NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications

This paper presents a NeRF-based framework for point cloud (PCD) reconstruction, specifically designed for indoor high-throughput plant phenotyping facilities. Traditional NeRF-based reconstruction methods require cameras to move around stationary objects, but this approach is impractical for high-throughput environments where objects are rapidly imaged while moving on conveyors or rotating pedestals. To address this limitation, we develop a variant of NeRF-based PCD reconstruction that uses a single stationary camera to capture images as the object rotates on a pedestal. Our workflow comprises COLMAP-based pose estimation, a straightforward pose transformation to simulate camera movement, and subsequent standard NeRF training. A defined Region of Interest (ROI) excludes irrelevant scene data, enabling the generation of high-resolution point clouds (10M points). Experimental results demonstrate excellent reconstruction fidelity, with precision-recall analyses yielding an F-score close to 100.00 across all evaluated plant objects. Although pose estimation remains computationally intensive with a stationary camera setup, overall training and reconstruction times are competitive, validating the method's feasibility for practical high-throughput indoor phenotyping applications. Our findings indicate that high-quality NeRF-based 3D reconstructions are achievable using a stationary camera, eliminating the need for complex camera motion or costly imaging equipment. This approach is especially beneficial when employing expensive and delicate instruments, such as hyperspectral cameras, for 3D plant phenotyping. Future work will focus on optimizing pose estimation techniques and further streamlining the methodology to facilitate seamless integration into automated, high-throughput 3D phenotyping pipelines.

3D Neural Operator-Based Flow Surrogates around 3D geometries: Signed Distance Functions and Derivative Constraints

Accurate modeling of fluid dynamics around complex geometries is critical for applications such as aerodynamic optimization and biomedical device design. While advancements in numerical methods and high-performance computing have improved simulation capabilities, the computational cost of high-fidelity 3D flow simulations remains a significant challenge. Scientific machine learning (SciML) offers an efficient alternative, enabling rapid and reliable flow predictions. In this study, we evaluate Deep Operator Networks (DeepONet) and Geometric-DeepONet, a variant that incorporates geometry information via signed distance functions (SDFs), on steady-state 3D flow over complex objects. Our dataset consists of 1,000 high-fidelity simulations spanning Reynolds numbers from 10 to 1,000, enabling comprehensive training and evaluation across a range of flow regimes. To assess model generalization, we test our models on a random and extrapolatory train-test splitting. Additionally, we explore a derivative-informed training strategy that augments standard loss functions with velocity gradient penalties and incompressibility constraints, improving physics consistency in 3D flow prediction. Our results show that Geometric-DeepONet improves boundary-layer accuracy by up to 32% compared to standard DeepONet. Moreover, incorporating derivative constraints enhances gradient accuracy by 25% in interpolation tasks and up to 45% in extrapolatory test scenarios, suggesting significant improvement in generalization capabilities to unseen 3D Reynolds numbers.

Accessing the Effect of Phyllotaxy and Planting Density on Light Use Efficiency in Field-Grown Maize using 3D Reconstructions

High-density planting is a widely adopted strategy to enhance maize productivity, yet it introduces challenges such as increased interplant competition and shading, which can limit light capture and overall yield potential. In response, some maize plants naturally reorient their canopies to optimize light capture, a process known as canopy reorientation. Understanding this adaptive response and its impact on light capture is crucial for maximizing agricultural yield potential. This study introduces an end-to-end framework that integrates realistic 3D reconstructions of field-grown maize with photosynthetically active radiation (PAR) modeling to assess the effects of phyllotaxy and planting density on light interception. In particular, using 3D point clouds derived from field data, virtual fields for a diverse set of maize genotypes were constructed and validated against field PAR measurements. Using this framework, we present detailed analyses of the impact of canopy orientations, plant and row spacings, and planting row directions on PAR interception throughout a typical growing season. Our findings highlight significant variations in light interception efficiency across different planting densities and canopy orientations. By elucidating the relationship between canopy architecture and light capture, this study offers valuable guidance for optimizing maize breeding and cultivation strategies across diverse agricultural settings.

