发表机构
University of Bonn; Lamarr Institute for Machine Learning and Artificial Intelligence; CSIRO（波恩大学; 拉马尔机器学习与人工智能研究所; 联邦科学与工业研究组织）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对单株甜椒产量预测，提出融合图像特征与计数测量的多模态深度学习框架，利用LSTM网络处理时间依赖性和不规则采样，实验证明该方法能降低RMSE，提供校准不确定性估计，还发布数据集和代码助力相关研究。
AI 中文摘要
在精准农业和供应链规划中，单株水平的准确产量预测至关重要，但同时包含视觉生长动态和单株测量标签的公共数据集稀缺。本文引入一个新的注释图像时间序列数据集，涵盖691株甜椒在两个生长季节的情况，含4837张图像及按成熟度分类的单株果实计数。提出多模态深度学习框架，融合用DinoV3编码器提取的高维图像特征与数值计数测量。利用长短期记忆网络建模时间依赖性并处理温室监测中常见的不规则采样间隔。定量实验表明，该多模态方法在2022年和2023年季节分别比持久性基线降低RMSE 33%和38%，比仅测量模型平均增益1.2%。还采用深度集成和高斯负对数似然提供校准不确定性估计，不确定性校准误差在0.39至0.89之间。最后发布数据集和代码以支持可重复研究并加速园艺作物数据驱动产量预测方法的发展。
英文摘要
Accurate yield forecasting at the individual-plant level is critical for precision agriculture and supply-chain planning, yet public datasets capturing both visual growth dynamics and per-plant measurement labels are scarce. In this paper, we introduce a novel, annotated image time-series dataset of 691 sweet pepper plants monitored over two growing seasons, comprising 4837 images with per-plant fruit counts categorized by maturity. We propose a multimodal deep learning framework that fuses high-dimensional image features, extracted using the DinoV3 encoder, with numerical count measurements. Our architecture utilizes a Long Short-Term Memory (LSTM) network to model temporal dependencies and handles irregular sampling intervals common in greenhouse monitoring. Through quantitative experiments, we demonstrate that this multimodal approach reduces RMSE over a persistence baseline by 33% and 38% in the 2022 and 2023 seasons, respectively, with a further 1.2% average gain over a measurement-only model. Furthermore, we employ Deep Ensembles and Gaussian Negative Log-Likelihood (NLL) to provide calibrated uncertainty estimates, with an Uncertainty Calibration Error (UCE) ranging from 0.39 to 0.89 depending on the cross-season evaluation direction, offering a principled confidence signal for real-world agricultural decision-making. We release the dataset and code to support reproducible research and to accelerate development of data-driven yield forecasting methods for horticultural crops.
