In precision agriculture and plant biology, monitoring nutrient stress in crops is of paramount importance for ensuring optimal yield and resource utilization. However, nutrient stress phenotypes can be nuanced, subtle, and display in a variety of ways. We propose using Neural Radiance Fields (NeRFs) for the organized reconstruction of plant structures to observe the changes of plant structure and color under nutrient stress.Neural Radiance Fields, a cutting-edge technique in computer vision, leverage neural networks to model complex high-frequency geometry directly from 2D images, offering high-fidelity reconstructions. This methodology holds immense potential for plant imaging, as it allows for the creation of detailed and organized 3D models that can capture subtle alterations in plant morphology associated with nutrient stress responses.The proposed methodology involves the acquisition of high-resolution images of plants under different nutrient conditions. These images are inputted to the NeRFStudio Nerfacto implementation, a NeRF model that is a aggregation of many different existing models. A 3D reconstruction of the scene is outputted from the model and can be further reduced to a point cloud containing point locations, colors, and normals. Phenotypic traits are then calculated from the point clouds. The reconstructed plant models enable the quantitative analysis of morphological changes associated with nutrient stress. This includes alterations in leaf size, branching patterns, and overall plant geometry. The utilization of NeRFs allows for non-destructive monitoring, offering a significant advantage over traditional methods that may be labor-intensive or invasive.This research not only contributes to the field of precision agriculture but also presents a powerful tool for plant biologists to deepen their understanding of how nutrient stress impacts plant architecture. The insights gained from this approach have the potential to inform precision nutrient management strategies, leading to more sustainable and efficient agricultural practices.

(250 words) Micronutrients, such as iron, zinc, and sulfur, play a vital role in both plant and human development. Understanding how plants sense and allocate nutrients within their tissues may offer different venues to develop plants with high nutritional value. Despite decades of intensive research, more than 40% of genes in Arabidopsis remain uncharacterized or have no assigned function. While several resources such as mutant populations or diversity panels offer the possibility to identify genes critical for plant nutrition, the ability to consistently track and assess plant growth in an automated, unbiased way is still a major limitation. High-throughput phenotyping (HTP) is the new standard in plant biology but few HTP systems are open source and user friendly. Therefore, we developed OPEN Leaf, an open source HTP for hydroponic experiments. OPEN Leaf is capable of tracking changes in both size and color of the whole plant and specific regions of the rosette. We have also integrated communication platforms (Slack) and cloud services (CyVerse) to facilitate user communication, collaboration, data storage, and analysis in real time. As a proof-of-concept, we report the ability of OPEN Leaf to track changes in size and color when plants are growing hydroponically with different levels of nutrients. We expect that the availability of open source HTP platforms, together with standardized experimental conditions agreed by the scientific community, will advance the identification of genes and networks mediating nutrient uptake and allocation in plants.

Predictive biology is the ability to predict a biological outcome from known inputs (and vice versa). Complex and urgent problems, such as climate change and a growing global population, requires a better grasp of predictive biology approaches. However, predictive biology requires a deep understanding of the genome-to-phenome relationship within an organism. The field of genomics has accelerated rapidly in the last few decades with technological advances that have helped reduced the costs of genomics research and easy-to-use computational tools. Phenomics technologies has not advanced at the same rate. Many phenotyping systems are expensive to own, require special training to use, focus on narrow areas of research, and produce large amounts of data with no standards of storage and documentation. We propose a design philosophy for high-throughput phenotyping systems called the OPEN Series. This philosophy focuses on systems that use off-the-shelf commercial products and open-source software to make high quality phenotyping systems efficiently and for use by general users. In addition, the OPEN Series focuses on integrating cloud-based image processing through the NSF-funded cyberinfrastructure CyVerse, thus allowing users to share and process data remotely. We've worked to integrate this philosophy into our own phenotyping systems, OPEN leaf and OPEN root, to great success. We hope to export our work in creating accessible and affordable phenotyping system to labs across the globe to accelerate our understanding of the genome-to-phenome relationship for predictive biology.