Abstract
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.
Highlights
Wheat is one of the most important food crops worldwide providing calories and protein for human consumption [1]
This study investigated the feasibility of developing a decision support system for wheat lodging detection at multiple growth stages with different canopy phenology
Our study suggests the importance of incorporating diversity into training data in the big data analytics and suggests the exploitation of the temporal data to enhance the data diversity for decision-making systems
Summary
Wheat is one of the most important food crops worldwide providing calories and protein for human consumption [1]. Various CNN models were proposed to detect and map crop lodging from the high-resolution UAV imagery [41]. The objective of this study was to investigate the importance of training data diversity on CNN-based lodging detection and mapping by comparing the performance of models trained and tested by different combinations of aerial imagery collected at two growth stages with different canopy phenotypes. We trained three CNN-based wheat lodging detection and mapping models with aerial imagery collected at early grain filling stage only, at physiological maturity only, and at both stages. These models were tested and compared for Agronomy.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
