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English(EN) Forecasting the Number of Harvest-ready Fruits of Sweet Peppers Using Multimodal Time-Series Data

AI模型利用多模态数据预测甜椒产量

研究人员开发了一个新的多模态深度学习框架,用于预测单株植物可收获的甜椒数量。该系统结合了由DinoV3编码器处理的视觉数据和数值果实计数测量,并利用长短期记忆(LSTM)网络来捕捉时间模式。实验表明,与基线模型相比,均方根误差(RMSE)显著降低,并且该框架还使用深度集成(Deep Ensembles)和高斯负对数似然(Gaussian Negative Log-Likelihood)提供了校准的不确定性估计。 AI

影响 通过实现更准确的、单株植物级别的产量预测,增强了精准农业。

排序理由 学术论文,详细介绍了用于农业产量预测的新型多模态深度学习框架。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

AI模型利用多模态数据预测甜椒产量

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学术论文,详细介绍了用于农业产量预测的新型多模态深度学习框架。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Enrico Pallotta, Mohamed Farag, Esra Guclu, Chris McCool, Ribana Roscher, Juergen Gall ·

    利用多模态时间序列数据预测甜椒可收获果实数量

    arXiv:2607.19975v1 Announce Type: new Abstract: 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…