Proceedings
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| Filter results14 paper(s) found. |
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1. Application Of Hyperspectral Imaging For Rapid And Non-Invasive Quantification Of Quality Of Mulberry FruitThis study investigated the potential of using hyperspectral imaging working in visible and short-wave near infrared region (380-1030 nm) for rapid and non-invasive determination of the total flavonoid in mulberry fruit. Mulberry fruit with its sweet flavor is widely used in jam, pies, tarts, wines, and liquor, and is a delicacy among humans and birds alike. The quality evaluation of mulberry is usually determined by chemical or sensory analysis. However these methods are not capable... L. Huang, H. Jin, Y. He, F. Liu, Y. Zhou |
2. Diagnosis Of Sclerotinia Infected Oilseed Rape (Brassica Napus L) Using Hyperspectral Imaging And ChemomtricsAbstract: Brassica napus L leaf diseases could cause seriously reduction in crop yield and quality. Early diagnosis of Brassica napus L leaf diseases plays a vital role in Brassica napus L growth. To explore an effective methodology for diagnosis of Sclerotinia infected Brassica napus L plants, healthy Brassica napus L leaves and Brassica napus L leaves infected by Sclerotinia were prepared in a controlled circumstance. A visible/short-wave near infrared hyperspectral... N. Chen, F. Liu, L. Jiang, L. Feng, Y. He, Y. Bao |
3. 3-Dimension Reconstruction Of Cactus Using Multispectral ImagesUsing 3D reconstruction result to investigate plant morphology has been a focus of virtual plant. And multispectral imaging has proved to carried biological information in quite a lot work. This paper present a idea to investigate chlorophyll spatial variability of cactus using a bunch of multispectral images. 46 multispectral images are taken at equally distributed angles surrounding the tree and have over 80% overlap. Structure from motion approach has been used... F. Liu, Y. He, Y. Zhang, L. Tan, Y. Zhang, L. Jiang |
4. A Novel Hyperspectral Feature Extraction Algorithm Based On Waveform Resolving For Raisin ClassificationNear infrared hyperspectral imaging technology was adopted in the paper to determine the variety of raisins produced in Xinjiang Uygur Autonomous Region, China. There are 2 varieties of raisins taking part in the research and the wavelengths of the hyperspectral images are from 900nm to 1700nm. A novel waveform resolving method was proposed in the paper to reduce the hyperspectral data and extract features. The waveform resolving method compresses the original hyperspectral data for one pixel... Y. Zhao, X. Xu, Y. Shao, Y. He, Q. Li |
5. 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 |
6. Rape Plant NDVI Spatial Distribution Model Based on 3D ReconstructionPlants’ morphology changes in their growing process. The 3D reconstruction of plant is of great significance for studying the impacts of plant morphology on biomass estimation, illness and insect infestation, genetic expression, etc. At present, the 3D point cloud reconstructed through 3D reconstruction mainly includes the morphology, color and other features of the plant, but cannot reflect the change in spatial 3D distribution of organic matters caused by the nutritional status (e.g. chlorophyll... Y. Chen, Y. He |
7. 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 |
8. 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 |
9. 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 |
10. 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 |
11. 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 |
12. 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 |
13. 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 |
14. 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 |