Using Artificial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

Abstract

Crop yield prediction prior to harvest is important for crop income and insurance projections, and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and predictor variables, especially at the field scale. In this study, an artificial neural network (ANN) method was used: (1) to evaluate the relative importance of predictor variables for the prediction of within-field corn and soybean end-of-season yield and (2) to evaluate the performance of the ANN models with a minimal optimized variable dataset for their capacity to predict corn and soybean yield over multiple years at the within-field level. Several satellite derived vegetation indices (normalized difference vegetation index—NDVI, red edge NDVI and simple ratio—SR) and elevation derived variables (slope, flow accumulation, aspect) were used as crop yield predictor variables, hypothesizing that the different variables reflect different crop and site conditions. The study identified the SR index and the slope as the most important predictor variables for both crop types during two training and testing years (2011, 2012). The dates of the most important SR images, however, were different for the two crop types and corresponded to their critical crop developmental stages (phenology). The relative mean absolute errors were overall smaller for corn compared to soybean: all of the 2011 corn study fields had errors below 10%; 75% of the fields had errors below 10% in 2012. The errors were more variable for soybean. In 2011, 37% of the fields had errors below 10%, while in 2012, 100% of the fields had errors below 20%. The results are promising and can provide yield estimates at the farm level, which could be useful in refining broader scale (e.g., county, region) yield projections.

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

“…Irrigation ratio [46], number of open wells (OW) [26] [41], number of tanks (TK) [ [40], GDD [24], growing degree days [46], killing degree days [46], day length [51] [13] [43], snow water [13], drought index (DI) [ [66], GCVI [50], VOD [46] [62], RVI [63] [39], GNDVI [63] [39], GRVI [63] [39], EVI2 [63], OSAVI [63] [39], WDRVI [63] [39], NDVIre [64], TSAVI [39], IPVI [39], MSAVI [39], GI [39], PVI [39], SAVI [39]], GESAVI [39], GLAI [39], CWSI [39], NDWI [39], GVI [39] LAI [42] [40] [65], FPAR [42], GPP [42], NIRv [56] [47], CDL [42], cropland census [42], satellite images from the landsat thematic mapper (TM) [42], satellite images from advanced wide field sensor (AWIFS) [42], empty-land [45], harrowed land [45], texture conditions [48], PVI [48].…”

“…Terliksiz et al[109] employed a 3D CNN method where LST and SR based spatiotemporal features from satellite data were utilized. Kross et al[64] evaluated the relative importance of predictor set consists of NDVI, red edge NDVI and SR. Finally, ANN based prediction model was developed using the selected variables.…”

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

“…The ANN-based predictive model forecasted the yield three months before harvest, and the RRMSE did not exceed 8%. Kross et al [12] studied the relationship between the topography of the area, NDVI, NDVIre, and simple ratio (SR) indices and the yield of maize and soybeans. These crops were grown in Eastern Ontario, Canada, and remote sensing data were acquired from June to August of each study year.…”

“…Crop prediction for the current growing season is a relatively difficult task due to the complexity of the relationship between the plant growth process and environmental factors such as weather or soil variability [12]. As such, the final yielding result is determined by many independent variables.…”

Yield Prediction in Soybean Crop Grown under Different Levels of Water Availability Using Reflectance Spectroscopy and Partial Least Squares Regression

“…Christenson et al [19] collected hyperspectral data from soybean canopy between R1 and R6 reproductive stages and applied PLSR to predict grain yield but did not conclude about the best stage for yield prediction. The R5 phenological stage has been suggested to be more suitable for soybean grain yield monitoring and prediction using satellite-based [11,13,78], UAV-based [15] and field-based [52,79] remote-sensed data.…”