Proceedings
Authors
| Filter results3 paper(s) found. |
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1. Autonomous Edge-AI–Enabled Drone Systems for Real-Time Agricultural Inference and Decision-MakingHigh-throughput, low-latency phenotyping and field surveillance remain critical bottlenecks in precision agriculture and environmental monitoring due to delayed data turnaround, large data volumes, computationally intensive preprocessing, and expertise-heavy analysis workflows. These constraints hinder timely crop improvement, pest and disease management, and informed agronomic decision-making. To address these challenges, we present an integrated, end-to-end autonomous drone system that enables... |
2. Development of a Predictive Machine Learning Model for Pasture Biomass Using Satellite Vegetation Indices and Climate Data in Spanish Dehesa SystemsExtensive livestock systems are fundamental to the ecological, economic, and cultural sustainability of Mediterranean agroecosystems such as the Spanish dehesa. These silvopastoral landscapes support biodiversity, prevent land abandonment, and sustain rural livelihoods, but their productivity is highly dependent on pasture availability. Efficient management therefore requires accurate and timely information on pasture biomass, which is strongly influenced by climatic variability, soil properties,... C. Ferraz, A. Tamayo López, A. Do Vale Dondo |
3. Semi-Automatic Plot Segmentation for Crop Phenotyping Using Adaptive Spectral Indices and SAM3Yield trials and hill plots are widely used in plant breeding to evaluate large numbers of genotypes simultaneously. Extracting per-plot canopy boundaries from drone imagery is a key step in this process, but manual delineation is time-consuming, and rigid grid overlays do not account for true canopy boundaries. This paper presents an annotation-free pipeline for segmenting individual plots from multispectral drone imagery, requiring only approximate plot dimensions as input. The pipeline first... A. Lotfi, S. Shirtliffe, A. Carter, T. Ha, M. Eramian, S. Neupane |