Abstract
Precise estimation of the leaf area index (LAI) is vital in efficient maize growth monitoring and precision farming. Traditional LAI measurement methods are often destructive and labor-intensive, while techniques relying solely on spectral data suffer from limitations such as spectral saturation. To overcome these difficulties, the study integrated computer vision techniques with UAV-based remote sensing data to establish a rapid and non-invasive method for estimating the LAI in maize. Multispectral imagery of maize was acquired via UAV platforms across various phenological stages, and vegetation features were derived based on the Excess Green (ExG) Index and the Hue–Saturation–Value (HSV) color space. LAI standardization was performed through edge detection and the cumulative distribution function. The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively. When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages. The estimation results were validated against ground-truth data collected using the LAI-2200C plant canopy analyzer and compared with six machine learning algorithms, including Gradient Boosting (GB), Random Forest (RF), Ridge Regression (RR), Support Vector Regression (SVR), and Linear Regression (LR). Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34. However, VisLAI consistently outperformed all machine learning models, especially during the grain filling stage, demonstrating superior robustness and accuracy. The VisLAI model proposed in this study effectively utilizes UAV-captured visible light imagery and computer vision techniques to achieve accurate, efficient, and non-destructive estimation of maize LAI. It outperforms traditional and machine learning-based approaches and provides a reliable solution for real-world maize growth monitoring and agricultural decision-making.
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 19
Claim · model-written summary
VisLAI attained R2 0.84 at big trumpet.
Zea maysOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
VisLAI attained RMSE 0.24 at big trumpet.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
VisLAI attained R2 0.75 at tasseling–silking.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
VisLAI attained RMSE 0.35 at tasseling–silking.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
VisLAI attained R2 0.50 at grain filling.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
VisLAI attained RMSE 0.44 at grain filling.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
HSV-optimized VisLAI attained R2 0.92 at big trumpet.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
HSV-optimized VisLAI attained RMSE 0.19 at big trumpet.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
HSV-optimized VisLAI attained R2 0.90 at tasseling–silking.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
HSV-optimized VisLAI attained RMSE 0.23 at tasseling–silking.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
HSV-optimized VisLAI attained R2 0.85 at grain filling.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
HSV-optimized VisLAI attained RMSE 0.22 at grain filling.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“When HSV-based optimization was applied, VisLAI achieved even better performance, with R2 values of 0.92, 0.90, and 0.85, and RMSE values of 0.19, 0.23, and 0.22 at the respective stages.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
GB attained R2 0.88 at big trumpet.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
GB attained R2 0.88 at tasseling–silking.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
GB attained R2 0.65 at grain filling.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
GB attained RMSE 0.22 at big trumpet.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
GB attained RMSE 0.25 at tasseling–silking.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
GB attained RMSE 0.34 at grain filling.
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“Among these, GB achieved the best performance, with R2 values of 0.88, 0.88, and 0.65, and RMSE values of 0.22, 0.25, and 0.34.”
e1“The proposed LAI estimation model, named VisLAI, based solely on visible light imagery, demonstrated high accuracy, with R2 values of 0.84, 0.75, and 0.50, and RMSE values of 0.24, 0.35, and 0.44 across the big trumpet, tasseling–silking, and grain filling stages, respectively.”
Claim · model-written summary
VisLAI outperformed all machine learning models, especially at grain filling.
- Organ / stage
- grain filling stagee0
- Outcome
- outperformede0
- Direction
- outperformede0
- Comparator
- all machine learning modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Maize”
e0“VisLAI consistently outperformed all machine learning models, especially during the grain filling stage”