Analysis and forecasting of Australian rice yield using phenology-based aggregation of satellite and weather data

Brinkhoff, James; Clarke, Allister; Dunn, Brian W.; Groat, Mark

doi:10.1016/j.agrformet.2024.110055

Cited by 3 publications

(1 citation statement)

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“…Consequently, future research should explore methodologies to predict the harvest date, potentially through surveys conducted with growers and agronomists or by analysing crop vigour trends via satellite-derived VI. These methods have demonstrated considerable success in estimating rice phenology and harvest timing in Australia [62,63] and internationally [32,64]. Adoption of VI-based phenological estimates could also refine the aggregation of environmental variables, improving the static time intervals employed in this research [1,5].…”

Section: Limitationsmentioning

confidence: 99%

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

Clarke,

Yates,

Blanchard

et al. 2024

Remote Sensing

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Precise and prompt predictions of crop yields are crucial for optimising farm management, post-harvest operations, and marketing strategies within the agricultural sector. While various machine learning approaches have been employed to forecast crop yield, their application to grain quality, particularly head rice yield (HRY), is less explored. This research collated crop-level HRY data across four seasons (2017/18–2020/21) from Australia’s rice-growing region. Models were developed using the XGBoost algorithm trained at varying time steps up to 16 weeks pre-harvest. The study compared the accuracy of models trained on datasets with climate data alone or paired with vegetative indices using two- and four-week aggregations. The results suggest that model accuracy increases as the harvest date approaches. The dataset combining climate and vegetative indices aggregated over two weeks surpassed industry benchmarks early in the season, achieving the highest accuracy two weeks before harvest (LCCC = 0.65; RMSE = 6.43). The analysis revealed that HRY correlates strongly with agroclimatic conditions nearer harvest, with the significance of vegetative indices-based features increasing as the season progresses. These features, indicative of crop and grain maturity, could aid growers in determining optimal harvest timing. This investigation offers valuable insights into grain quality forecasting, presenting a model adaptable to other regions with accessible climate and satellite data, consequently enhancing farm- and industry-level decision-making.

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Section: Limitationsmentioning

confidence: 99%

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

Clarke,

Yates,

Blanchard

et al. 2024

Remote Sensing

Self Cite

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On crop yield modelling, predicting, and forecasting and addressing the common issues in published studies

Filippi,

Han,

Bishop

2024

Precision Agric

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There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), as well as the abundance of machine learning modelling approaches. However, there are several common issues in published studies in the field of precision agriculture (PA) that must be addressed. This includes the terminology used in relation to crop yield modelling, predicting, forecasting, and interpolating, as well as the way that models are calibrated and validated. As a typical example, many studies will take a crop yield map or several plots within a field from a single season, build a model with satellite or Unmanned Aerial Vehicle (UAV) imagery, validate using data-splitting or some kind of cross-validation (e.g. k-fold), and say that it is a ‘prediction’ or ‘forecast’ of crop yield. However, this poses a problem as the approach is not testing the forecasting ability of the model, as it is built on the same season that it is then validating with, thus giving a substantial overestimation of the value for decision-making, such as an application of fertiliser in-season. This is an all-too-common flaw in the logic construct of many published studies. Moving forward, it is essential that clear definitions and guidelines for data-driven yield modelling and validation are outlined so that there is a greater connection between the goal of the study, and the actual study outputs/outcomes. To demonstrate this, the current study uses a case study dataset from a collection of large neighbouring farms in New South Wales, Australia. The dataset includes 160 yield maps of winter wheat (Triticum aestivum) covering 26,400 hectares over a 10-year period (2014–2023). Machine learning crop yield models are built at 30 m spatial resolution with a suite of predictor data layers that relate to crop yield. This includes datasets that represent soil variation, terrain, weather, and satellite imagery of the crop. Predictions are made at both the within-field (30 m), and field resolution. Crop yield predictions are useful for an array of applications, so four different experiments were set up to reflect different scenarios. This included Experiment 1: forecasting yield mid-season (e.g. for mid-season fertilisation), Experiment 2: forecasting yield late-season (e.g. for late-season logistics/forward selling), Experiment 3: predicting yield in a previous season for a field with no yield data in a season, and Experiment 4: predicting yield in a previous season for a field with some yield data (e.g. two combine harvesters, but only one was fitted with a yield monitor). This study showcases how different model calibration and validation approaches clearly impact prediction quality, and therefore how they should be interpreted in data-driven crop yield modelling studies. This is key for ensuring that the wealth of data-driven crop yield modelling studies not only contribute to the science, but also deliver actual value to growers, industry, and governments.

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A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data

Peng,

Cheng,

Feng

et al. 2024

Remote Sensing

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Accurately predicting winter wheat yield before harvest could greatly benefit decision-makers when making management decisions. In this study, we utilized weather forecast (WF) data combined with Sentinel-2 data to establish the deep-learning network and achieved an in-season county-scale wheat yield prediction in China’s main wheat-producing areas. We tested a combination of short-term WF data from the China Meteorological Administration to predict in-season yield at different forecast lengths. The results showed that explicitly incorporating WF data can improve the accuracy in crop yield predictions [Root Mean Square Error (RMSE) = 0.517 t/ha] compared to using only remote sensing data (RMSE = 0.624 t/ha). After comparing a series of WF data with different time series lengths, we found that adding 25 days of WF data can achieve the highest yield prediction accuracy. Specifically, the highest accuracy (RMSE = 0.496 t/ha) is achieved when predictions are made on Day of The Year (DOY) 215 (40 days before harvest). Our study established a deep-learning model which can be used for early yield prediction at the county level, and we have proved that weather forecast data can also be applied in data-driven deep-learning yield prediction tasks.

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Analysis and forecasting of Australian rice yield using phenology-based aggregation of satellite and weather data

Cited by 3 publications

References 72 publications

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

Integrating Climate and Satellite Data for Multi-Temporal Pre-Harvest Prediction of Head Rice Yield in Australia

On crop yield modelling, predicting, and forecasting and addressing the common issues in published studies

A Deep–Learning Network for Wheat Yield Prediction Combining Weather Forecasts and Remote Sensing Data

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