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| Filter results8 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. Evaluation of an Ai-Driven High-Precision Spraying System for Targeted Weed Control in OnionsWeeds are a persistent challenge in the Vidalia onion production region, where limited herbicide options make placement and selectivity critical. AI-based high-precision spraying systems may reduce herbicide use and crop injury by targeting only weeds and non-crop areas. This study compared the Ecorobotix ARA high-precision sprayer operating in an all-but-the-crop mode with a conventional broadcast application for weed control, crop phytotoxicity, and onion performance. A field trial was conducted... R. Dos Santos, L. Oliveira, L.D. Sales, M. Barbosa, C.T. Tyson |
5. A Statistical Approach to Defining Coffee Management Zones: Integrating Apparent Soil Electrical Conductivity, Altimetry and Satelitte Indices for Moisture MonitoringCharacterizing the spatial and temporal behavior of soil and plant attributes represents the elementary step toward adoption precision agriculture. The expanding availability of multi-temporal remote sensing imagery with enhanced spatial resolution has rendered the delineation of management zones (MZ) an increasingly feasible strategy, especially when the intention is to carry out spatially differentiated interventions considering the vegetative vigor throughout the crop cycle or the plant yield.... E.A. Speranza, E.J. Ferreira, L.H. Bassoi, L.M. Rabello, C.M. Vaz, A. Torre-neto |
6. Integration of LiDAR-Derived TWI and UAV Multispectral Data for Waterlogging Susceptibility Mapping in Precision AgricultureTopography directly controls water redistribution across the landscape, shaping the spatial variability of soil moisture in agricultural areas. The Topographic Wetness Index (TWI), derived from digital elevation models, is widely used to estimate the potential for water accumulation; however, its field-scale validation supported by high-resolution multispectral drone imagery remains limited. In agricultural systems, recurrent waterlogging can reduce productivity by impairing germination, promoting... |
7. Comparative Evaluation of Ground Point Classifiers in LiDAR Point Clouds for DEM Generation in Pasture AreasThe classification of ground points in LiDAR point clouds is an essential step for generating reliable Digital Terrain Models (DTMs), particularly in livestock production systems based on pastures. Despite methodological advances in forested and urban environments, studies specifically addressing ground classification in pasture areas remain limited, where the proximity between the forage canopy and the ground surface makes altimetric distinction between classes challenging. The heterogeneous... |
8. YOLOv10x-based deep learning for automated detection and counting of seeds per soybean podAccurate quantification of the number of seeds per soybean pod is a fundamental step for reliable yield estimation. However, this measurement still relies on manual procedures, which are subject to observational variability and limited scalability. In the context of digital agriculture, deep learning–based techniques have shown promise for automating the detection and counting of reproductive structures. Nevertheless, there is still limited application of models specifically aimed... |