Machine-learned prediction of annual crop planting in the U.S. Corn Belt based on historical crop planting maps

Abstract An accurate crop planting map can provide essential information for decision support in agriculture. The method of post-season and in-season crop mapping has been widely studied in the land use and land cover community. However, it remains a challenge to predict the spatial distribution of crop planting before the growing season. This paper is the first attempt to use machine learning approach on the prediction of field-level annual crop planting from historical crop planting maps. We present an end-to-end machine learning framework for crop planting prediction using Cropland Data Layer (CDL) time series as reference data and multi-layer artificial neural network as prediction model. The proposed framework was first tested at Lancaster County of Nebraska State, then scaled up to the U.S. Corn Belt. According to the experiment results from 53 Agricultural Statistics Districts, we found the machine-learned crop planting map was expected to reach 88% agreement with the future CDL. Meanwhile, the crop acreage estimates derived from the machine-learned prediction were highly correlated (R2 > 0.9) with the crop acreage estimates of CDL and official statistics by the U.S. Department of Agriculture National Agricultural Statistics Service. This study provides a low-cost and efficient way to predict annual crop planting map, which can be used to support many agricultural applications and decision makings before the beginning of a growing season.

[1]  Xanthoula Eirini Pantazi,et al.  Wheat yield prediction using machine learning and advanced sensing techniques , 2016, Comput. Electron. Agric..

[2]  Hang Zhou,et al.  Deep learning based multi-temporal crop classification , 2019, Remote Sensing of Environment.

[3]  W. Raun,et al.  In-Season Prediction of Corn Grain Yield Potential Using Normalized Difference Vegetation Index , 2006 .

[4]  Yuqi Bai,et al.  Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer , 2017 .

[5]  David M. Johnson,et al.  Measuring land-use and land-cover change using the U.S. department of agriculture's cropland data layer: Cautions and recommendations , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[7]  N. Silleos,et al.  The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction , 1993 .

[8]  John P. Fulton,et al.  An overview of current and potential applications of thermal remote sensing in precision agriculture , 2017, Comput. Electron. Agric..

[9]  Heather McNairn,et al.  Early season monitoring of corn and soybeans with TerraSAR-X and RADARSAT-2 , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Sigeru Omatu,et al.  Neural network approach to land cover mapping , 1994, IEEE Trans. Geosci. Remote. Sens..

[11]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[12]  Molly E. Brown.  Remote sensing technology and land use analysis in food security assessment , 2016 .

[13]  D. Mulla.  Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[14]  Aydin Kaya,et al.  Analysis of transfer learning for deep neural network based plant classification models , 2019, Comput. Electron. Agric..

[15]  Marcel Salathé,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..

[16]  David H. Fleisher,et al.  Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units , 2012 .

[17]  Nataliia Kussul,et al.  Early Season Large-Area Winter Crop Mapping Using MODIS NDVI Data, Growing Degree Days Information and a Gaussian Mixture Model , 2017 .

[18]  B. Wylie,et al.  Rapid Crop Cover Mapping for the Conterminous United States , 2018, Scientific Reports.

[19]  S. K. Balasundram,et al.  A Review: The Role of Remote Sensing in Precision Agriculture , 2010 .

[20]  Nataliia Kussul,et al.  Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data , 2017, IEEE Geoscience and Remote Sensing Letters.

[21]  Fei Chen,et al.  Noah‐MP‐Crop: Introducing dynamic crop growth in the Noah‐MP land surface model , 2016 .

[22]  J. Hicke,et al.  Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA , 2015 .

[23]  Xuesong Zhang,et al.  Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt , 2014 .

[24]  Xiaoming Xu,et al.  Integrating the system dynamic and cellular automata models to predict land use and land cover change , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Pablo M. Granitto,et al.  Deep learning for plant identification using vein morphological patterns , 2016, Comput. Electron. Agric..

[26]  Li Lin,et al.  Improvement and Validation of NASA/MODIS NRT Global Flood Mapping , 2019, Remote. Sens..

[27]  D. Roy,et al.  Robust Landsat-based crop time series modelling , 2020 .

[28]  Tarmo Lipping,et al.  Crop yield prediction with deep convolutional neural networks , 2019, Comput. Electron. Agric..