AgriField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel

The application of artificial intelligence (AI) in three-dimensional (3D) agricultural research, particularly for maize, has been limited by the scarcity of large-scale, diverse datasets. While 2D image datasets are abundant, they fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present AgriField3D (https://baskargroup.github.io/AgriField3D/), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset comprises over 1,000 high-quality point clouds collected using a Terrestrial Laser Scanner, complemented by procedural models that provide structured, parametric representations of maize plants. These procedural models, generated using Non-Uniform Rational B-Splines (NURBS) and optimized via a two-step process combining Particle Swarm Optimization (PSO) and differentiable programming, enable precise, scalable reconstructions of leaf surfaces and plant architectures. To enhance usability, we performed graph-based segmentation to isolate individual leaves and stalks, ensuring consistent labeling across all samples. We also conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset further includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled versions (100k, 50k, 10k points) optimized for various computational needs. By integrating point cloud data of field grown plants with high-fidelity procedural models and ensuring meticulous manual validation, AgriField3D provides a comprehensive foundation for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research.

MaizeEar-SAM: Zero-Shot Maize Ear Phenotyping

Quantifying the variation in yield component traits of maize (Zea mays L.), which together determine the overall productivity of this globally important crop, plays a critical role in plant genetics research, plant breeding, and the development of improved farming practices. Grain yield per acre is calculated by multiplying the number of plants per acre, ears per plant, number of kernels per ear, and the average kernel weight. The number of kernels per ear is determined by the number of kernel rows per ear multiplied by the number of kernels per row. Traditional manual methods for measuring these two traits are time-consuming, limiting large-scale data collection. Recent automation efforts using image processing and deep learning encounter challenges such as high annotation costs and uncertain generalizability. We tackle these issues by exploring Large Vision Models for zero-shot, annotation-free maize kernel segmentation. By using an open-source large vision model, the Segment Anything Model (SAM), we segment individual kernels in RGB images of maize ears and apply a graph-based algorithm to calculate the number of kernels per row. Our approach successfully identifies the number of kernels per row across a wide range of maize ears, showing the potential of zero-shot learning with foundation vision models combined with image processing techniques to improve automation and reduce subjectivity in agronomic data collection. All our code is open-sourced to make these affordable phenotyping methods accessible to everyone.

Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries

Rapid yet accurate simulations of fluid dynamics around complex geometries is critical in a variety of engineering and scientific applications, including aerodynamics and biomedical flows. However, while scientific machine learning (SciML) has shown promise, most studies are constrained to simple geometries, leaving complex, real-world scenarios underexplored. This study addresses this gap by benchmarking diverse SciML models, including neural operators and vision transformer-based foundation models, for fluid flow prediction over intricate geometries. Using a high-fidelity dataset of steady-state flows across various geometries, we evaluate the impact of geometric representations -- Signed Distance Fields (SDF) and binary masks -- on model accuracy, scalability, and generalization. Central to this effort is the introduction of a novel, unified scoring framework that integrates metrics for global accuracy, boundary layer fidelity, and physical consistency to enable a robust, comparative evaluation of model performance. Our findings demonstrate that foundation models significantly outperform neural operators, particularly in data-limited scenarios, and that SDF representations yield superior results with sufficient training data. Despite these advancements, all models struggle with out-of-distribution generalization, highlighting a critical challenge for future SciML applications. By advancing both evaluation methodologies and modeling capabilities, this work paves the way for robust and scalable ML solutions for fluid dynamics across complex geometries.

STITCH: Surface reconstrucTion using Implicit neural representations with Topology Constraints and persistent Homology

We present STITCH, a novel approach for neural implicit surface reconstruction of a sparse and irregularly spaced point cloud while enforcing topological constraints (such as having a single connected component). We develop a new differentiable framework based on persistent homology to formulate topological loss terms that enforce the prior of a single 2-manifold object. Our method demonstrates excellent performance in preserving the topology of complex 3D geometries, evident through both visual and empirical comparisons. We supplement this with a theoretical analysis, and provably show that optimizing the loss with stochastic (sub)gradient descent leads to convergence and enables reconstructing shapes with a single connected component. Our approach showcases the integration of differentiable topological data analysis tools for implicit surface reconstruction.

