Proceedings
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| Filter results8 paper(s) found. |
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1. Pasture Yield Measurement With The C-DAX Pasture MeterA system of pasture yield measurement was developed for New Zealand’s pasture based, rotationally grazed farming systems. Pasture yield measurement is complex because the pasture biomass has to be measured in-situ, pre and post grazing so that pasture consumption and utilisation can be calculated. The “Pasture Meter” was initially developed by Massey University and subsequently commercialised by... I.J. Yule |
2. Monitoring Dairy Cow Activity With GPS-tracking And Supporting TechnologiesNutrient loss from dairy farms is an issue of serious concern to most dairy farmers around the world. On grazed systems such as those practiced in New Zealand animal excreta has been identified as a major source of nutrient loss, which for nitrogen (N) relates to cattle urine in particular. A study was commissioned to examine nutrient transfer around dairy farms associated with the cows with a view to developing improved precision nutrient application... I. Draganova, I.J. Yule, K. Betteridge, M.J. Hedley, K.J. Stafford |
3. Spatial Livestock Research In Australia And New Zealand: Towards A Cooperative Research ModelA number of researchers in Australia and New Zealand are working in the area of animal tracking as an important technological step to gaining a deeper understanding of animal behavior in various farmed and natural environments. The ultimate goals of the research vary from simply trying to understand how animals can be farmed more effectively to how animals could be controlled without fences. There are a number of parallels with the development of conventional... I.J. Yule |
4. Enhancing Spatial Resolution of Maize Grain Yield DataGrain yield data is frequently used for precision agriculture management purposes and as a parameter for evaluating agronomy experiments, but unexpected challenges sometimes interfere with harvest plans or cause total losses. The spatial detail of modern grain yield monitoring data is also limited by combine header width, which could be nearly 14 m in some crops. Remote sensing data, such as multispectral imagery collected via satellite and unmanned aerial systems (UAS), could be used to... J. Siegfried, R. Khosla, D. Mandal, W. Yilma |
5. Yolo Strawberry Maturity Classification and Harvest Priority with 3d CameraAccurate harvesting timing is essential to improve crop quality and productivity, and recent advances in agricultural automation have led to the emergence of fruit maturity classification and harvest optimization algorithms for agricultural robots as major technical challenges. This study proposes a pipeline for strawberry object detection, maturity classification, distance estimation, and harvest priority. We train a YOLOv8 detector on an open RGB dataset, and estimate the camera-fruit distance... M. Yang |
6. Unsupervised Anomaly Detection of Tipburn in Leafy Vegetables Using Denoising AutoencoderTipburn, a common physiological disorder in leafy vegetables, presents as marginal necrosis but its fuzzy boundaries make annotation costly and inconsistent. We present a label-free pipeline that combines CIE Lab–based preprocessing with a chroma-only denoising autoencoder (DAE) trained solely on healthy samples for real-time, pixel-level anomaly mapping. Lettuce images were acquired under controlled lighting, segmented in CIE Lab space, and reduced to the a channel and a/b chromatic ratio... M. Yang |
7. Development of 3D Phenotypic Analysis Technology for Precision Monitoring of StrawberriesStrawberries exhibit high overall production volume but low productivity per unit area, primarily due to diseases that occur during cultivation. These yield losses can be mitigated through precision monitoring technologies based on phenotypic analysis. To enhance monitoring accuracy, 3D phenotyping techniques are essential. This study aims to automate such 3D phenotyping by constructing a 3D segmentation model capable of identifying plant organs. Strawberry plants were imaged from all angles using... M. Yang |
8. Development of Light-Normalized Crop Monitoring Framework Using RGB-D Imaging and Spatial Light RegressionTo achieve high-quality, high-yield crop production, non-destructive precision monitoring technologies combined with image-based artificial intelligence are being studied to establish finely controlled cultivation environments tailored to crop growth stages. However, variations in lighting-one of the most critical cultivation factors-can cause significant fluctuations in crop image data, limiting the accuracy of phenotype extraction. This study aims to develop a light-normalized crop monitoring... M. Yang |