[29]  Zhengwei Yang,et al.  CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support , 2012 .

[30]  Maria Gruszczynska,et al.  Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices , 2002 .

[31]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[32]  A. J. Richardson,et al.  Vegetation indices in crop assessments , 1991 .

[33]  Stephan Dabbert,et al.  Generating crop sequences in land-use models using maximum entropy and Markov chains , 2011 .

[34]  Mariana Belgiu,et al.  Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis , 2018 .

[35]  Mutlu Ozdogan,et al.  Large area cropland extent mapping with Landsat data and a generalized classifier , 2018, Remote Sensing of Environment.

[36]  Jordi Inglada,et al.  Assessment of a Markov logic model of crop rotations for early crop mapping , 2015, Comput. Electron. Agric..

[37]  Zhengwei Yang,et al.  Cloud Environment for Disseminating NASS Cropland Data Layer , 2019, 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics).

[38]  Soizik Laguette,et al.  Remote sensing applications for precision agriculture: A learning community approach , 2003 .

[39]  P. Srivastava,et al.  Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information , 2015, Environmental Processes.

[40]  David M. Johnson,et al.  A multi-resolution approach to national-scale cultivated area estimation of soybean , 2017 .

[41]  Terry L. Kastens,et al.  Image masking for crop yield forecasting using AVHRR NDVI time series imagery , 2005 .

[42]  Zhengwei Yang,et al.  Remote sensing for agricultural applications , 2017 .

[43]  Armando Apan,et al.  Early-season crop area estimates for winter crops in NE Australia using MODIS satellite imagery , 2010 .

[44]  Meixia Deng,et al.  The effect of corn–soybean rotation on the NDVI-based drought indicators: a case study in Iowa, USA, using Vegetation Condition Index , 2015 .

[45]  George F. Hepner,et al.  Application of an artificial neural network to landcover classification of thematic mapper imagery , 1990 .

[46]  Giles M. Foody,et al.  Land Cover Classification by an Artificial Neural Network with Ancillary Information , 1995, Int. J. Geogr. Inf. Sci..

[47]  M. S. Mondal,et al.  Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results , 2016 .

[48]  A. J. Stern,et al.  Crop Yield Assessment from Remote Sensing , 2003 .

[49]  Prasad S. Thenkabail,et al.  Remote Sensing of Global Croplands for Food Security , 2009 .

[50]  Yud-Ren Chen,et al.  Machine vision technology for agricultural applications , 2002 .

[51]  Liping Di,et al.  A Decision-Tree Classifier for Extracting Transparent Plastic-Mulched Landcover from Landsat-5 TM Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  Geoffrey M. Henebry,et al.  Characterizing land cover/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier , 2020 .

[53]  Emmanuelle Vaudour,et al.  Early-season mapping of crops and cultural operations using very high spatial resolution Pléiades images , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[54]  Pengyu Hao,et al.  Annual Cropland Mapping Using Reference Landsat Time Series - A Case Study in Central Asia , 2018, Remote. Sens..

[55]  Paolo Remagnino,et al.  Deep-plant: Plant identification with convolutional neural networks , 2015, 2015 IEEE International Conference on Image Processing (ICIP).

[56]  Xianjun Hao,et al.  Agriculture Drought and Food Security Monitoring Over the Horn of Africa (HOA) from Space , 2018, 2018 7th International Conference on Agro-geoinformatics (Agro-geoinformatics).

[57]  Zheng Niu,et al.  Comparison of Hybrid Classifiers for Crop Classification Using Normalized Difference Vegetation Index Time Series: A Case Study for Major Crops in North Xinjiang, China , 2015, PloS one.

[58]  Liping Di,et al.  RF-CLASS: A remote-sensing-based flood crop loss assessment cyber-service system for supporting crop statistics and insurance decision-making , 2017 .

[59]  Douglas K. Bolton,et al.  Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics , 2013 .

[60]  C. Walthall,et al.  Artificial neural networks for corn and soybean yield prediction , 2005 .

[61]  M. Benoît,et al.  Modeling the spatial distribution of crop sequences at a large regional scale using land-cover survey data: A case from France , 2014 .

[62]  Pengyu Hao,et al.  Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data , 2018, PeerJ.