Automatic grading evaluation of winter wheat lodging based on deep learning.

Lodging is a crucial factor that limits wheat yield and quality in wheat breeding. Therefore, accurate and timely determination of winter wheat lodging grading is of great practical importance for agricultural insurance companies to assess agricultural losses and good seed selection. However, using artificial fields to investigate the inclination angle and lodging area of winter wheat lodging in actual production is time-consuming, laborious, subjective, and unreliable in measuring results. This study addresses these issues by designing a classification-semantic segmentation multitasking neural network model MLP_U-Net, which can accurately estimate the inclination angle and lodging area of winter wheat lodging. This model can also comprehensively, qualitatively, and quantitatively evaluate the grading of winter wheat lodging. The model is based on U-Net architecture and improves the shift MLP module structure to achieve network refinement and segmentation for complex tasks. The model utilizes a common encoder to enhance its robustness, improve classification accuracy, and strengthen the segmentation network, considering the correlation between lodging degree and lodging area parameters. This study used 82 winter wheat varieties sourced from the regional experiment of national winter wheat in the Huang-Huai-Hai southern area of the water land group at the Henan Modern Agriculture Research and Development Base. The base is located in Xinxiang City, Henan Province. Winter wheat lodging images were collected using the unmanned aerial vehicle (UAV) remote sensing platform. Based on these images, winter wheat lodging datasets were created using different time sequences and different UAV flight heights. These datasets aid in segmenting and classifying winter wheat lodging degrees and areas. The results show that MLP_U-Net has demonstrated superior detection performance in a small sample dataset. The accuracies of winter wheat lodging degree and lodging area grading were 96.1% and 92.2%, respectively, when the UAV flight height was 30 m. For a UAV flight height of 50 m, the accuracies of winter wheat lodging degree and lodging area grading were 84.1% and 84.7%, respectively. These findings indicate that MLP_U-Net is highly robust and efficient in accurately completing the winter wheat lodging-grading task. This valuable insight provides technical references for UAV remote sensing of winter wheat disaster severity and the assessment of losses.

The Left Amygdala and Right Frontoparietal Cortex Support Emotional Adaptation Aftereffects.

Adaptation aftereffects-in which prolonged prior experience (adaptation) can bias the subsequent judgment of ambiguous stimuli-are a ubiquitous phenomenon. Numerous studies have found behaviorally stable adaptation aftereffects in a variety of areas. However, it is unclear which brain regions are responsible for this function, particularly in the case of high-level emotional adaptation aftereffects. To address this question, the present study used fMRI technology to investigate the neural mechanism of emotional adaptation aftereffects. Consistent with previous studies, we observed typical emotional adaptation effects in behavior. Specifically, for the same morphed facial images, participants perceived increased sadness after adapting to a happy facial image and increased happiness after adapting to a sad facial image. More crucially, by contrasting neural responses to ambiguous morphed facial images (i.e., facial images of intermediate morph levels) following adaptation to happy and sad expressions, we demonstrated a neural mechanism of emotional aftereffects supported by the left amygdala/insula, right angular gyrus, and right inferior frontal gyrus. These results suggest that the aftereffects of emotional adaptation are supported not only by brain regions subserving emotional processing but also by those subserving cognitive control.

Neural similarities and differences between native and second languages in the bilateral fusiform cortex in Chinese-English bilinguals.

In the field of bilingualism, researchers have proposed an assimilation hypothesis that posits that bilinguals apply the neural network of their native language to process their second language. In Chinese-English bilinguals, the bilateral fusiform gyrus has been identified as the key brain region showing the assimilation process. Specifically, in contrast to left-lateralized activation in the fusiform gyrus in native English speakers, Chinese-English bilinguals recruit the bilateral fusiform cortex to process English words as they do in the processing of Chinese characters. Nevertheless, it is unclear which type of information processing is assimilated in the fusiform gyrus. Using representational similarity analysis (RSA) and psychophysiological interaction (PPI) analysis, this study examined the differences in information representation and functional connectivity between both languages in the fusiform subregions in Chinese-English bilinguals. Univariate analysis revealed that both Chinese and English naming elicited strong activations in the bilateral fusiform gyrus, which confirmed the assimilation process at the activation intensity level. RSA indicated that the neural pattern of English phonological information was assimilated by Chinese in the anterior and middle right fusiform gyrus, while those of orthographic and visual form information were not. Further PPI analysis demonstrated that the neural representation of English phonological information in the right anterior fusiform subregion was related to its interaction with the frontotemporal areas for high-level linguistic processing, while the neural representation of English orthographic information in the right middle fusiform subregion was linked to its interaction with the left inferior occipital cortex for visual processing. These results suggest that, despite the recruitment of similar neural resources in one's native and second languages, the assimilation of information representation is limited in the bilateral fusiform cortex. Our results shed light on the neural mechanisms of second language processing.

Individuals' preference on reading pathways influences the involvement of neural pathways in phonological learning.

Introduction: Existing behavioral and neuroimaging studies revealed inter-individual variability in the selection of the two phonological routes in word reading. However, it is not clear how individuals' preferred reading pathways/strategies modulate the involvement of a certain brain region for phonological learning in a new language, and consequently affect their behavioral performance on phonological access. Methods: To address this question, the present study recruited a group of native Chinese speakers to learn two sets of artificial language characters, respectively, in addressed-phonology training (i.e., whole-word mapping) and assembled-phonology training conditions (i.e., grapheme-to-phoneme mapping). Results: Behavioral results showed that the more lexical pathways participants preferred, the better they performed on newly-acquired addressed characters relative to assembled characters. More importantly, neuroimaging results showed that participants who preferred lexical pathway in phonological access show less involvement of brain regions for addressed phonology (e.g., the bilateral orbitofrontal cortex and right pars triangularis) in the processing of newly-acquired addressed characters. Conclusion: These results indicated that phonological access via the preferred pathway required less neural resources to achieve better behavioral performance. These above results provide direct neuroimaging evidence for the influence of reading pathway preference on phonological learning.