农业信息成像感知与深度学习应用研究进展

农业信息感知与准确的数据分析是智慧农业定量决策与管理服务的基础。现代农业中彩色、可见光-近红外光谱、3D与热红外等多源和多维度的成像感知手段提供了丰富的数据源,传统研究中围绕颜色、形态、纹理、反射光谱等特征展开分析,由于样本量和特征抽象层级的局限性,对复杂背景变化及未知样本检测时,还存在噪声抑制鲁棒性不足、识别与检测模型精度不高等问题。深度学习(Deep learning,DL)是机器学习的分支之一,结合神经网络通过组合底层特征形成抽象的高层表示属性类别或特征,以发现数据的分布式特征与属性,在图像目标识别与检测中其模型检测精度与泛化能力比传统方法均有所提升。因而,DL技术在农业信息检测中的应用日益增多。为了深入分析应用DL技术驱动智慧农业继续发展的潜力和方向,本文从农业信息成像感知的数据源与DL技术应用相结合的角度出发,分别以植物识别与检测、病虫害诊断与识别、遥感区域分类与监测、果实在体检测与产品分级、动物识别与姿态检测5个研究方向总结概括DL在农业信息检测中最新的应用研究成果,展望需要加强的方面,以提升对应用DL开展农业信息检测过程的理解,促进农业信息感知技术的发展。

Research Progress of Image Sensing and Deep Learning in Agriculture

Accurate data sensing and processing are basic of quantitative decision-making in smart agriculture management. Image sensing provide multi-dimensional information for agriculture detection, such as color, visible-near infrared spectroscopy, 3D and thermal radiation. The traditional way to analyze these images focuses on the characteristics of color, morphology, texture, spectral reflection and so on. The limitations of sample mounts and extracted features always lead to the problems such as insufficient noise reduction and low accuracy of the recognition and detection models, especially for complex background changes and unknown samples. Deep learning (DL), a subset of machine learning approaches, emerged and combined neural networks to extract and represent the high-level features of image. It provided a versatile tool to assimilate and explore distribution and features from heterogeneous data. It could help to build reliable predictions of complex and uncertain phenomena in agriculture. In order to explain the application potential and further direction, the applied sensors, specific models and dataset sources were examined from five areas, including plant recognition and detection, disease and pest identification, remote sensing classification and monitoring, products detection and grading, and animal detection. Finally, several avenues of researches were outlined.