Abstract
Crop leaf purpling is a common phenotypic change when plants are subject to some biotic and abiotic stresses during their growth. The extraction of purple leaves can monitor crop stresses as an apparent trait and meanwhile contributes to crop phenotype analysis, monitoring, and yield estimation. Due to the complexity of the field environment as well as differences in size, shape, texture, and color gradation among the leaves, purple leaf segmentation is difficult. In this study, we used a U-Net model for segmenting purple rapeseed leaves during the seedling stage based on unmanned aerial vehicle (UAV) RGB imagery at the pixel level. With the limited spatial resolution of rapeseed images acquired by UAV and small object size, the input patch size was carefully selected. Experiments showed that the U-Net model with the patch size of 256 × 256 pixels obtained better and more stable results with a F-measure of 90.29% and an Intersection of Union (IoU) of 82.41%. To further explore the influence of image spatial resolution, we evaluated the performance of the U-Net model with different image resolutions and patch sizes. The U-Net model performed better compared with four other commonly used image segmentation approaches comprising support vector machine, random forest, HSeg, and SegNet. Moreover, regression analysis was performed between the purple rapeseed leaf ratios and the measured N content. The negative exponential model had a coefficient of determination (R²) of 0.858, thereby explaining much of the rapeseed leaf purpling in this study. This purple leaf phenotype could be an auxiliary means for monitoring crop growth status so that crops could be managed in a timely and effective manner when nitrogen stress occurs. Results demonstrate that the U-Net model is a robust method for purple rapeseed leaf segmentation and that the accurate segmentation of purple leaves provides a new method for crop nitrogen stress monitoring.
Highlights
Biotic and abiotic stresses such as plant diseases, low temperature, and deficiencies of mineral elements can greatly affect the crop production and yield [1,2]
Pixel-wise plant segmentation based on unmanned aerial vehicle (UAV) images is a big challenge due to limited spatial resolution, small object size, and complex background features, compared with images obtained from the ground platform
The appearance of some ground objects in region c interfered with the segmentation processes by support vector machine (SVM) aRnedmoRteFS.enIsn. 2r0e2g0,io12n, 1d4,03when the purple leaves were close to the green leaves, random forest (RF) and SVM tende9dofto17 misclassify the mixed pixels at the boundary of the purple leaf
Summary
Biotic and abiotic stresses such as plant diseases, low temperature, and deficiencies of mineral elements can greatly affect the crop production and yield [1,2]. Plant segmentation using images based on the color space is a traditional target segmentation method [8]. Milioto et al proposed an end-to-end encoder-decoder network, which can accurately perform the pixel-wise prediction task for real-time semantic segmentation of crop and weed [26]. Pixel-wise plant segmentation based on UAV images is a big challenge due to limited spatial resolution, small object size, and complex background features, compared with images obtained from the ground platform. We used a U-Net convolutional neural network architecture as a binary semantic segmentation task of purple rapeseed leaves during the seedling stage based on UAV imagery. The U-Net model has shown significant outperformance over other deep architectures in some segmentation tasks, which is more suitable for target segmentation with uncertain size, small resolution, and complicated.
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