A New Approach for Nitrogen Status Monitoring in Potato Plants by Combining RGB Images and SPAD Measurements

Abstract

Precise nitrogen (N) application ensures the best N status of potato plants to improve crop growth and food quality and to achieve the best N use efficiency. Four N fertilization levels (0, 2, 4 and 6 g N pot−1) were used to establish a critical N dilution curve (CNDC) of potato plants cultivated in substrates with a greenhouse environment. RGB images of potato plants were obtained, and a red–green fit index (RGFI) was calculated based on the linear relationship between R and G channels and the principle of the excess green index (EXG). The N in the substrate can meet the nutritional requirements of potato plants during the first 35 days after emergence. In order to solve the complex sampling problem of maintaining a sufficient N strip for aboveground dry biomass (DM) and crop nitrogen concentration, a reference curve method for detecting N status was proposed. RGFI and SPAD values from the economically optimum 4 g N pot−1 treatment were used to derive the reference curve. The RGFI and SPAD values from the 4 g N pot−1 treatment had high correlations and were fitted with a second-order polynomial function with an R2 value of 0.860 and an RMSE value of 2.10. The validation results show that the N concentration dilution curve constructed by RGFI and SPAD values can effectively distinguish N-limiting from non-N-limiting treatments, CNDCs constructed based on RGFI and SPAD values could be used as an effective N status monitoring tool for greenhouse potato production.

Can SPAD Values and CIE L*a*b* Scales Predict Chlorophyll and Carotenoid Concentrations in Leaves and Diagnose the Growth Potential of Trees? An Empirical Study of Four Tree Species

“…A threshold SPAD value can guide N management for crops [60]. The practice of using threshold SPAD values in nutritional diagnosis has been widely reported in rice [61][62][63], maize [60,64], wheat [65], potato [66,67], cassava [68], pumpkins [69], tomato [70], strawberries [71], and other crops such as Zizania, soybean, peanut, and cotton [72]. However, there are relatively few reports on tree species.…”

A non-destructive monitoring method for chlorophyll content of potato plants based on feature reconstruction algorithm

“…The Pearson correlation coefficient method was used to screen variables by evaluating the linear relationship between two parameters. Apart from Pearson correlation coefficient method, random forest (RF) provides feasible solutions for feature screening by comparing feature contributions [18,41]. RF was selected to assess the contribution rate of each feature during the two growth periods of potato plants in comparison with Pearson correlation coefficient method to further explore the law of feature contribution.…”

“…In recent years, remote sensing technology gradually place in the forefront position of modern crop growth monitoring due to its advantages of quickly, 2 accuracy, and non-destruction [11][12][13]. SPAD meter is widely adopted to measure chlorophyll content of crops [14,15], and the measurement results reflect the actual chlorophyll content [16], and can be used for nitrogen (N) concentration diagnosis [17,18]. However, the time-consuming, high labor intensity confine its further application in large areas detection.…”

RGB Imaging as a Tool for Remote Sensing of Characteristics of Terrestrial Plants: A Review

“…Surface of an individual plant [125] Vegetation cover (R 2 = 0.05-0.81) [45] Stem diameter [125] Plant height (R 2 = 0.27-0.53) [102,125] Plant water content (R 2 = 0.37-0.50) [102] Grain yield (R 2 = 0.26-0.59) [124] Flower number (R 2 = 0.83) SPAD (R 2 = 0.45) [126] Plant nitrogen concentration (R 2 = 0.28-0.68) [105,120] Leaf nitrogen concentration (R 2 = 0.26-0.65) [105,120] Aboveground biomass (R 2 = 0.47) [5] Fresh biomass (R 2 = 0.48-0.88) Dry biomass (R 2 = 0.27-0.81) [101] Global primary productivity (GPP) [46] LAI (R 2 = 0.09-0.88) [101,124] Surface of an individual plant [125] Vegetation cover (R 2 = 0.03-0.77) [45] Plant height (R 2 = 0.53-0.69) [101,125] Stem diameter [125] Excess green index [83,119] Flower number (R 2 = 0.58) [52] Excess red index (ExR) ExR = 1.4•g − r Plant nitrogen concentration (R 2 = 0.52-0.72) [104,105] Leaf nitrogen concentration (R 2 = 0.7) [105] Leaf nitrogen accumulation (R 2 = 0.49) [43] Aboveground biomass (R 2 = 0.40-0.56) [102,116] LAI (R 2 = 0.16-0.8) [43,102,117,124] Leaf dry matter (R 2 = 0.52) [43] Surface of an individual plant Stem diameter [125] Plant height (R 2 = 0.26-0.52) [102,125] Plant water content (R...…”