Yin Caixia, Wang Zhenyang, Lv Xin, Qin Shizhe, Ma Lulu, Zhang Ze, Tang Qiuxiang
College of Agriculture, Xinjiang Agricultural University, Urumqi, China.
Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Shihezi University, Shihezi, China.
Front Plant Sci. 2024 Aug 16;15:1380306. doi: 10.3389/fpls.2024.1380306. eCollection 2024.
Individual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.
In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square ( ), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model.
(i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients () can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, , RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.
图像中的单张叶片部分被其他叶片遮挡，从而在另一叶片上形成阴影。为消除土壤和叶片阴影对棉花光谱的干扰，并实现对棉花氮含量的可靠监测，提出了一种针对无人机（UAV）图像像素的分类方法。
在本研究中，将绿光（550纳米）分为10个等级，以限制土壤和叶片阴影（LS）对棉花光谱的影响。植被指数（VI）与叶片氮含量（LNC）之间的强相关性可用于确定有多少阴影对棉花光谱有影响。利用几种机器学习方法，通过受干扰较小的VI来预测LNC。采用决定系数（R²）、均方根误差（RMSE）和平均绝对误差（MAE）来评估模型性能。
（i）在光谱经过高斯滤波（GF）、Savitzky-Golay平滑（SG）以及GF与SG的组合（GF&SG）预处理后，VI与LNC之间的显著关系得到极大改善，数据集的标准差也大幅降低；（ii）对图像像素进行了两次顺序分类。第一次分类后，土壤对植被指数（VI）的影响降低。二次分类后，土壤和LS对VI的影响可降至最低。VI与LNC之间的关系得到显著改善；（iii）对图像像素进行分类后，2-3、2-4和2-5的VI与LNC的关系相应更强。相关系数（R）可达0.5。当结合GF&SG预测LNC时，支持向量机回归（SVMR）具有更好的性能，R²、RMSE和MAE分别高达0.86、1.01和0.71。本研究中的无人机图像分类技术可将土壤和LS对棉花光谱的负面影响降至最低，从而实现对LNC的高效、及时预测。
