Plant architecture is an important determinant of crop performance and agro-ecological adaptation. In this study, a field experiment was carried out during the 2008-2009 season to study the architecture of two winter wheat cultivars in situ with a 3-D digitizer at the grain filling stage. The parameters describing plant architecture were analyzed and 3-D light capture and potential carbon gain of the plant were calculated. Simulation results showed that light capture and potential carbon gain were more uniformly distributed for the cultivar with more erect leaves. At the grain filling stage, the photosynthetic contributions were about 55%, 49% and 30% for flag leaf, ear, and stem relative to total leaf photosynthesis.
Leaf age is an important trait in the process of maize (Zea mays L.) growth. It is significant to estimate the seed activity and yield of maize by counting leaves. Detection and counting of the maize leaves in the field are very difficult due to the complexity of the field scenes and the cross-covering of adjacent seedling leaves. A method was proposed in this study for detecting and counting maize leaves based on deep learning with RGB images collected by unmanned aerial vehicles (UAVs). The Mask R-CNN was used to separate the complete maize seedlings from the complex background to reduce the impact of weeds on leaf counting. We proposed a new loss function SmoothLR for Mask R-CNN to improve the segmentation performance of the model. Then, YOLOv5 was used to detect and count the individual leaves of maize seedlings after segmentation. The 1005 field seedlings images were randomly divided into the training, validation, and test set with the ratio of 7:2:1. The results showed that the segmentation performance of Mask R-CNN with Resnet50 and SmoothLR was better than that with LI Loss. The average precision of the bounding box (Bbox) and mask (Mask) was 96.9% and 95.2%, respectively. The inference time of single image detection and segmentation was 0.05 s and 0.07 s, respectively. YOLOv5 performed better in leaf detection compared with Faster R-CNN and SSD. YOLOv5x with the largest parameter had the best detection performance. The detection precision of fully unfolded leaves and newly appeared leaves was 92.0% and 68.8%, and the recall rates were 84.4% and 50.0%, respectively. The average precision (AP) was 89.6% and 54.0%, respectively. The rates of counting accuracy for newly appeared leaves and fully unfolded leaves were 75.3% and 72.9%, respectively. The experimental results showed the possibility of current research on exploring leaf counting for field-grown crops based on UAV images.
The three-dimensional (3-D) radiation distribution model in plant canopy is pivotal for understanding and modelling plant eco-physiological processes. Diffuse and direct radiations penetrate into plant canopies in different ways and may present different intensity and wavelength composition. Sunfleck (the canopy surfaces where the direct radiation reaches) distribution in the plant canopy is usually regarded as an important index for crop development, especially under dense canopy conditions. Distributions of direct and diffuse components of photosynthetically active radiation (PAR) in maize (Zea mays L.) canopies were estimated respectively using a 3-D incident radiation model (3DIRM). The 3DIRM model was set up for computing incident radiation in crop canopies by applying a parallel-projection based submodel for direct solar radiation and a central-projection based submodel for incident diffuse radiation simulation in crop canopy. It was well assessed with a field experiment with multi-point PAR measurement in maize canopies with relative errors of 2.6, 4.5 and 2.6%, respectively, for sunfleck area ratio, diffuse PAR and total PAR. The results suggest that the 3DIRM model could be used to estimate the direct, diffuse and total PAR at any specific surface part in the 3-D canopy space. The exponential distinction model for direct, diffuse and total PAR along with leaf area index in different heights in maize canopies was also evaluated based on the 3DIRM simulation results.
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