36 research outputs found
Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development - Fig 3
<p>(a) Relationship between normalized difference vegetation index (NDVI) and fraction cover (f<sub>c</sub>); (b) Measured f<sub>c</sub> vs. corresponding f<sub>c</sub> values predicted using empirical equation in Fig 3A. The solid black diagonal line in the graph is the 1:1 line. The dashed black line is the least-squares linear regression between the measured and predicted values.</p
Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development - Fig 3
<p>(a) Relationship between normalized difference vegetation index (NDVI) and fraction cover (f<sub>c</sub>); (b) Measured f<sub>c</sub> vs. corresponding f<sub>c</sub> values predicted using empirical equation in Fig 3A. The solid black diagonal line in the graph is the 1:1 line. The dashed black line is the least-squares linear regression between the measured and predicted values.</p
Case_Study_4_weed_management_evaluation_Raw_Image_Data_10_of_10_GroundTruth
Part 10 of 10 of raw individual images with gps log and ground truth
Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development - Fig 2
<p>(a) Relationship between normalized difference vegetation index (NDVI) and leaf area index (LAI); (b) Measured LAI vs. corresponding values of LAI predicted using the empirical equation in Fig 2A. The solid black diagonal line in the graph is the 1:1 line. The dashed black line is the least-squares linear regression between the measured and predicted values.</p
Fractional vegetation cover (f<sub>c</sub>) map of the sorghum field derived from UAS imagery acquired on 10 June 2016.
<p>Fractional vegetation cover (f<sub>c</sub>) map of the sorghum field derived from UAS imagery acquired on 10 June 2016.</p
Regression models developed between vegetation indices and leaf area index (LAI) for the training data set.
<p>Best fit functions, determination coefficients (R<sup>2</sup>), root mean square errors (RMSE) and mean absolute performance errors (MAPE) are presented for the four vegetation indices.</p
Regression models developed between vegetation indices and fractional vegetation cover (f<sub>c</sub>) for the training data set.
<p>Best fit functions, determination coefficients (R<sup>2</sup>), root mean square errors (RMSE) and mean absolute performance errors (MAPE) are presented for the four vegetation indices.</p
Relationship between leaf area index (LAI) and fraction cover (f<sub>c</sub>) of sorghum.
<p>Relationship between leaf area index (LAI) and fraction cover (f<sub>c</sub>) of sorghum.</p
Relationships between normalized difference vegetation index (NDVI) and seeding rates for six different sorghum hybrids at 50, 66 and 74 days after planting (DAP) in 2016.
<p>Each data point represents the mean of three replicates and was regressed against seeding rate.</p
Case_Study_4_weed_management_evaluation_Raw_Image_Data_1_of_10
Part 1 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10