Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV

Abstract

The vegetation index (VI) has been successfully used to monitor the growth and to predict the yield of agricultural crops. In this paper, a long-term observation was conducted for the yield prediction of maize using an unmanned aerial vehicle (UAV) and estimations of chlorophyll contents using SPAD-502. A new vegetation index termed as modified red blue VI (MRBVI) was developed to monitor the growth and to predict the yields of maize by establishing relationships between MRBVI- and SPAD-502-based chlorophyll contents. The coefficients of determination (R2s) were 0.462 and 0.570 in chlorophyll contents’ estimations and yield predictions using MRBVI, and the results were relatively better than the results from the seven other commonly used VI approaches. All VIs during the different growth stages of maize were calculated and compared with the measured values of chlorophyll contents directly, and the relative error (RE) of MRBVI is the lowest at 0.355. Further, machine learning (ML) methods such as the backpropagation neural network model (BP), support vector machine (SVM), random forest (RF), and extreme learning machine (ELM) were adopted for predicting the yields of maize. All VIs calculated for each image captured during important phenological stages of maize were set as independent variables and the corresponding yields of each plot were defined as dependent variables. The ML models used the leave one out method (LOO), where the root mean square errors (RMSEs) were 2.157, 1.099, 1.146, and 1.698 (g/hundred grain weight) for BP, SVM, RF, and ELM. The mean absolute errors (MAEs) were 1.739, 0.886, 0.925, and 1.356 (g/hundred grain weight) for BP, SVM, RF, and ELM, respectively. Thus, the SVM method performed better in predicting the yields of maize than the other ML methods. Therefore, it is strongly suggested that the MRBVI calculated from images acquired at different growth stages integrated with advanced ML methods should be used for agricultural- and ecological-related chlorophyll estimation and yield predictions.

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

“…Most of the studies use a combination of UAV complemented by a multispectral sensor to analyse agriculture crops for precision farming (e.g., tomato, vineyard, or wheat production), where they usually employ Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), or the Soil Adjusted Vegetation Index (SAVI), to monitor crop health [27][28][29][30][31][32]. Authors monitoring vineyards described the use of multispectral and thermal sensors in combination to obtain additional information about crop water status [33][34][35].…”

Hyperspectral Estimation Models of Winter Wheat Chlorophyll Content Under Elevated CO2

“…The current results showed that the sensitive bands at 740 and 749 nm wavelength correlated with the chlorophyll content most, under aCO 2 and eCO 2 , respectively ( Figures 4–6 ), even though the established model fit slightly better under aCO 2 than at eCO 2 conditions. Vegetation indexes calculated from hyperspectral remote sensing technology had long been used to monitor the chlorophyll content of vegetation leaves ( Meng et al, 2012 ; Guo et al, 2020 ). Among the five tested vegetation indexes, the DVI based model simulated the chlorophyll content best under both aCO 2 and eCO 2 conditions and the model using overall data from both the CO 2 treatments gave similar results (results not shown).…”

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV): Norway Spruce Forest Case Study

“…So far, most of the studies using UAVs and multispectral cameras have been carried out on agricultural species such as maize [69][70][71], sugar beet crops [72] and wheat [73,74]. As concerns woody species, the UAV-based approach has been applied to managed plantations with regular plant arrangement, such as vineyards [75] and fruit tree orchards [76,77].…”