Accurate reconstruction of plant phenotypes plays a key role in optimising
sustainable farming practices in the field of Precision Agriculture (PA).
Currently, optical sensor-based approaches dominate the field, but the need for
high-fidelity 3D reconstruction of crops and plants in unstructured
agricultural environments remains challenging. Recently, a promising
development has emerged in the form of Neural Radiance Field (NeRF), a novel
method that utilises neural density fields. This technique has shown impressive
performance in various novel vision synthesis tasks, but has remained
relatively unexplored in the agricultural context. In our study, we focus on
two fundamental tasks within plant phenotyping: (1) the synthesis of 2D
novel-view images and (2) the 3D reconstruction of crop and plant models. We
explore the world of neural radiance fields, in particular two SOTA methods:
Instant-NGP, which excels in generating high-quality images with impressive
training and inference speed, and Instant-NSR, which improves the reconstructed
geometry by incorporating the Signed Distance Function (SDF) during training.
In particular, we present a novel plant phenotype dataset comprising real plant
images from production environments. This dataset is a first-of-its-kind
initiative aimed at comprehensively exploring the advantages and limitations of
NeRF in agricultural contexts. Our experimental results show that NeRF
demonstrates commendable performance in the synthesis of novel-view images and
is able to achieve reconstruction results that are competitive with Reality
Capture, a leading commercial software for 3D Multi-View Stereo (MVS)-based
reconstruction. However, our study also highlights certain drawbacks of NeRF,
including relatively slow training speeds, performance limitations in cases of
insufficient sampling, and challenges in obtaining geometry quality in complex
setups.

精准农业领域的重要问题之一是植物表型的准确重建。最近，神经辐射场（NeRF）方法在 2D 新视图图像合成和 3D 农作物和植物模型重建方面表现出出色的性能，但在农业环境中尚未深入研究。本研究通过两种最先进的方法 Instant-NGP 和 Instant-NSR，探索了 NeRF 方法的优势和局限，并提出了一种新的植物表型数据集，以全面评估 NeRF 在农业上下文中的应用。实验结果显示，NeRF 方法在合成新视图图像和与 Reality Capture 相媲美的 3D 重建方面表现出可观的性能，但存在训练速度较慢、采样不足时的性能限制以及在复杂环境中获取几何质量的挑战等问题。

利用神经辐射场进行植物高保真度三维重建

High-fidelity 3D Reconstruction of Plants using Neural Radiance Field

The preservation of soil health has been identified as one of the main
challenges of the XXI century given its vast (and potentially threatening)
ramifications in agriculture, human health and biodiversity. Here, we provide
the first deep investigation of the predictive potential of machine-learning
models to understand the connections between soil and biological phenotypes.
Indeed, we investigate an integrative framework performing accurate
machine-learning-based prediction of plant phenotypes from biological, chemical
and physical properties of the soil via two models: random forest and Bayesian
neural network. We show that prediction is improved, as evidenced by higher
weighted F1 scores, when incorporating into the models environmental features
like soil physicochemical properties and microbial population density in
addition to the microbiome information. Furthermore, by exploring multiple data
preprocessing strategies such as normalization, zero replacement, and data
augmentation, we confirm that human decisions have a huge impact on the
predictive performance. In particular, we show that the naive total sum scaling
normalization that is commonly used in microbiome research is not the optimal
strategy to maximize predictive power. In addition, we find that accurately
defined labels are more important than normalization, taxonomic level or model
characteristics. That is, if humans are unable to classify the samples and
provide accurate labels, the performance of machine-learning models will be
limited. Lastly, we present strategies for domain scientists via a full model
selection decision tree to identify the human choices that maximize the
prediction power of the models. Our work is accompanied by open source
reproducible scripts (this https URL) for
maximum outreach among the microbiome research community.

本研究使用机器学习的方法，通过分析土壤生物、化学和物理性质以及微生物群落密度等环境特征，提高了对植物表型的预测准确度，并对数据预处理策略进行探讨，强调合理的标签定义比正态化、分类水平或模型特征更为关键。

机器学习中的人类限制：利用土壤微生物组数据预测植物表型

Human Limits in Machine Learning: Prediction of Plant Phenotypes Using  Soil Microbiome Data

Estimation of photometric plant phenotypes (e.g., hue, shine, chroma) in
field conditions is important for decisions on the expected yield quality,
fruit ripeness, and need for further breeding. Estimating these from images is
difficult due to large variances in lighting conditions, shadows, and sensor
properties. We combine the image and metadata regarding capturing conditions
embedded into a network, enabling more accurate estimation and transfer between
different conditions. Compared to a state-of-the-art deep CNN and a human
expert, metadata embedding improves the estimation of the tomato's hue and
chroma.

本文旨在探究基于图像和元数据嵌入网络的方法以提高作物表型特征的精确度，相对于深层卷积神经网络和人类专家，该方法在西红柿色调和色度的估计中表现更好。