基于mRMR-XGBoost的水稻关键生育期识别  

Rice key growth stage identification based on mRMR-XGBoost

作　　者：冯健昭[1,2] 潘永琪 熊悦淞 吴彻 肖德琴[1,2] FENG Jianzhao;PAN Yongqi;XIONG Yuesong;WU Che;XIAO Deqin(College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China;Key Laboratory of Smart Agricultural Technology in Tropical South China,Ministry of Agriculture and Rural Affairs,South China Agricultural University,Guangzhou 510642,China;Guangzhou South China Agricultural University Smart Agriculture Technology Co.,Ltd.,Guangzhou 510642,China)

机构地区：[1]华南农业大学数学与信息学院,广州510642 [2]华南农业大学农业农村部华南热带智慧农业技术重点实验室,广州510642 [3]广州华农大智慧农业科技有限公司,广州510642

出　　处：《农业工程学报》2024年第15期111-118,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(U23A20174);广州市科技计划项目(202206010116);广东省重点领域研发计划项目(2019B020214002)。

摘　　要：针对目前使用无人机识别水稻关键生育期时光谱数据特征维度高和冗余,导致模型准确率和泛化能力不足的问题,该研究提出一种基于最优特征组合的水稻关键生育期(分蘖期、拔节期、抽穗期、乳熟期、完熟期)识别方法。首先使用无人机采集田间光谱图像,基于相对植被指数和迭代自组织数据分析算法对光谱图像进行分割,以有效提取水稻冠层区域。然后对水稻生育期的关键特征进行表达,采用最小冗余特征选择算法对特征进行重要性排序,并通过增量分组法确定最优特征组合。最后基于极度梯度提升算法构建水稻生育期的识别模型。对比试验结果显示,本文模型对5个关键生育期的识别较好,混淆情况少,对水稻分蘖期、拔节期、抽穗期、乳熟期和完熟期识别精确率分别为98.08%、100.00%、99.68%、97.50%和99.29%,整体识别精确率达到98.77%,F1值为0.9891,Kappa系数为0.984,相比于SVM(支持向量机)分别提高了1.59个百分点、0.0146和0.02,相比于RF(随机森林)分别提升了1.23个百分点、0.011和0.015。研究结果可为田间作物的精准管理和决策提供重要依据。A near-real-time recognition model was constructed to rapidly and timely identify the rice growth stages using sequential spectral images.The key stages of rice growth were identified,including tillering,jointing,heading,milking,and maturity.An optimal combination of features was also obtained.The rice experimental field was carried out in the Tianhe District,Guangzhou City,Guangdong Province,China.Unmanned aerial vehicles(UAVs)were involved in capturing spectral images of the field.The drone imaging system was equipped with one visible light and five multispectral cameras,each of which had a 2-million-pixel resolution for the high-definition captures.Detailed spectral data was obtained to precisely identify the different rice growth stages.A significant challenge was the variation in the RGB values under different lighting conditions.The reason was that the accurate segmentation of the rice canopy was hindered by using color as the primary feature.A novel spectral image segmentation was introduced to combine the Relative Vegetation Index using an iterative self-organizing data analysis.The complete area of the rice canopy was precisely extracted for the accurate identification of the growth stage.Then,the dataset was simplified to reduce the dimensions and computational complexity,in order to enhance the performance and efficiency of recognition.Among them,the expression of features was analyzed during different rice growth stages,where the reflectance data was from green,red,blue,red-edge,and near-infrared bands.Eight vegetation indices were considered as the candidate features,including the normalized difference red edge index(NDRE),visual atmospherically resistant index(VARI),and red edge chlorophyll index(RECI).Minimum Redundancy Feature Selection was used to efficiently rank and select the most relevant and least redundant features,in order to avoid computational overload and potential impact on the model.The optimal combination of features was identified as the NDRE,VARI,and RECI,thus forming the crux of t

关 键 词：水稻 机器学习 生育期 光谱图像 识别 

分 类 号：S126[农业科学—农业基础科学]