Abstract
Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 10
Claim · model-written summary
Six vegetation indices had the highest correlations with corn grain moisture, with r from 0.68 to 0.80.
- Taxon
- Corn
Zea mays subsp. mayst0 - Outcome
- CGMe0
- Value
- r: 0.68–0.80e0
- Comparator
- VIse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80)”
t0“Corn Grain Moisture”
Claim · model-written summary
Corn grain moisture estimation accuracy depended on model and train–test split ratio.
- Taxon
- CornZea mays subsp. mayst0
- Treatment
- ML model and train-test data split ratioe0
- Outcome
- CGM estimation accuracye0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The CGM estimation accuracy was impacted by the ML model and train-test data split ratio.”
t0“Corn Grain Moisture”
Claim · model-written summary
Input groups did not significantly affect corn grain moisture estimation accuracy.
- Taxon
- CornZea mays subsp. mayst0
- Outcome
- CGM estimation accuracye0
- Comparator
- input groupse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups.”
t0“Corn Grain Moisture”
Claim · model-written summary
RF with REFs+VIs and a 95:5 split performed best on training and entire data.
- Taxon
- CornZea mays subsp. mayst0
- Treatment
- RFe0
- Outcome
- validatione0
- Direction
- beste0
- Value
- rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%e0
- Qualifier
- 95:5 split ratioe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%).”
t0“Corn Grain Moisture”
Claim · model-written summary
Increasing the train–test split ratio decreased test performance of other models.
- Taxon
- CornZea mays subsp. mayst0
- Outcome
- performances of the other ML modelse0
- Direction
- decreasede0
- Qualifier
- train–test split ratioe0
Not reportedValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“an increase in the train–test split ratio decreased the performances of the other ML models”
t0“Corn Grain Moisture”
Claim · model-written summary
SVM performed best on test data with a 50:50 split and REFs+VIs inputs.
- Taxon
- CornZea mays subsp. mayst0
- Treatment
- SVMe0
- Outcome
- performed the beste0
- Direction
- beste0
- Value
- r = 0.70, rRMSE = 2.58%e0
- Qualifier
- 50:50 split ratioe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables.”
t0“Corn Grain Moisture”
Claim · model-written summary
The 95:5 train–test ratio performed best across models.
- Taxon
- CornZea mays subsp. mayst0
- Treatment
- 95:5 train–test ratioe0
- Outcome
- performancee0
- Direction
- beste0
Not reportedValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The 95:5 train–test ratio showed the best performance across all the models”
t0“Corn Grain Moisture”
Claim · model-written summary
RF was the most stable and consistent model.
- Taxon
- CornZea mays subsp. mayst0
- Treatment
- RFe0
- Outcome
- most stable and consistent ML modele0
- Value
- r: 0.95, rRMSE: 1.37%e0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%).”
t0“Corn Grain Moisture”
Claim · model-written summary
The authors suggest a 95:5 ratio for smaller or medium-sized datasets.
- Taxon
- CornZea mays subsp. mayst0
- Treatment
- 95:5 train–test ratioe1
- Outcome
- suitable ratio for relatively smaller or medium-sized datasetse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“which may be a suitable ratio for relatively smaller or medium-sized datasets”
e1“The 95:5 train–test ratio showed the best performance across all the models”
t0“Corn Grain Moisture”
Claim · model-written summary
The authors conclude that aerial remote sensing with machine learning could reliably predict pre-harvest corn grain moisture for harvest management.
- Taxon
- CornZea mays subsp. mayst0
- Outcome
- reliably predicting CGM at the pre-harvest stagee0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers”
t0“Corn Grain Moisture”