Proceedings
Authors
| Filter results2 paper(s) found. |
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1. Field Evaluation of Automated Estrus Detection Systems - Meeting Farmers' ExpectationAutomated systems for oestrus detection are commonly marketed as a suitable, or in some cases, a higher performing alternative to visual observation. Farmers, particularly those with larger herds relying on less experienced staff, view the perceived benefits of automated systems as both economic and physical, with expectations of improved oestrus detection efficiency with lower labour input. There is little evidence-based information available on the field performance of these systems to... B.T. Dela rue, C. Kamphuis, J.G. Jago, C.R. Burke |
2. Using Deep Learning - Convolutional Naural Networks (CNNS) for Real-Time Fruit Detection in the TreeImage/video processing for fruit detection in the tree using hard-coded feature extraction algorithms have shown high accuracy on fruit detection during recent years. While accurate, these approaches even with high-end hardware are still computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks architecture based on single-stage detectors. Using deep-learning techniques eliminates the need for hard-code specific features for specific... K. Bresilla, L. Manfrini, A. Boini, G. Perulli, B. Morandi, L.C. Grappadelli |