Soybean Maturity Prediction using 2D Contour Plots from Drone based Time Series Imagery

Plant breeding programs require assessments of days to maturity for accurate selection and placement of entries in appropriate tests. In the early stages of the breeding pipeline, soybean breeding programs assign relative maturity ratings to experimental varieties that indicate their suitable maturity zones. Traditionally, the estimation of maturity value for breeding varieties has involved breeders manually inspecting fields and assessing maturity value visually. This approach relies heavily on rater judgment, making it subjective and time-consuming. This study aimed to develop a machine-learning model for evaluating soybean maturity using UAV-based time-series imagery. Images were captured at three-day intervals, beginning as the earliest varieties started maturing and continuing until the last varieties fully matured. The data collected for this experiment consisted of 22,043 plots collected across three years (2021 to 2023) and represent relative maturity groups 1.6 - 3.9. We utilized contour plot images extracted from the time-series UAV RGB imagery as input for a neural network model. This contour plot approach encoded the temporal and spatial variation within each plot into a single image. A deep learning model was trained to utilize this contour plot to predict maturity ratings. This model significantly improves accuracy and robustness, achieving up to 85% accuracy. We also evaluate the model's accuracy as we reduce the number of time points, quantifying the trade-off between temporal resolution and maturity prediction. The predictive model offers a scalable, objective, and efficient means of assessing crop maturity, enabling phenomics and ML approaches to reduce the reliance on manual inspection and subjective assessment. This approach enables the automatic prediction of relative maturity ratings in a breeding program, saving time and resources.

Robust soybean seed yield estimation using high-throughput ground robot videos

We present a novel method for soybean (Glycine max (L.) Merr.) yield estimation leveraging high throughput seed counting via computer vision and deep learning techniques. Traditional methods for collecting yield data are labor-intensive, costly, prone to equipment failures at critical data collection times, and require transportation of equipment across field sites. Computer vision, the field of teaching computers to interpret visual data, allows us to extract detailed yield information directly from images. By treating it as a computer vision task, we report a more efficient alternative, employing a ground robot equipped with fisheye cameras to capture comprehensive videos of soybean plots from which images are extracted in a variety of development programs. These images are processed through the P2PNet-Yield model, a deep learning framework where we combined a Feature Extraction Module (the backbone of the P2PNet-Soy) and a Yield Regression Module to estimate seed yields of soybean plots. Our results are built on three years of yield testing plot data - 8500 in 2021, 2275 in 2022, and 650 in 2023. With these datasets, our approach incorporates several innovations to further improve the accuracy and generalizability of the seed counting and yield estimation architecture, such as the fisheye image correction and data augmentation with random sensor effects. The P2PNet-Yield model achieved a genotype ranking accuracy score of up to 83%. It demonstrates up to a 32% reduction in time to collect yield data as well as costs associated with traditional yield estimation, offering a scalable solution for breeding programs and agricultural productivity enhancement.

Disentangling Genotype and Environment Specific Latent Features for Improved Trait Prediction using a Compositional Autoencoder

This study introduces a compositional autoencoder (CAE) framework designed to disentangle the complex interplay between genotypic and environmental factors in high-dimensional phenotype data to improve trait prediction in plant breeding and genetics programs. Traditional predictive methods, which use compact representations of high-dimensional data through handcrafted features or latent features like PCA or more recently autoencoders, do not separate genotype-specific and environment-specific factors. We hypothesize that disentangling these features into genotype-specific and environment-specific components can enhance predictive models. To test this, we developed a compositional autoencoder (CAE) that decomposes high-dimensional data into distinct genotype-specific and environment-specific latent features. Our CAE framework employs a hierarchical architecture within an autoencoder to effectively separate these entangled latent features. Applied to a maize diversity panel dataset, the CAE demonstrates superior modeling of environmental influences and 5-10 times improved predictive performance for key traits like Days to Pollen and Yield, compared to the traditional methods, including standard autoencoders, PCA with regression, and Partial Least Squares Regression (PLSR). By disentangling latent features, the CAE provides powerful tool for precision breeding and genetic research. This work significantly enhances trait prediction models, advancing agricultural and biological sciences.