Abstract
Predicting maize crop yields especially in maize production is paramount in order to alleviate poverty and contribute towards food security. Many regions experience food shortage especially in Africa because of uncertain climatic changes, poor irrigation facilities, reduction in soil fertility and traditional farming techniques. Therefore, predicting maize crop yields helps policymakers to make timely import and export decisions to strengthen national food security. However, none of the published work has been done to predict maize crop yields using machine learning in Eswatini, Africa. This paper aimed at applying machine learning (ML) to predict maize yields for a single season in Eswatini. A ML model was trained and tested using open-source data and local data. This is done by using three different data splits with the opensource predictor data consisting of 48 data points each with 7 attributes and open-source response data consisting of 48 data points each with a single attribute, adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination. At the second attempt, it is done by using the combined predictor data of 68 data points with 7 attributes each and combined response data of 68 data points with a single attribute each, with the same data splits and methods adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.
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Claims 18
Claim · model-written summary
open-source model before being normalized at 70:30 had adjusted R² 0.784.
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model before being normalized at 80:20 had adjusted R² 0.849.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model before being normalized at 90:10 had adjusted R² 0.878.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model after normalization at 70:30 had adjusted R² 1.00.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model after normalization at 80:20 had adjusted R² 1.00.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model after normalization at 90:10 had adjusted R² 1.00.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model after backward elimination at 70:30 had adjusted R² 0.846.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model after backward elimination at 80:20 had adjusted R² 0.886.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
open-source model after backward elimination at 90:10 had adjusted R² 0.885.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.784 (at 70:30), 0.849 (at 80:20), and 0.878 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.846 (at 70:30), 0.886 (at 80:20), and 0.885 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model before being normalized at 70:30 had adjusted R² 0.966.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model before being normalized at 80:20 had adjusted R² 0.972.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model before being normalized at 90:10 had adjusted R² 0.978.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model after normalization at 70:30 had adjusted R² 1.00.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model after normalization at 80:20 had adjusted R² 1.00.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model after normalization at 90:10 had adjusted R² 1.00.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model after backward elimination at 70:30 had adjusted R² 0.967.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model after backward elimination at 80:20 had adjusted R² 0.973.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”
Claim · model-written summary
combined model after backward elimination at 90:10 had adjusted R² 0.978.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“adjusted R² values were 0.966 (at 70:30), 0.972 (at 80:20), and 0.978 (at 90:10) before being normalized, 1.00 across the board after normalization, and 0.967 (at 70:30), 0.973 (at 80:20), and 0.978 (at 90:10) after backward elimination.”
t0“maize crop yields”