Use of a Digital Camera to Monitor the Growth and Nitrogen Status of Cotton

Abstract

The main objective of this study was to develop a nondestructive method for monitoring cotton growth and N status using a digital camera. Digital images were taken of the cotton canopies between emergence and full bloom. The green and red values were extracted from the digital images and then used to calculate canopy cover. The values of canopy cover were closely correlated with the normalized difference vegetation index and the ratio vegetation index and were measured using a GreenSeeker handheld sensor. Models were calibrated to describe the relationship between canopy cover and three growth properties of the cotton crop (i.e., aboveground total N content, LAI, and aboveground biomass). There were close, exponential relationships between canopy cover and three growth properties. And the relationships for estimating cotton aboveground total N content were most precise, the coefficients of determination (R 2) value was 0.978, and the root mean square error (RMSE) value was 1.479 g m−2. Moreover, the models were validated in three fields of high-yield cotton. The result indicated that the best relationship between canopy cover and aboveground total N content had an R 2 value of 0.926 and an RMSE value of 1.631 g m−2. In conclusion, as a near-ground remote assessment tool, digital cameras have good potential for monitoring cotton growth and N status.

Monitoring the Nitrogen Nutrition Status of Rice Plants Using Spectral and Image Technologies

“…Compared with other image parameters, CC is obtained more easily and is not affected by weather or light intensity. The R 2 value of the model to predict PNA was 0.6768 (RMSE, 11.6925 g.m -2 ), lower than that reported for cotton [18]. This is because rice, as a dryland crop, has a more complex growing environment than that of cotton, and image processing is affected by complex environmental factors such as water.…”

“…constructed a regression model of a maize N nutrition index using a dual-band spectral index (R710, R512) [24], and it was proven to be a can describe plant color [25], which can reflect its nutrient status, especially N content and absorption. Several studies have shown that RGB color space parameters extracted from vegetation canopy images can be used to predict vegetation yield and nutrient status [14,18,19]. Among the models constructed with image parameters, those constructed using NRI and GMR were unstable, possibly because the parameters of NRI and GMR were obtained by extracting RGB values from images.…”

“…The results revealed an exponential relationship between the image parameter canopy cover (CC) and aboveground total N content. The R 2 value of the model was 0.926, and the root mean square error (RMSE) value was 1.631 g• m −2 [18]. Lee et al used image red-green-blue (RGB) parameters and CC to monitor rice nutrition status in real time [12,19].…”

Effects of Foliar Applied Titanium (Ti) on Quality and Nutrient Uptake Of Creeping Bentgrass (Agrostis Stolonifera) on Professional Golf Course Greens

“…Near-ground remote sensing is becoming increasingly important in modern agriculture production (Jia et al, 2014). Ground-based observations of crop growth provide fast, real-time, non-destructive, automatic, and relatively inexpensive information about crop status (Jia et al, 2014).…”

“…Near-ground remote sensing is becoming increasingly important in modern agriculture production (Jia et al, 2014). Ground-based observations of crop growth provide fast, real-time, non-destructive, automatic, and relatively inexpensive information about crop status (Jia et al, 2014). This information can significantly increase yields by allowing growers to properly time cultivation, fertilizer application, irrigation, pest control, and harvest (Jia et al, 2014).…”