Proceedings
Authors
| Filter results9 paper(s) found. |
|---|
1. A Counterfactual Modelling Framework with On-farm Experimentation for Guiding Site-specific Nitrogen ApplicationsNitrogen (N) fertiliser is a key driver of wheat grain protein content (GPC) and yield, and represents one of the largest variable input costs and sources of emissions in Australian grain production. Yet estimating optimal N fertiliser rates remains challenging due to spatio-temporal variability in soil N supply and crop nutrient demand, as well as dynamic interactions between yield, GPC, and water availability. On-farm experimentation (OFE) provides valuable insights into crop responses... M.J. Tilse, T. Bishop, S. Poole, P. Filippi |
2. An Interpretable Machine Learning Framework for Soil Nutrient Assessment Based on pH and Electrical ConductivityUnderstanding how the physical and chemical properties of soil influence nutrient availability is fundamental for advancing precision agriculture, as these properties directly affect the efficiency of macro- and micronutrient absorption by plants. In recent years, the increasing availability of open agricultural datasets has created new opportunities for developing data-driven frameworks capable of supporting large-scale soil assessment and decision-making. However, the effective integration of... T.A. Rodolfo, O.J. Gonzalez Zarate, C. Gonzalez Aguilera |
3. Detection of Maize Foliar Diseases Using AI Optimized for Deployment on Edge DevicesMaize is a strategic crop for both regional and global food security. Its productivity is significantly affected by several foliar diseases, among which—common rust, gray leaf spot, and blight—are some of the most prevalent and damaging. These pathologies can cause substantial yield losses if not detected and treated in a timely manner, making early diagnosis a fundamental factor to ensure healthy and sustainable crop development. However, traditional diagnostic methods based on manual... O.J. González Zarate, L. Macea Zabaleta, N. Castillo Ojeda, A.F. Flórez Olivera, T.A. Rodolfo, C. Gonzalez Aguilera |
4. An Integrated Water–Energy Vulnerability Index for Irrigated Agricultural RegionsThe growing interdependence between water availability and energy infrastructure has significantly increased the vulnerability of irrigated agricultural regions, particularly under conditions of climate variability, hydrological uncertainty, and seasonal demand peaks. Irrigated production systems simultaneously depend on reliable water supply and stable energy provision, making them particularly sensitive to disruptions in either domain. Although the water–energy nexus literature has advanced... T.A. Rodolfo, P.S. Schneider, M.A. Perez, F.D. Mantovan, H.R. Bressan, A.C. Reginatto |
5. Sensor-based Variable Rate Nitrogen Recommendations: Comparing Proximal, Drone, and Satellite Sensors in CornNitrogen (N) represents 20–25% of corn (Zea mays L.) production costs, yet 15–65% is lost through volatilization and leaching. Conventional uniform-rate application ignores spatial variability and seasonal demand. Sensor-based variable rate nitrogen (VRN) addresses this by using real-time reflectance data, but the influence of sensing platform proximal, drone, or satellite on economic outcomes under varying N stress remains under-researched. The objective of this study at Iron Horse,... A. Jakhar, L. Bastos, A. Bhattarai, K. Poudel, A. Dhaliwal |
6. Evaluation of Kriging Models and Variogram Structures for Daily Weather Interpolation Across Georgia, United StatesSpatial interpolation fills gaps between scattered weather stations to create continuous maps of variables like temperature. In Georgia, USA—a state with rolling hills in the north, coastal plains in the south, and the Appalachian foothills—this process is vital for accurate climate monitoring, irrigation scheduling, and crop-yield forecasting. Without reliable grids, downstream models suffer from bias or uncertainty. This study aimed to assess... |
7. A Canopy-based Decision Framework for Selecting Sensor Platform and Vegetation Index in Variable-rate Nitrogen Management of Irrigated CornSensor-based variable-rate nitrogen (VRN) management promises field-specific N optimization, yet the choice of sensing platform fundamentally alters N recommendations. At early growth stages, a "double penalty" emerges: nitrogen-deficient plants produce smaller canopies, exposing more bare soil, which deflates vegetation index (VI) values and inflates N recommendations where accuracy matters most. This study developed a canopy coverage-based decision framework for selecting optimal sensor... A. Jakhar, L. Bastos, R. Roth, S. Virk, A. Bhattarai, K. Poudel, A. Dhaliwal |
8. AgGeoSampler: A Geospatial Open-Source Data Acquisition and Sampling Design Dashboard for Agricultural ApplicationsModern agricultural and environmental research increasingly depends on high-resolution geospatial data to support precise, site-specific decision-making. Advances in satellite remote sensing, unmanned aerial systems, and digital soil mapping have generated vast spatial datasets that capture fine-scale variability in vegetation health, soil properties, and terrain attributes. However, translating this wealth of information into effective field-sampling... A. Bhattarai, A. Jakhar, K. Poudel, A. Dhaliwal, L.M. Bastos |
9. Predicting Yield Stability Classes Using Satellite Imagery in the Absence of Yield Monitor DataSite-specific management is essential for improving agricultural productivity while reducing input costs and minimizing environmental impacts. Although yield monitor data are commonly used to characterize within-field yield variability, their availability is often limited by technological and economic constraints. The primary objective of this study was to compare spatial–temporal stability classes derived from yield monitor data and satellite imagery in cotton production... K. Poudel, A. Bhattarai, A. Jakhar, L. Bastos, A. Dhaliwal |