Abstract: Accurately obtaining chlorophyll content in direct‑seeded rice is of great significance for innovating unmanned rice direct seedling cultivation management technologies.Field experiments were conducted to identify the optimal monitoring model for the relative chlorophyll content（SPAD value）in leaves of direct‑seeded rice.This study systematically examined the correlations between 13 commonly used multispectral feature indices and SPAD values，followed by a comparative analysis of SPAD estimation results derived from four modeling approaches：Particle swarm optimization‑support vector machine（PSO‑SVM），random forest（RF），radial basis function（RBF）neural network，and least squares support vector machine（LSSVM）.The results showed that the SPAD value of direct‑seeded rice leaves exhibited significant variation with growth progression under different treatments，and the SPAD values at the same growth stage generally followed the trend：N4（N 240 kg/ha）>N3（N 195 kg/ha）>N2（N 150 kg/ha）>N1（N 75 kg/ha）>N0（N 0 kg/ha）.During the three critical growth periods（the tillering，jointing，heading periods）of direct‑seeded rice，the vegetation indices NDVI，RVI，SAVI，CI_green，and GNDVI all showed strong correlations with SPAD values，with the absolute values of correlation coefficients reaching 0.838，0.783，0.838，0.671，and 0.690，respectively. Independent validation using PSO‑SVM，RF，RBF，and LSSVM models yielded determination coefficients（R_cv²）of 0.770，0.771，0.857，and 0.773，respectively.This indicates that the RBF neural network‑based model provides the best predictive performance for monitoring SPAD values in the leaves of direct‑seeded rice.

Key words: Rice, SPAD values, Direct?seeding, Unmanned aerial vehicle, Radial basis function neural network

摘要： 准确获取直播水稻叶绿素含量信息对创新水稻无人化直播栽培管理技术具有重要意义。为筛选直播水稻叶片叶绿素相对含量（SPAD值）的最优监测模型，于2022—2023年连续2 a设置了大田直播水稻试验，分析了13个常用多光谱特征指数与SPAD值的相关关系，并对基于粒子群算法优化支持向量机（Particle swarm optimization‑support vector machine，PSO-SVM）、随机森林（Random forest，RF）、径向基函数（Radial basis function，RBF）神经网络和最小二乘支持向量机（Least squares support vector machine，LSSVM）模型的SPAD值估算结果进行了对比分析。结果表明，不同氮肥处理下直播水稻叶片SPAD值因生育时期时间节点不同而存在差异，且不同的肥力下直播水稻叶片SPAD值高低总体表现为N4处理（N 240 kg/hm²）>N3 处理（N 195 kg/hm²）>N2 处理（N 150 kg/hm²）>N1 处理（N 75 kg/hm²）>N0 处理（N 0 kg/hm²）；在水稻分蘖期、拔节期、抽穗期3个生育时期，NDVI、RVI、SAVI、CI_green、GNDVI植被指数与SPAD值的相关性均相对较好，相关系数绝对值分别达到0.838、0.783、0.838、0.671、0.690。基于PSOSVM、RF、RBF、LSSVM模型的独立验证Rcv2分别为0.770、0.771、0.857、0.773，可见RBF模型可更好地用于监测直播水稻叶片SPAD值。

关键词: 水稻, SPAD值, 直播, 无人机, 径向基神经网络