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_c); (b) Measured f_c vs. corresponding f_c 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_c); (b) Measured f_c vs. corresponding f_c 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 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

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_2_of_10

Part 2 of 10 of raw individual images with gps log and ground truth. GPS log and ground truth are in part 10

Relationship between leaf area index (LAI) and fraction cover (f_c) of sorghum.

<p>Relationship between leaf area index (LAI) and fraction cover (f_c) of sorghum.</p

Regression models developed between vegetation indices and leaf area index (LAI) for the training data set.

<p>Best fit functions, determination coefficients (R²), 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_c) for the training data set.

<p>Best fit functions, determination coefficients (R²), root mean square errors (RMSE) and mean absolute performance errors (MAPE) are presented for the four vegetation indices.</p

Fractional vegetation cover (f_c) map of the sorghum field derived from UAS imagery acquired on 10 June 2016.

<p>Fractional vegetation cover (f_c) map of the sorghum field derived from UAS imagery acquired on 10 June 2016.</p

Relationship between final sorghum yield and NDVI on 18 June 2016.

<p>Relationship between final sorghum yield and NDVI on 18 June 2016.</p