Predicting county-scale maize yields with publicly available data

Jiang, Zehui; Liu, Chao; Ganapathysubramanian, Baskar; Hayes, Dermot J.; Sarkar, Soumik

doi:10.1038/s41598-020-71898-8

Cited by 35 publications

(13 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the model forecasting the yield at the end of May, one of the important factors influencing the rapeseed's proper growth and development was the average air temperature in the period from 1 to 31 May. Jiang et al [79], when forecasting the yield of maize for ten states in the United States, identified the ten best input variables, including minimum, maximum, and average temperature.…”

Section: Primary Factorsmentioning

confidence: 99%

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Hara

Piekutowska

Niedbała

2021

Land

View full text Add to dashboard Cite

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

show abstract

Section: Primary Factorsmentioning

confidence: 99%

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Hara

Piekutowska

Niedbała

2021

Land

View full text Add to dashboard Cite

show abstract

“…One of the avenues to yield prediction is through the fusion of high dimensional phenotypic trait data using machine learning (ML) approaches to provide plant breeders the tools to do in-season seed yield (SY) prediction [7], and fusing ML and optimization techniques to identify a suite of in-season phenotypic traits collected from multiple sensors that decrease the dependence on resource-intensive end-season phenotyping in breeding programs [8]. Other avenues have been through integrating weather and genetic information in conjunction with deep time series attention models for crop SY prediction [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

et al. 2021

Self Cite

View full text Add to dashboard Cite

Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean (Glycine max L. (Merr.)) pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multiview image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort and opening new breeding method avenues to develop cultivars.

show abstract

“…Some of these models also integrate remotely sensed satellite information such as soil moisture or leaf area index [12][13][14][15]. A strong research bias has been toward modeled corn yields versus other crops with any of these methods [16][17][18][19][20]. Predictions from any of these models can be good, but they suffer from complexity in an operational setting because many input datasets and assumptions must be managed.…”

Section: Introductionmentioning

confidence: 99%

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

et al. 2021

View full text Add to dashboard Cite

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R2 of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R2 results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2 performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

show abstract

Predicting county-scale maize yields with publicly available data

Cited by 35 publications

References 23 publications

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better than Simple Trend Analyses?

Contact Info

Product

Resources

About