Abstract
Conventional crop-monitoring methods are time-consuming and labor-intensive, necessitating new techniques to provide faster measurements and higher sampling intensity. This study reports on mathematical modeling and testing of growth status for Chinese cabbage and white radish using unmanned aerial vehicle-red, green and blue (UAV-RGB) imagery for measurement of their biophysical properties. Chinese cabbage seedlings and white radish seeds were planted at 7–10-day intervals to provide a wide range of growth rates. Remotely sensed digital imagery data were collected for test fields at approximately one-week intervals using a UAV platform equipped with an RGB digital camera flying at 2 m/s at 20 m above ground. Radiometric calibrations for the RGB band sensors were performed on every UAV flight using standard calibration panels to minimize the effect of ever-changing light conditions on the RGB images. Vegetation fractions (VFs) of crops in each region of interest from the mosaicked ortho-images were calculated as the ratio of pixels classified as crops segmented using the Otsu threshold method and a vegetation index of excess green (ExG). Plant heights (PHs) were estimated using the structure from motion (SfM) algorithm to create 3D surface models from crop canopy data. Multiple linear regression equations consisting of three predictor variables (VF, PH, and VF × PH) and four different response variables (fresh weight, leaf length, leaf width, and leaf count) provided good fits with coefficients of determination (R2) ranging from 0.66 to 0.90. The validation results using a dataset of crop growth obtained in a different year also showed strong linear relationships (R2 > 0.76) between the developed regression models and standard methods, confirming that the models make it possible to use UAV-RGB images for quantifying spatial and temporal variability in biophysical properties of Chinese cabbage and white radish over the growing season.
Highlights
On-site monitoring of crop growth throughout the growing season plays an important role in assessing overall crop conditions, determining when to irrigate, and forecasting potential yields [1,2,3,4]
The results indicate that the digital numbers (DNs) could be successfully converted into reflectance spectra, showing strong exponential relationships with coefficient of determination (R2) ranging from 0.93 to 0.99, and their reflectance data could be normalized to minimize the effect of varying sunlight conditions on unmanned aerial vehicles (UAVs)-RGB images
Crop growth estimation models based on UAV-RGB imagery were developed and validated for quantifying various biophysical parameters of field-grown Chinese cabbage and white radish
Summary
On-site monitoring of crop growth throughout the growing season plays an important role in assessing overall crop conditions, determining when to irrigate, and forecasting potential yields [1,2,3,4]. Crop-monitoring studies have used in-field measurements or airborne/satellite data to effectively cover wide areas. Field-based methods involving on-site sampling and laboratory analysis have disadvantages in collection of data because they are often destructive, labor-intensive, costly, and time consuming, thereby limiting the number of samples required for establishment of efficient crop growth management [8,9]. Precision agriculture is a site-specific soil and crop management system that assesses variability in soil properties (e.g., pH, organic matter, and soil nutrient levels) and field (e.g., slope and elevation) and crop parameters (e.g., yield and biomass) using various tools including the global positioning system (GPS), geographic information systems (GIS), and remote sensing (RS). A common use of remote sensing is evaluation of crop growth status based on canopy greenness by quantifying the distribution of vegetation index (VI) in the crop field. Various vegetation indices, including Normalized Difference Vegetation Index (NDVI) and Excess Green (ExG), have been defined as representative reflectance values of the vegetation canopy [12]
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