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Remote sensing systems based on consumer-grade cameras have been increasingly used in scientific research and remote sensing applications because of their low cost and ease of use. However, the performance of consumer-grade cameras for practical applications has not been well documented in related studies. The objective of this research was to apply three commonly-used classification methods (unsupervised, supervised, and object-based) to three-band imagery with RGB (red, green, and blue bands) and four-band imagery with RGB and near-infrared (NIR) bands to evaluate the performance of a dual-camera imaging system for crop identification. Airborne images were acquired from a cropping area in Texas and mosaicked and georeferenced. The mosaicked imagery was classified using the three classification methods to assess the usefulness of NIR imagery for crop identification and to evaluate performance differences between the object-based and pixel-based methods. Image classification and accuracy assessment showed that the additional NIR band imagery improved crop classification accuracy over the RGB imagery and that the object-based method achieved better results with additional non-spectral image features. The results from this study indicate that the airborne imaging system based on two consumer-grade cameras used in this study can be useful for crop identification and other agricultural applications.

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Age-related alterations of functional brain networks contribute to cognitive decline. Current theories indicate that age-related intrinsic brain functional reorganization may be a critical marker of cognitive aging. Yet, little is known about how intrinsic interhemispheric functional connectivity changes with age in adults, and how this relates to critical executive functions. To address this, we examined voxel-mirrored homotopic connectivity (VMHC), a metric that quantifies interhemispheric communication, in 93 healthy volunteers (age range: 19-85) with executive function assessment using the Delis-Kaplan Executive Function System (D-KEFS) scales. Resting functional MRI data were analyzed to assess VMHC, and then a multiple linear regression model was employed to evaluate the relationship between age and the whole-brain VMHC. We observed age-related reductions in VMHC of ventromedial prefrontal cortex (vmPFC) and hippocampus in the medial temporal lobe subsystem, dorsal anterior cingulate cortex and insula in salience network, and inferior parietal lobule in frontoparietal control network. Performance on the color-word inhibition task was associated with VMHC of vmPFC and insula, and VMHC of vmPFC mediated the relationship between age and CWIT inhibition reaction times. The percent ratio of correct design scores in design fluency test correlated positively with VMHC of the inferior parietal lobule. The current study suggests that brain interhemispheric functional alterations may be a promising new avenue for understanding age-related cognitive decline.

Eating behaviors are closely related to body weight, and eating traits are depicted in three dimensions: dietary restraint, disinhibition, and hunger. The current study aims to explore whether these aspects of eating behaviors are related to intrinsic brain activation, and to further investigate the relationship between the brain activation relating to these eating traits and body weight, as well as the link between function connectivity (FC) of the correlative brain regions and body weight. Our results demonstrated positive associations between dietary restraint and baseline activation of the frontal and the temporal regions (i.e., food reward encoding) and the limbic regions (i.e., homeostatic control, including the hypothalamus). Disinhibition was positively associated with the activation of the frontal motivational system (i.e., OFC) and the premotor cortex. Hunger was positively related to extensive activations in the prefrontal, temporal, and limbic, as well as in the cerebellum. Within the brain regions relating to dietary restraint, weight status was negatively correlated with FC of the left middle temporal gyrus and left inferior temporal gyrus, and was positively associated with the FC of regions in the anterior temporal gyrus and fusiform visual cortex. Weight status was positively associated with the FC within regions in the prefrontal motor cortex and the right ACC serving inhibition, and was negatively related with the FC of regions in the frontal cortical-basal ganglia-thalamic circuits responding to hunger control. Our data depicted an association between intrinsic brain activation and dietary restraint, disinhibition, and hunger, and presented the links of their activations and FCs with weight status.

The automatic intelligent acquisition of apple growth information in the long-term provides a promising benefit for growers to plan the application of nutrients and pesticides during apple maturation. The overall goal of this study was to develop an apple growth monitoring system in an orchard based on a deep learning edge detection network for apple size remote estimation throughout the entire growth period. A remote apple growth monitoring hardware system was built with a spherical video camera and two personal computers to regularly acquire apple images. For software, an edge detection network that fused convolutional features (FCF) was proposed to segment the apple images. To filter out irrelevant apples in the images, points on apples to be monitored were manually selected from the images as seed points, and the region growing method was conducted on the extracted edge maps. Then, the horizontal diameters of the apples were calculated. The experimental results showed that the F1 score of the FCF method was 53.1% on the apple test set, and the average run time was 0.075 s per image, which was better than the other five methods in comparison. The growth of the apples was monitored by our system from the date after apple thinning to apple ripening. The mean average absolute error of the apples' horizontal diameters detected by our system was 0.90 mm, and it decreased by 67.9% when compared with the circle fitting-based method (2.8 mm). These results suggest that our system provides an effective and accurate way to monitor the growth of apples on the trees. The proposed method provides a reference for monitoring the growth of other fruits during the growth period, and it can be used to optimize orchard management.

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.

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