Unmanned aerial vehicles (UAVs)-based crop lodging susceptibility and seed yield assessment during different growth stages of rapeseed (Brassica napus)

Lodging is a great challenge in rapeseed production that significantly affects seed yield and quality. Prediction of lodging susceptibility and seed yield through unmanned aerial vehicles (UAVs)-based framework offers remarkable prospects for higher applicability in agriculture. This study aims to explore the possibility of using a UAV-based framework for predicting the stem and root lodging susceptibility (represented by safety factor, SFs and SFr respectively) and seed yield at different growth stages of rapeseed. The Red-Green-Blue (RGB) and multispectral (MS) images were captured during various growth stages by UAV platforms to calculate 16 vegetation indices (VIs). Furthermore, the relationships of these VIs with lodging susceptibility and seed yield were also established using multiple linear regression (MLR) and four machine learning methods (including random forest machine (RFR), support vector machine, artificial neural network, and K-nearest neighbors). The results revealed that MS-VIs provided a good estimation of seed yield, and stem and root lodging susceptibilities. Among the 16 VIs analyzed, MS-VI SR85 emerged as the best predictor for both seed yield and lodging susceptibility, as is evident by its highest importance scores. Furthermore, when RGB-VIs were coupled with MS-VIs, the R2 values for estimating seed yield, stem lodging and root lodging resistance were enhanced by 150%, 69.6% and 106%, respectively, in comparison with RGB-VIs. Similarly, the RFR provided a more accurate machine learning method for predicting seed yield and lodging susceptibility compared to the other three models. Stem elongation stage was the optimum growth stage for the estimation of seed yield, and stem and root lodging susceptibilities due to its maximum prediction accuracy, as is suggested by the highest R2 values. It can be inferred that a UAV-based framework in combination with RFR could serve as a high-throughput technique for large-scale prediction of lodging susceptibility and seed yield, as early as at stem elongation stage and thus provides an opportunity for timely agronomic intervention.