In recent years, the delay in sowing has become a major obstacle to high wheat yield in Jiangsu Province, one of the major wheat producing areas in China; hence, it is necessary to screen wheat varieties are resilient for late sowing. This study aimed to provide an effective, fast, and non-destructive monitoring method of soil plant analysis development (SPAD) values, which can represent leaf chlorophyll contents, for late-sown winter wheat variety screening. This study acquired multispectral images using an unmanned aerial vehicle (UAV) at the overwintering stage of winter wheat growth, and further processed these images to extract reflectance of five single spectral bands and calculated 26 spectral vegetation indices. Based on these 31 variables, this study combined three variable selection methods (i.e., recursive feature elimination (RFE), random forest (RF), and Pearson correlation coefficient (r)) with four machine learning algorithms (i.e., random forest regression (RFR), linear kernel-based support vector regression (SVR), radial basis function (RBF) kernel-based SVR, and sigmoid kernel-based SVR), resulted in seven SVR models (i.e., RFE-SVR_linear, RF-SVR_linear, RF-SVR_RBF, RF-SVR_sigmoid, r-SVR_linear, r-SVR_RBF, and r-SVR_sigmoid) and three RFR models (i.e., RFE-RFR, RF-RFR, and r-RFR). The performances of the 10 machine learning models were evaluated and compared with each other according to the achieved coefficient of determination (R²), residual prediction deviation (RPD), root mean square error (RMSE), and relative RMSE (RRMSE) in SPAD estimation. Of the 10 models, the best one was the RF-SVR_sigmoid model, which was the combination of the RF variable selection method and the sigmoid kernel-based SVR algorithm. It achieved high accuracy in estimating SPAD values of the wheat canopy (R² = 0.754, RPD = 2.017, RMSE = 1.716 and RRMSE = 4.504%). The newly developed UAV- and machine learning-based model provided a promising and real time method to monitor chlorophyll contents at the overwintering stage, which can benefit late-sown winter wheat variety screening.

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R² = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R² = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R² = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R²) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R² of 0.878 and RMSE of 0.773 for the validation set 1, and R² of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field.

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R² = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R² = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R² = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.

To determine the combining effects of nitrogen application rate and planting density on rice yield and grain quality formation under salinity conditions, a field experiment was conducted in the coastal saline area using Oryza sativa L. cv. Nangeng 9108 from 2019 to 2020. The experiment was designed with six nitrogen rates (0, 210, 255, 300, 345, and 390 kg ha⁻¹; denoted as N0-N390, respectively) and two transplanting densities (334,000 and 278,000 hills ha⁻¹; denoted as D1 and D2, respectively). The results indicated that, with the increase of nitrogen input rate, the panicles number and spikelets per panicle increased first, subsequently decreased, and peaked under 300 kg ha⁻¹ N, whereas the filled-kernel rate and grain weight displayed a decreasing trend. The panicle number and grain weight were higher under D1 treatment compared to those under D2 treatment, while the spikelet number per panicle and the filled-kernel rate displayed an opposite trend. The grain yield displayed highest under N300D1 treatment among all treatments, accompanied by the highest agronomic N use efficiency, and the actual yield reached 8060.4 kg ha⁻¹ and 7869.8 kg ha⁻¹ in 2019 and 2020, respectively. Increased nitrogen application rate significantly improved the grain processing quality and nutritional quality, while reducing the appearance quality and cooking/eating quality. Higher transplant density was conductive to grain nutritional quality, but notably reduced the processing quality, appearance quality and cooking/eating quality. Overall, a combination of 300 kg ha⁻¹ nitrogen rate and 334,000 hills ha⁻¹ planting density was recommended for relatively higher rice yield and better grain quality in the saline area.

In China’s second-largest wheat-producing region, the mid-lower Yangtze River area, cold stress impacts winter wheat production during the pre-heading growth stage. Previous research focused on specific growth stages, lacking a comprehensive approach. This study utilizes Unmanned Aerial Vehicle (UAV) multispectral imagery to monitor Soil-Plant Analysis Development (SPAD) values throughout the pre-heading stage, assessing crop stress resilience. Vegetation Indices (VIs) and Texture Indices (TIs) are extracted from UAV imagery. Recursive Feature Elimination (RFE) is applied to VIs, TIs, and fused variables (VIs + TIs), and six machine learning algorithms are employed for SPAD value estimation. The fused VIs and TIs model, based on Long Short-Term Memory (LSTM), achieves the highest accuracy (R² = 0.8576, RMSE = 2.9352, RRMSE = 0.0644, RPD = 2.6677), demonstrating robust generalization across wheat varieties and nitrogen management practices. This research aids in mitigating winter wheat frost risks and increasing yields.