Abstract
Accurate production management decisions for soybeans are crucial for increasing yield and improving harvest quality. However, the complex canopy structure of soybean plants, characterized by mutual shading, complicates the accurate acquisition of phenotypic data. Additionally, soybean growth is intricately linked to environmental factors, which are increasingly variable due to climate change. The diversity of production monitoring data, coupled with varied and complex scenarios across different regions, poses significant challenges for modeling soybean production management and decision-making. To address these challenges, this project leverages a collaborative effort between Chinese and Brazilian research teams, focuses on utilizing multi-modal data gathered during the soybean production process and employs 3D reconstruction, phenotypic analysis, multi-layer complex network modeling, and domain-adaptive learning as its theoretical and technical foundation. The research aims to develop key technologies that leverage machine learning and multi-modal data fusion to enhance soybean yield and improve harvest quality. The main tasks include: 1) Developing methods for high-quality 3D reconstruction of soybean plants using sparse multi-view images to achieve precise structural modeling; 2) Advancing phenotyping technology for soybean plants based on multi-modal data such as images, point clouds, and spectral information to enhance phenotype accuracy; 3) Creating an intelligent soybean management decision model that integrates multi-modal data fusion to optimize production management; and 4) Designing domain-adaptive models for yield prediction and plant analysis to improve the generalization of management strategies across diverse environments. By integrating machine learning with multi-modal data fusion, this this Brazil-China collaborative project aims to elevate soybean production management and provide valuable insights into the application of these technologies. (AU)
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