Proceedings
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| Filter results3 paper(s) found. |
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1. Evaluation of Strip Tillage Systems in Maize Production in HungaryStrip tillage is a form of conservation tillage system. It combines the benefits of conventional tillage systems with the soil-protecting advantages of no-tillage. The tillage zone is typically 0.25 to 0.3 m wide and 0.25 to 0.30 m deep. The soil surface between these strips is left undisturbed and the residue from the previous crop remain on the soil surface. The residue-covered area reaches 60-70%. Keeping residue on the surface helps prevent soil structure and reduce water loss from the soil.... T. Rátonyi, P. Ragán, D. Sulyok, J. Nagy, E. Harsányi, A. Vántus, N. Csatári |
2. Examining the Relationship Between SPAD, LAI and NDVI Values in a Maize Long-Term ExperimentIn Hungary, the preconditions for the use of precision crop production have undergone enormous development over the last five years. RTK coverage is complete in crop production areas. Consultants are increasingly using the vegetation index maps from Landsat and Sentinel satellite data, but measurements with on-site proximal plant sensors are also needed to exclude the influence of the atmosphere. The aim of our studies was to compare the values measured by proximal plant sensors in the... P. Ragán, E. Harsányi, J. Nagy, T. Ágnes, T. Rátonyi, A. Vántus, N. Csatári |
3. Lameness Detection in Side-View Videos of Dairy Cows Based on Pose Estimation and Deep LearningLameness is a critical factor affecting milk production and remains a major concern in dairy farming. Conventional lameness detection relies on visual observation and veterinary judgment, which are subjective and labor-intensive. This study proposed a non-contact lameness detection system integrating pose estimation and machine learning. A YOLOv11-pose model was trained to detect cow keypoints, and features such as back curvature, head swing, and Back Posture Measurement (BPM) were extracted.... C. Chu |