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Artificial General Intelligence (AGI) is poised to revolutionize a variety of sectors, including healthcare, finance, transportation, and education. Within healthcare, AGI is being utilized to analyze clinical medical notes, recognize patterns in patient data, and aid in patient management. Agriculture is another critical sector that impacts the lives of individuals worldwide. It serves as a foundation for providing food, fiber, and fuel, yet faces several challenges, such as climate change, soil degradation, water scarcity, and food security. AGI has the potential to tackle these issues by enhancing crop yields, reducing waste, and promoting sustainable farming practices. It can also help farmers make informed decisions by leveraging real-time data, leading to more efficient and effective farm management. This paper delves into the potential future applications of AGI in agriculture, such as agriculture image processing, natural language processing (NLP), robotics, knowledge graphs, and infrastructure, and their impact on precision livestock and precision crops. By leveraging the power of AGI, these emerging technologies can provide farmers with actionable insights, allowing for optimized decision-making and increased productivity. The transformative potential of AGI in agriculture is vast, and this paper aims to highlight its potential to revolutionize the industry. 

Large-scale in-situ 3D reconstruction of crop fields presents a challenging task, as the 3D crop structures play a crucial role in plant phenotyping and significantly influence crop growth and yield. While existing efforts focus on close range plants, only a limited number of deep learning-based methods have been developed explicitly for large-scale 3D crop reconstruction, mainly due to the scarcity of large-scale crop sensing data. In this paper, we leverage unmanned aerial vehicles (UAVs) in agriculture and utilize a recently captured multiview real-world snap beans crop dataset to develop an unsupervised structure-from-motion (SfM) framework. Our framework is designed specifically for reconstructing large-scale 3D crop structures. It addresses the challenge of inaccurate depth inference caused by excessively repeated patterns in the crop dataset, resulting in highly accurate 3D crop reconstruction for large-scale scenarios. Through experiments conducted on the crop dataset, we demonstrate the accuracy and robustness of our 3D crop reconstruction algorithm. The application of our proposed framework has the potential to advance research in agriculture, enabling better plant phenotyping and understanding of crop growth and yield. 

Unsupervised visual odometry as an active topic has attracted extensive attention, benefiting from its label free practical value and robustness in real-world scenarios. However, the performance of camera pose estimation and tracking through deep neural network is still not as ideal as most other tasks, such as detection, segmentation and depth estimation, due to the lack of drift correction in the estimated trajectory and map optimization in the recovered 3D scenes. In this work, we introduce pose graph and bundle adjustment optimization to our network training process, which iteratively updates both the motion and depth estimations from the deep learning network, and enforces the refined outputs to further meet the unsupervised photometric and geometric constraints. The integration of pose graph and bundle adjustment is easy to implement and significantly enhances the training effectiveness. Experiments on KITTI dataset demonstrate that the introduced method achieves a significant improvement in motion estimation compared with other recent unsupervised monocular visual odometry algorithms. 

Single image depth estimation is a critical issue for robot vision, augmented reality, and many other applications when an image sequence is not available. Self-supervised single image depth estimation models target at predicting accurate disparity map just from one single image without ground truth supervision or stereo image pair during real applications. Compared with direct single image depth estimation, single image stereo algorithm can generate the depth from different camera perspectives. In this paper, we propose a novel architecture to infer accurate disparity by leveraging both spectral-consistency based learning model and view-prediction based stereo reconstruction algorithm. Direct spectral-consistency based method can avoid false positive matching in smooth regions. Single image stereo can preserve more distinct boundaries from another camera perspective. By learning confidence maps and designing a fusion strategy, the two disparities from the two approaches are able to be effectively fused to produce the refined disparity. Extensive experiments and ablations indicate that our method exploits both advantages of spectral consistency and view prediction, especially in constraining object boundaries and correcting wrong predicting regions.