UAV-based rice nitrogen status monitoring using machine learning and deep learning
UAV-based rice nitrogen status monitoring using machine learning and deep learning。Precision Agriculture
水稻
以水稻为对象的生产、育种、植保、机械化与产业内容
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《面向部署的中国南方水稻产量预测阶段特异性信息评估:跨年份、地点及地点-年份环境》
基于中国南方多环境水稻数据，评估机器学习产量预测在不同部署场景下的真实精度。
Wild rice kernel inspection using a prompt-guided vision language model with evidence-grounded reasoning
用提示引导的视觉语言模型对野生稻米粒进行检测，并给出基于证据的推理。
Leakage-Proof Reliability-Aware Deep Learning Framework for Rice Leaf Disease Diagnosis
提出防泄漏、可靠性导向的水稻叶病诊断深度学习框架，并系统评估其泛化与部署表现。
Knowledge-guided deep learning improves estimation of rice aboveground biomass
提出知识引导的深度学习模型，提高水稻地上生物量估算精度。
Deep learning technique for rice leaf disease classification and severity level identification via Hybrid ResConvolutional Neural Network
提出混合Res卷积神经网络用于水稻叶部病害分类与严重度识别，结合物联网数据采集。
From frame-wise detection to instance-level tracking: robust crop row perception for autonomous paddy field navigation
提出从逐帧检测到实例级跟踪的作物行感知方法，用于水稻田自主导航。
Rice seedling diagnosis of light and water stress based on green – blue channel feature enhancement
提出基于绿蓝通道特征增强的水稻幼苗光水胁迫诊断方法。