The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models.

The intertwined history of artificial intelligence and neuroscience has significantly impacted their development, with AI arising from and evolving alongside neuroscience. The remarkable performance of deep learning has inspired neuroscientists to investigate and utilize artificial neural networks as computational models to address biological issues. Studying the brain and its operational mechanisms can greatly enhance our understanding of neural networks, which has crucial implications for developing efficient AI algorithms. Many of the advanced perceptual and cognitive skills of biological systems are now possible to achieve through artificial intelligence systems, which is transforming our knowledge of brain function. Thus, the need for collaboration between the two disciplines demands emphasis. It's both intriguing and challenging to study the brain using computer science approaches, and this dissertation centers on exploring computational mechanisms related to face perception. Face recognition, being the most active artificial intelligence research area, offers a wealth of data resources as well as a mature algorithm framework. From the perspective of neuroscience, face recognition is an important indicator of social cognitive formation and neural development. The ability to recognize faces is one of the most important cognitive functions. We first discuss the problem of how the brain encodes different face identities. By using DNNs to extract features from complex natural face images and project them into the feature space constructed by dimension reduction, we reveal a new face code in the human medial temporal lobe (MTL), where neurons encode visually similar identities. On this basis, we discover a subset of DNN units that are selective for facial identity. These identity-selective units exhibit a general ability to discriminate novel faces. By establishing coding similarities with real primate neurons, our study provides an important approach to understanding primate facial coding. Lastly, we discuss the impact of face learning during the critical period. We identify a critical period during DNN training and systematically discuss the use of facial information by the neural network both inside and outside the critical period. We further provide a computational explanation for the critical period influencing face learning through learning rate changes. In addition, we show an alternative method to partially recover the model outside the critical period by knowledge refinement and attention shifting. Our current research not only highlights the importance of training orientation and visual experience in shaping neural responses to face features and reveals potential mechanisms for face recognition but also provides a practical set of