| 高博,王利书,贾艳辉,陈英超,何帅.基于色度自适应颜色校正的棉田土壤盐分反演[J].干旱地区农业研究,2026,(2):253~264 |
| 基于色度自适应颜色校正的棉田土壤盐分反演 |
| Soil salinity inversion in cotton fields based on chroma\|adaptive color correction |
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| DOI：10.7606/j.issn.1000-7601.2026.02.25 |
| 中文关键词: 棉花叶片成像 颜色校正 机器学习 线性拟合回归 土壤盐分反演 |
| 英文关键词:cotton leaf imaging color correction machine learning linear fitting regression soil salinity inversion |
| 基金项目:家自然科学基金（52209049）；潍坊科技学院高层次人才科研启动资金项目（KJRC2024001） |
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| 中文摘要: |
| 为快速有效地通过“RGB成像技术”反演棉田盐分状态，提出了一种改进后的色度自适应颜色校正算法；利用提取校正后棉花叶片图像的R、G、B值，探究棉花叶片表型R、G、B值与土壤盐渍化梯度间的关联规律。结果表明：经色度自适应颜色校正算法校正后的棉花叶片图像相较于传统校正矩阵算法而言，色差值提升22.26%，实现了对棉花叶片图像的R、G、B值的修正；而在R、G、B三通道颜色值与土壤电导率（EC）进行的拟合回归中，RF模型（R2=0.869）相较于MLR模型（R2=0.633）更全面地解释了R、G、B三通道颜色值与EC间的非线性复杂交互作用，在最优验证集的R2、RMSE、MAE等评估指标上均有提升，最优预测精度分别提升37.28%、9.06%、11.52%，印证了基于作物表型信息对田间土壤盐分具有一定解释力。相较于传统校正算法，本研究提出的算法实现了更高精度的棉花叶片表型特征解析，其增强的图像表征能力有效还原了叶片在自然生理状态下的真实表型参数，进一步验证了基于R、G、B图像颜色特征的植物表型数据与土壤盐分间存在显著协同响应。 |
| 英文摘要: |
| To rapidly and effectively invert soil salinity status in cotton fields using “RGB imaging technology”, an improved chromaticity\|adaptive color correction algorithm was proposed. This method utilized the extracted R， G， B values from corrected cotton leaf images to investigate the correlation patterns between phenotypic R， G， B values of cotton leaves and soil salinity gradients. The results showed that the cotton leaf images corrected by the chromaticity\|adaptive algorithm exhibit a 22.26% reduction in color difference compared to those processed by traditional correction matrix algorithms, achieving effective R， G， B value modification. In the fitting regression between R， G， B tristimulus values and Electrical Conductivity (EC) values, the Random Forest (RF) model (R2=0.869) outperformed the Multiple Linear Regression (MLR) model (R2=0.633) by more comprehensively explaining the nonlinear complex interactions between R， G， B channels and EC. The RF model showed enhanced performance across key validation metrics, R2, RMSE, and MAE, yielding improvements of 37.28%, 9.06%, and 11.52%, respectively, in optimal prediction accuracy. This confirms the explanatory power of crop phenotypic information for in\|field soil salinity assessment. Compared with conventional algorithms, the proposed method achieved higher\|precision phenotypic characterization of cotton leaves. Its enhanced image characterization capability effectively restores the true phenotypic parameters of leaves under natural physiological conditions, further validating a significant synergistic response between plant phenotypic data (based on R， G， B image color features) and soil salinity levels. |
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