Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield

Abstract

Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R2) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R2 values and lower RMSECV values for all data sets. Overall, the highest R2 values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R2 = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model.

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

“…As such, only two images were required for estimating BT and NU a few days before each of the two TdFs. Yield prediction accuracy was higher at the booting stage (R 2 from 0.61 at tillering to 0.77 at booting with two images), which is consistent with a recent study [39] that showed that the best timing for predicting yield from hyperspectral measurements was indeed the booting stage. The alternative approach of using an intermediate image approximately seven to ten days after the first TdF is useful only in certain cases and, especially, if one is required to predict the plant's N content (NU or NC) at the maturity stage.…”

Prediction of End-Of-Season Tuber Yield and Tuber Set in Potatoes Using In-Season UAV-Based Hyperspectral Imagery and Machine Learning

“…Compared with other imaging modalities, such as natural color and multispectral imagey, hyperspectral imagery consists of hundreds of spectral bands, arranged in a narrower bandwidth, and is thus capable of providing more detailed spectral information which is of great importance for estimating complex plant traits, such as yield [ 15 ]. For example, the grain yield of paddy fields was predicted in the northern part of Vientiane in Laos, and the booting stage was found to be the best prediction time using hyperspectral data [ 16 ]. Also, wheat yield was successfully estimated using 190 hyperspectral narrow bands acquired from a UAV platform before harvest in Minnesota [ 17 ].…”

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

“…In gramineous crops, such as rice and wheat, the influence of soil background is prominent during early growth stages, and it decreases as vegetation cover (VC) increases. Numerous studies have shown that the appearance of panicles can not only change the light distribution in a crop canopy, but also considerably affect canopy spectral reflectance [25][26][27][28]. Inoue et al [29] and Asilo et al [30] also reported that panicles influence the relationship between X-band and LAI in rice.…”