Real-time remote-sensing based monitoring for the rice industry
University of New England
Project code: PRO-013078
Project stage: Current
Project start date: Tuesday, November 30, 2021
Project completion date: Sunday, August 31, 2025
Jounral Articles From Project: Rice ponding date detection using Sentinel-2 and Planet Fusion imagery Agricultural and Water Management (Issue: Not yet submitted on 1/12/2022), EARLY-SEASON INDUSTRY-WIDE RICE MAPS USING SENTINEL-2 TIME SERIES International Geoscience and Remote Sensing Symposium (Issue: Under review on 18/7/2022), Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery Agricultural and Water Management (Issue: 273 on 29/8/2022), EARLY-SEASON INDUSTRY-WIDE RICE MAPS USING SENTINEL-2 TIME SERIES International Geoscience and Remote Sensing Symposium (Issue: 1 on 20/7/2022), The influence of nitrogen and variety on rice grain moisture dry down Field Crops Research (Issue: Under review on 31/12/2023), Probabilistic rice phenology forecasting using per-date machine learning classification methods Agronomy Journal (Issue: Under preparation on 31/12/2023), Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery Agricultural and Water Management (Issue: 273 on 11/1/2022), EARLY-SEASON INDUSTRY-WIDE RICE MAPS USING SENTINEL-2 TIME SERIES International Geoscience and Remote Sensing Symposium (Issue: 2022 on 28/9/2022), Real-time predictive modelling of rice crops to optimize field management (Abstract) International Rice Conference (IRRI Philippines) (Issue: 1 on 17/10/2023), Predicting rice phenology and optimal sowing dates in temperate regions using machine learning Agronomy Journal (Issue: Forthcoming on 14/6/2023), The influence of nitrogen and variety on rice grain moisture content dry-down Field Crops Research (Issue: 302 on 18/7/2023)
National Priority: RIC-Agronomy and farming systems
This project will build on past knowledge, investment, collaboration and existing frameworks to deliver Australian rice growers, Rice Extension and SunRice a suite of real-time monitoring tools and alerts for improved rice management decision making and benchmarking. It will develop remote sensing products for growers that will directly address a number of key industry needs, including: improving yields (t/ha), water productivity (t/ML) and profitability ($/t). These outputs will be updated frequently on secure cloud services, and available for delivery through timely email alerts and platforms (such as MapRice).
The outputs will include:
(i) automatic detection of water application dates per field (critical for accurate growth stage prediction, which in turn is critical for optimal management such as N application at PI to reach yield potential, and maintaining sufficient water depth before flowering to minimise cold damage),
(ii) grain moisture prediction for optimal drainage and harvest decisions,
(iii) phenological date alerts including PI, flowering and maturity for management decisions,
(iv) real-time growth curves to alert to potential issues,
(v) automated detection of all rice fields,
(vi) yield forecasts,
(vii) powerful benchmarking of productivity vs water management based on all the above, to provide growers with insights that will improve industry-wide adoption of management strategies that result in higher productivity.
Rice growers, DPI agronomists, Rice Extension and SunRice have identified these deliverables as crucial information gaps that remote sensing science is able to fill.
The collaborative project team is industry-driven and research-based. On other projects, the team has delivered 10,000 ha of nitrogen maps to growers in 2020/2021, and are therefore well placed to deliver useful decision-support tools to growers. The team includes industry participants SunRice (provision of industry data, guidance on requirements needed for commercial adoption and delivery of alerts and outputs to growers including through MapRice), RRAPL (field-scale historical data and validation) and Rice Extension (facilitating adoption and use of the outputs and gathering feedback from growers); research groups NSWDPI (rice agronomy) and UNE AARSC (remote sensing, analysis). This collaboration of industry, research and commercial delivery will ensure the outcomes are grower demanded, well validated and ready for adoption.
University of New England