Youzhen Xiang's research while affiliated with Northwest A & F University and other places

... Chlorophyll is one of the most important pigments in plants; in the process of photosynthesis, chlorophyll plays a crucial role in facilitating energy conversion and sustaining the growth and development of plants. Furthermore, it acts as a protective and regulatory agent in maintaining plant health, thereby aiding in plant adaptation to external environmental conditions [4]. Romina et al. [5] used different kinds of chlorophyll instruments to determine the nitrogen content in crops by measuring the chlorophyll content of sweet peppers. ...

... Warlles et al. [6] utilized a portable chlorophyll meter for measuring the chlorophyll content in sorghum leaves; a significant correlation between photosynthetic pigments extracted in DMSO and leaf nitrogen content was determined. The above studies have indicated that chlorophyll content can be used to predict crop nitrogen requirements, assess crop photosynthetic capacity, and determine nutritional levels [7]. Accurate monitoring of the chlorophyll content in winter wheat can help prevent soil and water pollution caused by excessive fertilization [8]. ...

... This method is not only time-consuming and labor-intensive, but also yields delayed and unstable results, greatly limiting the accuracy and timeliness of agricultural decision making [10]. In modern precision agriculture, efficient and non-destructive monitoring of crop growth status is vital [11]. Remote sensing technology, as a novel detection approach, enables the real-time acquisition of various spectral information of land cover on a large scale and has been widely applied in modern agricultural management [12]. ...

... In modern precision agriculture, efficient and non-destructive monitoring of crop growth status is vital [11]. Remote sensing technology, as a novel detection approach, enables the real-time acquisition of various spectral information of land cover on a large scale and has been widely applied in modern agricultural management [12]. ...

... In our study on cultivated Pyropia in coastal area, we showed that the biomass could be estimated using multi-spectral imaging (Che et al. 2021). However, for essential macroelements such as C, N and S, there are still no published works on non-destructive phenotyping in marine crops by spectral imaging (Ge et al. 2019;Li et al. 2022;Du et al. 2023). In fact, these essential macroelements can be used as indicators for evaluating nutritional status and commercial value because they are main components of carbohydrates and proteins. ...

... Various machine learning models have been employed to forecast specific thermal properties, yielding notable outcomes that have sparked further inquiry. In the study conducted by Fan et al. [35], support vector machines and XGBoost algorithms were utilized to predict solar radiation, where SVM demonstrated slightly superior results compared to the XGBoost model. Another investigation by Bang et al. [36] focused on predicting the thermal conductivity of bentonite, employing various machine learning models and underscoring the exceptional performance of the Gaussian process regression model. ...

... China is increasingly focusing on the issue of food security, and the country's overall grain production capacity has been steadily improving (Shen et al., 2018;Gao et al., 2021). However, current crop production still faces challenges such as diminishing arable land, agricultural water scarcity, and low returns, etc. (Wang et al., 2023). Winter oilseed rape, as one of the primary oil crops, constitutes 90% of the oilseed rape area in China, which was mainly planted in the Yangtze River basin (Gu et al., 2018a). ...

... These studies suggest that spectral information has the capability to monitor crop LNC, and although constructing fixed-wavelength spectral indices can accurately monitor nitrogen conditions to some extent, the different physiological information of crops due to factors such as their growth environment and growth stage can lead to variations in their spectral characteristics [26,27]. In such cases, using the same wavelengths may result in inadequate utilization of spectral data, limiting the effectiveness of the calculated spectral index inversion model and reducing model accuracy [28]. Furthermore, for crops, there is a lack of comparison and discussion on the prediction effects of different vertical scales of LNCs. ...

... However, there is not good method for computing the diffuse radiation under a cloudy sky. Regarding this topic, many researchers have recently carried out several studies with empirical formulas based on different parameters for different regions of the world [3,[25][26][27], new estimation methodologies such as machine learning models and hybrids models [28][29][30][31][32], and remote sensing applications [33,34]. ...

... The sum of the yellow edge wavelength range after the 1-FD order treatment [28] SD r The sum of the red edge wavelength range after the 1-FD order treatment. [28] SDr-SDb [13] Notes: R i (i = 1, 2, 3) is any value of the wavelength reflectivity in the measurement range (350~1830 nm), 1-FD is the first-order differential, and R number is the spectral reflectivity of the digital band. ...