Proceedings
Authors
| Filter results3 paper(s) found. |
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1. Comparison Between Tractor-based and UAV-based Spectrometer Measurements in Winter WheatIn-season variable rate nitrogen fertilizer application needs a fast and efficient determination of nitrogen status in crops. Common sensor-based monitoring of nitrogen status mainly relies on tractor mounted active or passive sensors. Over the last few years, researchers tested different sensors and indicated the potential of in-season monitoring of nitrogen status by unmanned aerial vehicles (UAVs) in various crops. However, the UAV-platforms and the available sensors are not yet accepted to... M. Gnyp, M. Panitzki, S. Reusch, J. Jasper, A. Bolten, G. Bareth |
2. Supervised Feature Selection and Clustering for Equine Activity RecognitionIn this paper we introduce a novel supervised algorithm for equine activity recognition based on accelerometer data. By combining an approach of calculating a wide variety of time-series features with a supervised feature significance test we can obtain the best suited features using just 5 labeled samples per class and without requiring any expert domain knowledge. By using a simple cluster assignment algorithm with these obtained features, we get a classification algorithm that achieves a mean... T. De waele, D. Peralta, A. Shahid, E. De poorter |
3. Revolutionizing Poultry Health: AI-Powered Real-Time Disease Detection Using YOLO v7 and IQR for Enhanced Farm ProductivityPrompt and accurate detection of poultry diseases is crucial to prevent outbreaks and reduce economic losses. Conventional monitoring systems based on manual inspections are inefficient and prone to error, delaying timely interventions. This study proposes an AI-driven early warning system that integrates YOLO v7 for real-time image detection with Hampel Filters for anomaly recognition. The model specifically targets two critical health indicators: rooster combs and eyes. Over a period of 53 days... A. Santosa |