UAV imaging provides an efficient and non-destructive tool for characterizing farm information, but the quality of the UAV model is often affected by the image’s spatial resolution. In this paper, the predictability of models established using UAV multispectral images with different spatial resolutions for nitrogen content of winter wheat was evaluated during the critical growth stages of winter wheat over the period 2021–2022. Feature selection based on UAV image reflectance, vegetation indices, and texture was conducted using the competitive adaptive reweighted sampling, and the random forest machine learning method was used to construct the prediction model with the optimized features. Results showed that model performance increased with decreasing image spatial resolution with a R², a RMSE, a MAE and a RPD of 0.84, 4.57 g m⁻², 2.50 g m⁻² and 2.34 (0.01 m spatial resolution image), 0.86, 4.15 g m⁻², 2.82 g m⁻² and 2.65 (0.02 m), and 0.92, 3.17 g m⁻², 2.45 g m⁻² and 2.86 (0.05 m), respectively. Further, the transferability of models differed when applied to images with coarser (upscaling) or finer (downscaling) resolutions. For upscaling, the model established with the 0.01 m images had a R² of 0.84 and 0.89 when applied to images with 0.02 m and 0.05 m resolutions, respectively. For downscaling, the model established with the 0.05 m image features had a R² of 0.86 and 0.83 when applied to images of 0.01 m and 0.02 m resolutions. Though the image spatial resolution affects image texture features more than the spectral features and the effects of image spatial resolution on model performance and transferability decrease with increasing plant wetness under irrigation treatment, it can be concluded that all the UAV images acquired in this study with different resolutions could achieve good predictions and transferability of the nitrogen content of winter wheat plants.

Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (H_dsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R², root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the H_dsm, are 0.58, 4528.23 kg/hm², and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm²) is slightly smaller than the measured mean AGB (16,960.23 kg/hm²). (2) The R², RMSE, and RPD of the improved AGB estimation model, based on AGB/H_dsm, are 0.88, 2291.90 kg/hm², and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm²) is more similar to the measured mean AGB (17,222.59 kg/hm²). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the H_dsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

Information about the nitrogen dynamic in wheat is important for improving in-season crop precision nutrient management and cultivated land sustainability. To develop unmanned aerial vehicle (UAV)-based spectral models for an accurate and effective assessment of the plant nitrogen content in the key stages (jointing, booting, and filling) of wheat growth, winter wheat experiment plots in Henan Province, China, were used in this study. Based on the K6 multichannel imager, 5-band (Red, Green, Blue, Red edge, and Near-infrared (Nir)) multispectral images were obtained from a UAV system and used to calculate 20 vegetation indices and 40 texture features from different band combinations. Combining the sensitive spectral features and texture features of the nitrogen content of winter wheat plants, BP neural network (BP), random forest (RF), Adaboost, and support vector machine (SVR) machine learning methods were used to construct plant nitrogen content models, and compared for the model performance and transferability. The results showed that the characteristics of different spectral features were different, but most of them had a partial normal distribution. Compared with spectral features, the distribution of texture features was more discrete. Based on Pearsons correlation analysis, 51 spectral and texture features were selected to build four machine learning models. The estimates of plant nitrogen by the RF and Adaboost methods were relatively concentrated, mostly close to the 1:1 line; while the estimates of plant nitrogen from the BP and SVR methods were relatively scattered. The RF method was the best, with coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) of 0.811, 4.163, and 2.947g/m², respectively; the SVR method was the worst, with R², RMSE, and MAE of 0.663, 5.348, and 3.956g/m², respectively. All models showed strong transferability, especially the RF and Adaboost methods, in predicting winter wheat nitrogen content under rainfed and irrigation water management.

In order to quickly and accurately obtain density and height information of winter wheat varieties, it is of great practical significance for the growth monitoring of new wheat varieties. In actual production, the plant density and height are mainly obtained by manual measurement, which is inefficient, time-consuming and laborious. Therefore, the winter wheat were extracted coverage based on unmanned aerial vehicles (UAV) images at seedling stage, the relationship between coverage and plant density were investigated. Moreover, the high-definition digital images of winter wheat varieties at 4 growth stages including jointing, booting, flowering and grain filling stages were obtained. The digital orthophoto model (DOM) and digital surface model (DSM) of winter wheat varieties was generated in combination with the ground control points. The estimation model of plant height at the four growing stages were established. Based on the ground measured plant height (H) of new wheat varieties, the plant height of new wheat varieties extracted by DSM was verified. The results showed that the coverage of new wheat varieties extracted from the UAV images at seedling stage was highly correlated with the measured plant density, and the coefficient of determination (R²) was 0.82. The new wheat varieties H extracted by DSM was significantly correlated with the measured H, and the fittedR²and root mean square error (RMSE) of the predicted plant height and the measured value were 0.96 and 6.32 cm, respectively. It indicated that the use of UAV images to predict the plant density and plant height of new wheat varieties has good applicability, and can provide technical reference for the monitoring of wheat phenotypic information in the future.

To realize the digital and intelligent management of the agricultural production process, reduce farmland pollution and alleviate the contradiction between supply and demand of agricultural water, this paper studies and designs a farmland environment collection and remote environmental monitoring system based on the network system. The system uses sensors to collect ecological information related to crop growth, such as high temperature, humidity, solar radiation, rainfall, soil temperature, and moisture. It then transmits the data to the data processing module through wireless transmission. At last, the STM 32 F 103 RCT 6 chips are used to process and analyze the collected data and visualize the collected real-time data and analysis results. As a result, according to the demand for water and fertilizer for crop growth, agricultural irrigation can be guided to effectively improve the efficiency of farmland management, which is of great significance for promoting social and economic development.