Proceedings
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| Filter results6 paper(s) found. |
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1. 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 |
2. 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 |
3. Combining YOLOv9 and Fuzzy Inference System to Improve the Precision of Weed Recognition Systems in Soybean Crops Using UAV ImageryWeeds are a problem in crops because they compete with crops for nutrients, sunlight, and water, hindering their full development. To control these plants, herbicides are usually applied throughout the field. Therefore, to optimize the application process, many researchers have been working on automatic weed recognition systems based on artificial intelligence techniques for field imaging, enabling the localized application of herbicides. To this end, the YOLO (You Only Look Once) object detection... M. Tosin, I. Scheeren, C. Markus |
4. Assessing the Potential of Google Satellite Embeddings for Mapping Sugarcane in Brazilian Production AreasThe increasing availability of remote sensing (RS) data with higher spatial resolution, combined with advances in artificial intelligence (AI), has been an essential tool in driving the development of the agricultural sector, such as precision agriculture (PA). Among the most relevant information derived from these approaches, crop mapping plays an essential role in crop monitoring, management strategies and yield forecasting. However, the large amount of data required for training classification... G. Rodigheri, J. Da Silva, L. Alvim Santos Romani, J. Garcia Arnal Barbedo |
5. Development of Predictive Models for Determining Organic Carbon and Clay Content in Soils under Irrigated Fruit Production in the Brazilian Semi-Arid RegionThe agricultural sector plays a pivotal role in both greenhouse gas emissions and climate change mitigation through soil carbon sequestration. Total organic carbon (TOC) and soil texture - particularly clay content - are key indicators of this dynamic, as they influence organic matter stabilization, water retention, and soil structural quality. In semi-arid regions, where edaphoclimatic conditions and water scarcity constrain agricultural production, the integrated assessment of these attributes... A.L. Santos, A.A. Linhares, P.C. Barbosa , W.C. Lopes, M.D. Reis, K.D. Rocha, D.G. De Oliveira, M.S. Rodrigues, D.D. Costa |
6. IBRA MEGALAB - From Soil Maps to Precision Irrigation: Transforming Data into Decisions... R. Battist |