Proceedings
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| Filter results6 paper(s) found. |
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1. Mapping Marginal Crop Land on Millions of Acres in the Canadian PrairiesCrop fields cover more than 250,000 km2 of the Canadian Prairies, and many of these contain areas of marginal soil condition that are farmed annually at a loss. Setting aside these unprofitable areas may represent savings for growers as well as reductions in GHG emissions, while restoring them with perennial vegetation could create new natural carbon sinks. There is high potential for these in-field marginal zones to act as a nature-based climate solution in Alberta, Saskatchewan and Manitoba.... S. Shirtliffe, T. Ha, K. Nketia |
2. Digital Agriculture Driven by Big Data Analytics: a Focus on Spatio-temporal Crop Yield Stability and Land ProductivityIn the ever-evolving landscape of agriculture, the adoption of digital technologies and big data analytics has ushered in a transformative era known as digital agriculture. This paradigm shift is primarily motivated by the pressing imperative to address the growing global population's food requirements, mitigate the adverse effects of climate change, and promote sustainable land management. Canada, a significant player in global food production, has made a substantial commitment to reducing... K. Nketia, T. Ha, H. Fernando, S. Shirtliffe, S. Van Steenbergen |
3. 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... |
4. GAIG: High‑Resolution Wall‑to‑Wall Modelling of Within‑Field Spatial Variability in Crop YieldQuantifying the temporal stability and causes of within‑field variation in crop yield is fundamental to precision‑agriculture research, particularly as agricultural areas seek to identify lands with persistently low productivity that may constitute marginal cropland. What is needed is a modelling framework capable of using spatial patterns in yield to reveal stability zones, diagnose sources of variability, and enable consistent comparison across farms and years. Accordingly, the objective... |
5. Upscaling UAV Image-Trained Machine Learning Models from Research Plots to Commercially Cropped LandHigh-throughput plant phenotyping (HTPP) leverages the advancement of unmanned aerial vehicles (UAVs) technology, paired with improvement in spectral sensing technology to allow for the derivation of plant phenotypic traits from image analysis. Crop breeding programs continue to increase incorporation of HTTP methods into their pipelines to enhance their efficiency of selecting for varieties. Machine learning (ML) models, often used hand in hand with HTTP methods, generate phenotypic trait predictions... W. Maess, S. Shirtliffe, K. Nketia |
6. Africa Regional Meeting... K. Nketia |