Proceedings
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| Filter results4 paper(s) found. |
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1. soil2data: Concept for a Mobile Field Laboratory for Nutrient AnalysisKnowledge of the small-scale nutrient status of arable land is an important basis for optimizing fertilizer use in crop production. A mobile field laboratory opens up the possibility of carrying out soil sampling and nutrient analysis directly on the field. In addition to the benefits of fast data availability and the avoidance of soil material transport to the laboratory, it provides a future foundation for advanced application options, e.g. a high sampling density, sampling of small sub-fields... V. Tsukor, C. Scholz, W. Nietfeld, T. Heinrich, T. Mosler , F. Lorenz, E. Najdenko, A. Möller, D. Mentrup, A. Ruckelshausen, S. Hinck |
2. Securing Agricultural Data with Encryption Algorithms on Embedded GPU Based Edge Computing DevicesSmart Agriculture (SA) has captured the interest of both the agricultural business and the scientific community in recent years. Overall, SA aims to help the agricultural and food industry to avoid crop failures, loss of revenues as well as help farmers use inputs (such as fertilizers and pesticides) more efficiently by utilizing Internet of Things (IoT) devices and computing systems. However, rapid digitization and reliance on data-driven technologies create new security threats that can defeat... M. Alahe, J.O. Kemeshi, Y. Chang, K. Won, X. Yang, M. Sher |
3. Agronomist-in-the-Loop Semantic 3D Reconstruction of Cotton Boll Morphology from UAV Imagery for Precision AgricultureStandard aerial photogrammetry is failing precision agriculture in one specific area: the detailed morphological assessment of complex, cluttered canopies. While creating a field-level map is trivial, recovering the geometry of a single cotton boll from a drone altitude of 30 meters is often mathematically intractable for standard Structure-from-Motion (SfM) solvers. These traditional pipelines depend on pixel-perfect consistency, which breaks down amidst... P. Sundaravadivel, H. Manjunatha, S. Borah, A. Anand, A. Price, H. Torbert, L. Tamil, T. Stroud |
4. A Multimodal Spectral-Robustness-LLM Pipeline for Non-Destructive Identification of Loropetalum chinense CultivarsProprietary cultivars of ornamental shrub Loropetalum chinense, particularly the visually and spectrally similar ‘Cerise Charm’, ‘Purple Daybreak’, and ‘Red Diamond’, derive their market value from the intensity and stability of anthocyanin pigmentation, a trait that degrades subtly under abiotic stress. Reliance on manual (visual) grading makes the industry vulnerable to these latent, pre-manifestation pigment losses, which are often detected only... P. Sundaravadivel, S. Borah, H. Manjunatha, S.P. Kumpatla, L. Tamil, P. Knight, T. Stroud |