Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management

Latest advances in unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNNs) allow us to detect crop lodging in a more precise and accurate way. However, the performance and generalization of a model capable of detecting lodging when the plants may show different spectral and morphological signatures have not been investigated much. This study investigated and compared the performance of models trained using aerial imagery collected at two growth stages of winter wheat with different canopy phenotypes. Specifically, three CNN-based models were trained with aerial imagery collected at early grain filling stage only, at physiological maturity only, and at both stages. Results show that the multi-stage model trained by images from both growth stages outperformed the models trained by images from individual growth stages on all testing data. The mean accuracy of the multi-stage model was 89.23% for both growth stages, while the mean of the other two models were 52.32% and 84.9%, respectively. This study demonstrates the importance of diversity of training data in big data analytics, and the feasibility of developing a universal decision support system for wheat lodging detection and mapping multi-growth stages with high-resolution remote sensing imagery.