in Phalaenopsis orchids using the YOLOv8-seg deep learning model. The system integrates instance segmentation with Lab color space analysis, which was found to outperform HSV in distinguishing healthy and diseased leaf... C. Huang |
6. Quantitative Assessment of Discharge Depth Effects on Lithium-Based Batteries: LTO, LFP, and NCMThis study explores the impact of depth of discharge (DoD) on the performance degradation of three lithium-based battery chemistries: lithium titanate (LTO), lithium iron phosphate (LFP), and nickel cobalt manganese oxide (NCM). The objective is to establish a standardized methodology for evaluating battery health under partial cycling and to quantify the degradation behavior across three DoD ranges: 0–33%, 34–66%, and 67–100%. LFP and NCM cells were cycled at 1C under room temperature,... C. Huang |
7. Modeling the Effects of Greenhouse Environmental Factors on Soft Rot Incidence in PhalaenopsisPhalaenopsis spp. is one of Taiwan’s most important ornamental crops for export. However, during greenhouse cultivation, Phalaenopsis is frequently threatened by bacterial soft rot (Erwinia spp.), particularly under high-temperature and high-humidity conditions that accelerate pathogen spread and cause severe losses in seedlings. This study was conducted in a Phalaenopsis greenhouse located in Houbi District, Tainan, Taiwan. The greenhouse contained 21 planting beds, which were... C. Huang |
8. Cfd Evaluation of Uvc Air-cleaning Integration in Greenhouse Hvac SystemsGreenhouse crops in Taiwan are highly vulnerable to airborne pathogens due to the humid climate and poor ventilation. This study evaluated the integration of UVC air- cleaning devices with the greenhouse HVAC system to reduce pathogen concentrations. A SolidWorks model of the NTU smart greenhouse was constructed, and CFD simulations were conducted to compare three configurations in which four UVC units were placed at the upper, middle, and lower regions of the wet pad. Results showed that the... C. Huang |
9. A Low-cost Multi-view Image to 3d Reconstruction for Plant PhenotypingCurrent 3D plant phenotyping approaches often rely on LiDAR or multi-camera systems, which are costly, require complex calibration, and lack scalability. This study introduces a simple and cost-effective pipeline for 3D plant reconstruction using Hunyuan3D-2.5, a multi-view generative model. Plant samples were photographed directly using a mobile phone, and raw images were processed with a custom Python background-removal pipeline that enhanced plant contours and removed environmental noise. The... C. Huang |