Yang Wanli, Li Zhijun, Chen Guofu, Cui Shihao, Wu Yue, Liu Xiaochi, Meng Wen, Liu Yucheng, He Jinyao, Liu Danmao, Zhou Yifan, Tang Zijun, Xiang Youzhen, Zhang Fucang
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China.
Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China.
Plants (Basel). 2024 May 29;13(11):1498. doi: 10.3390/plants13111498.
Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean ( L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients ( < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.
高效获取作物叶片水分信息对农业生产具有重要意义。该信息为农民提供了准确的数据基础，使他们能够实施及时有效的灌溉管理策略，从而最大限度地提高作物生长效率和产量。在本研究中，采用了无人机多光谱技术。通过连续两年（2021 - 2022年）的田间试验，收集了大豆叶片水分数据及相应的无人机多光谱图像。建立了与先前研究和作物参数具有强相关性的植被指数、冠层纹理特征以及随机提取的纹理指数组合。通过分析这些参数与大豆叶片水分之间的相关性，选择相关系数显著（<0.05）的参数作为模型的输入变量（组合1：植被指数；组合2：纹理特征；组合3：随机提取的纹理指数组合；组合4：植被指数、纹理特征和随机提取的纹理指数组合）。随后，利用极限学习机（ELM）、极端梯度提升（XGBoost）和反向传播神经网络（BPNN）对叶片含水量进行建模。结果表明，与纹理特征相比，大多数植被指数与大豆叶片水分的相关系数更高，而随机提取的纹理指数在一定程度上可以增强与大豆叶片水分的相关性。随机组合纹理指数RDTI与叶片水分的相关系数最高，为0.683，其纹理组合为方差1和相关性5。当将组合4（植被指数、纹理特征和随机提取的纹理指数组合）作为输入并采用XGBoost模型进行大豆叶片水分监测时，本研究取得了最高水平。估计模型验证集的决定系数（R）达到0.816，均方根误差（RMSE）为1.404，平均相对误差（MRE）为1.934%。本研究为无人机多光谱监测大豆叶片水分提供了基础，为快速评估作物生长提供了有价值的见解。
