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Water stress has been one of the most serious threat to the crop growth, development and yield quality in agricultural fields. Timely and accurate diagnosis of crop water stress can greatly contribute to the precision irrigation for the crop resilience and yield. In this study, the research object was taken from the summer maize in the typical dryland agricultural area of northwest China. A six-channel multispectral sensor was mounted on a drone to obtain the remote sensing image data of summer maize at the nodulatione and staminate pulling stage in 2022. At the same time, the stomatal conductance and phenotypic parameters of summer maize were also collected. The background was removed by supervised classification. The canopy vegetation index and image texture were obtained using the gray-scale covariance matrix. The sensitive vegetation index, image texture and phenotypic parameters and their combinations were screened out by the Bayesian information criterion and full subset filtering. The summer maize stomatal conductance estimation model was constructed to combine the three types of machine learnings: the extreme learning machine, the random forest, and the back-propagation neural network. The optimal model was mapped to estimate the stomatal conductance. The Pearson correlation coefficient of vegetation index and stomatal conductance were significantly positively correlated, whereas, the canopy reflectance of summer maize was weakly negatively correlated. Different types of image textures at different wavelengths were correlated with the stomatal conductance, and the highest correlation was found in the 550 nm band. The Pearson correlation coefficients between morphological structure phenotypes (plant height, stem thickness and leaf area) and stomatal conductance of summer maize were 0.72, 0.58 and 0.69, respectively, where the three types of phenotypic parameters data were correlated well with stomatal conductance. Vegetation indices with spectral reflectance data were used to assess the overall health and moisture status of the vegetation. Image texture was used to capture the spatial distribution, texture and structural features of crops. Crop phenotypic parameters were then used to reflect the physiological and morphological responses of the crop in a three-dimensional manner, providing visual information about the growth and moisture of the vegetation. The decision coefficients of the crop water stress diagnostic models that constructed from the three information sources increased from 0.728 and 0.750 to 0.841, respectively, compared with the single or two combinations, indicating the great potential to stomatal conductance prediction. The optimal combination of indicators was screened by Bayesian information criterion and full subset screening: DWSI, NDVI, MEA, ENT, plant height and leaf area. The back-propagation neural network model with the three complementary information sources was the optimal model for the water stress diagnosis of summer maize (coefficient of determination of 0.841, root mean square error of 0.043 mol/(m2 ·s), and mean absolute error of 0.034 mol/(m² ·s)). The underestimation of stomatal conductance was significantly improved, compared with the rest models. The inverse map with the optimal model was widely applied to easily and accurately diagnose the crop water stress for the purpose of irrigation strategies and resource allocation. The finding can provide a feasible and accurate diagnosis of water stress in summer maize. [ABSTRACT FROM AUTHOR]

Airborne hyperspectral data can reflect the spectral characteristics of tree species, which can be used for precise clas‐ sification of forest tree species. This study applies different machine-learning classification algorithms to classify tree species in hyper‐ spectral images of unmanned aerial vehicles (UAV). Firstly, a UAV was used to collect hyperspectral data from the Maor Mountain Experimental Forest Farm in Heilongjiang Province, and a series of preprocessing was completed for the obtained data. Then, three different machine learning classification algorithms, namely, support vector machine (SVM） based on Gaussian kernel, random forest (RF), and K-nearest neighbor (KNN), were used to establish the tree species classification models, respectively, based on the fullband hyperspectral data. Meanwhile, tree species classification models were constructed based on the dimension-reduced full-band hyperspectral data using different band selection methods (successive projections algorithm, competitive adaptive reweighted sampling method and uninformative variable elimination method). Finally, the tree species classification model was constructed by combining different band selection methods and hyperspectral image texture features, and the results of different processing methods were com‐ pared. Research shows, the kernel SVM had the highest classification accuracy (87. 55%） among the tree species classification mod‐ els with full-band hyperspectral data. After selecting different bands, the stability of RF is the best among the three classification algorithms, and the accuracy rate was high, while the classification accuracy of the support vector machine based on the Gaussian kernel improved with the increase of feature dimension. The accuracy of the tree species classification model established by extracting texture features based on a grayscale co-occurrence matrix combined with band selection was higher than that of the model established by a single band selection. In particular, the K-nearest neighbor classification algorithm has the greatest improvement, which indicated that modeling with clearly partitioned features can achieve good classification results. This study used different feature selection methods combined with three different machine learning classification algorithms to achieve dominant tree species classification based on hyperspectral data, which provides technical reference for the combination of band selection methods and machine learning algorithms, and it is also of great significance for forest biomass retrieval and carbon storage estimation based on UAV hyperspectral data. [ABSTRACT FROM AUTHOR]

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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)