An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning

Filippi, Patrick; Jones, E.V.; Wimalathunge, N.S.; Somarathna, P.D.S.N.; Pozza, Liana; Ugbaje, Sabastine U.; Jephcott, Thomas G.; Paterson, Stacey E.; Whelan, Brett; Bishop, Thomas

doi:10.1007/s11119-018-09628-4

Cited by 192 publications

(103 citation statements)

References 23 publications

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“…Crop yield is driven by the interaction of management, soil, and weather conditions. The yield would vary not only from season to season but also from location to location [14]. To investigate the prediction errors comprehensively, we summarized them according to the areas and the machine learning types (Figure 7).…”

Section: Comparison Of Forecast Errors In Different Areasmentioning

confidence: 99%

“…and measured yields at different temporal and spatial scales [2,[9][10][11][12][13]. Such regression results did show distinctly how climatic factors affected yields, however their relative lower explanation ability was commonly debated, and the dominant factors controlling yields often varied by geographical location, crop variety, and growing season [14]. Thus, the spatial generalization ability of these models is very low, that is to say they were difficult to apply to larger areas.…”

Section: Introductionmentioning

confidence: 99%

“…decision support system for agrotechnology transfer (DSSAT) [15], agricultural production systems sIMulator (APSIM) [16], model to capture the crop-weather relationship over a large area (MCWLA) [8,17], and world food studies (WOFOST) [18]. Although these models can simulate crop yields with higher accuracy, lots of inputs (e.g., climatic variables, fertilizers, irrigations, soil, and hydrological features) are required to run a model, which are time-consuming, cost-intensive, and difficult to popularize into a larger region or a developing country [14,19,20]. Climate variables are the primary inputs for the above two approaches.…”

Section: Introductionmentioning

confidence: 99%

“…Crop yield is a function of the interaction between spatial and temporal changes of variables. Considering the strong ability in treating multi-dimensional datasets [14,25]. Accordingly, machine learning techniques could provide powerful supports for improving yield prediction models.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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Wheat is one of the main crops in China, and crop yield prediction is important for regional trade and national food security. There are increasing concerns with respect to how to integrate multi-source data and employ machine learning techniques to establish a simple, timely, and accurate crop yield prediction model at an administrative unit. Many previous studies were mainly focused on the whole crop growth period through expensive manual surveys, remote sensing, or climate data. However, the effect of selecting different time window on yield prediction was still unknown. Thus, we separated the whole growth period into four time windows and assessed their corresponding predictive ability by taking the major winter wheat production regions of China as an example in the study. Firstly we developed a modeling framework to integrate climate data, remote sensing data and soil data to predict winter wheat yield based on the Google Earth Engine (GEE) platform. The results show that the models can accurately predict yield 1~2 months before the harvesting dates at the county level in China with an R2 > 0.75 and yield error less than 10%. Support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF) represent the top three best methods for predicting yields among the eight typical machine learning models tested in this study. In addition, we also found that different agricultural zones and temporal training settings affect prediction accuracy. The three models perform better as more winter wheat growing season information becomes available. Our findings highlight a potentially powerful tool to predict yield using multiple-source data and machine learning in other regions and for crops.

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Section: Comparison Of Forecast Errors In Different Areasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

et al. 2020

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“…Remote sensing data provide more accurate results and can be used for digital surface models of soil fractions on a regional scale. In [3], attention was paid to precision agriculture, processing of yield monitoring data for fields which often go with another information, such as studies of soil test results. Data were obtained from monitoring time series of yields for wheat and barley during three different seasons.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Remote Sensing Methods in Precision Agriculture

Бикбулатова¹,

Kupreyeva²,

Pronina³

et al. 2020

Proceedings of the International Scientific Conference the Fifth Technological Order: Prospects for the Development and Moderni

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Investigation of natural resources requires development and improvement of remote sensing methods, to be precise, satellite observations, laser scanning and survey using aerial vehicles; their importance increases with comprehensive research and solving problems of rational use of natural resources and environmental protection. Effective soil productivity management requires an integrated approach including using and developing of GPS (Global Positioning), GIS (Geographic Information Systems), YMT (Yield Monitor Technologies), VRT (Variable Rate Technology), and others. This integrated approach using computer and satellite technologies is called precision agriculture. Precision agriculture system is characterized not by a strictly defined set of methods and technical means; but by a general concept based on the use of satellite positioning technologies (GPS), geographic information systems (GIS), precise mapping of fields and others. Remote sensing technologies for observation the Earth from space are an essential tool for studying and constant monitoring of our planet that helps to effectively use and manage its resources.

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Predicting within‐field cotton yields using publicly available datasets and machine learning

2021

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Early detection of within-field yield variability for high-value commodity crops, such as cotton (Gossypium spp.), offers growers potential to improve decision-making, optimize yields, and increase profits. Over recent years, publicly available datasets have become increasingly available and at a resolution where within-field yield prediction is possible. However, the viability of using these datasets with machine learning to predict within-field cotton lint yield at key growth stages are largely unknown. This study was conducted on two cotton fields, located near Mungindi, New South Wales, Australia. Three years of yield data, soil, elevation, rainfall, and Landsat imagery were collected from each field. A total of 12 models were created using: (a) two machine learning algorithms: random forest (RF) and gradient boosting machines (GBM); (b) three growth stages: squaring, flowering, and boll-fill; and(c) two different amounts of variables: all variables and the optimal variables determined by a recursive feature elimination (RFE). Results showed a strong agreement between predicted and observed yields at flowering and boll-fill when more information was available. At flowering and boll-fill, root mean square error (RMSE) ranged between 0.15 and 0.20 t ha −1 and Lin's concordance correlation coefficient (LCCC) ranged between 0.50 and 0.66, with RF providing superior results in most cases. Models created using the optimal variables determined by the RFE provided similar results compared to using all variables, allowing greater model accuracy and resolution with targeted sampling. Overall, these findings indicate significant potential of publicly available datasets to predict within-field cotton yield and guide decisionmaking in-season.

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An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning

Cited by 192 publications

References 23 publications

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Prediction of Winter Wheat Yield Based on Multi-Source Data and Machine Learning in China

Using Remote Sensing Methods in Precision Agriculture

Predicting within‐field cotton yields using publicly available datasets and machine learning

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