Recently, polyacrylates (PA) have been applied in coated controlled-release fertilizer (CRF), but the impacts of the soil on the degradation of PA have not been evaluated. In this study, an outdoor agriculture soil buried test was carried out for 12 months to investigate the degradation of PA films. The residual degraded films were taken regularly from the soil and analyzed by SEM, Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) and laser-induced breakdown spectroscopy (LIBS). The concentration of C–H and C=O molecular groups of PA were decreased, and the element concentrations of C, O, K, Si of PA were increased under the degradation process. The surface of PA became rough and the degradation of PA occurred on the surface layer. Principal component analysis (PCA) showed that soil invaded PA. The results indicated that PA were environmentally friendly when applied to CRF. FTIR-PAS and LIBS were advanced in the in-situ surface analysis of the degradation process of the polymer.

Metal-organic frameworks (MOFs) were usually synthesized in hydrothermal conditions; in this study, a more energy-saving, easier to control, and solvent-free mechanochemical method was firstly applied to synthesize MOFs with varied reactants as slow release fertilizer, and the components and structures were characterized by X-ray diffraction (XRD), Fourier transform infrared total attenuated reflectance (FTIR-ATR), and laser-induced breakdown spectroscopy (LIBS). Results showed that three MOFs (compounds I, II, and III) were obtained, the MOFs were confirmed as oxalate phosphate oxalate frameworks (OPA-MOF), and ions were adsorbed between layers that contributed to the contents, while urea molecules mainly impacted the structure. The elemental compositions significantly varied among the three compounds; compound I showed the highest content of N (4.91%), P (15.71%), and Fe (18.60%), compound III indicated the highest content of C (6.52%) and K (12.59%), while the contents of C, K, P, and Fe in compound II were in the medium range. Similar release profiles of Fe and P were found among the three MOFs, and the release rates of nutrients were demonstrated as the order of N > K > P > Fe. The compositions and release profiles demonstrated potential application of MOFs as a novel slow-release fertilizer.

Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS), versus attenuated total reflectance spectroscopy (FTIR-ATR) and diffuse reflectance spectroscopy (DRIFT), was firstly applied in quick assessment of rice quality in response to rising CO₂/temperature instead of conventional time-consuming chemical methods. The influences of elevated CO₂ and higher temperature were identified using FTIR-PAS spectra by principal component analysis (PCA). Variations in the rice functional groups are crucial indicators for rice identification, and the ratio of the intensities of two selected spectral bands was used for correlation analysis with starch, protein, and lipid content, and the ratios all showed a positive linear correlation (R² = 0.9103, R² = 0.9580, and R² = 0.9246, respectively). Subsequently, changes in nutritional components under future environmental conditions that encompass higher CO₂ and temperature were evaluated, which demonstrated the potential of FTIR-PAS to detect the responses of rice to climate change, providing a valuable technique for agricultural production and food security.

Estimating plant nitrogen concentration (PNC) has been conducted using vegetation indices (VIs) from UAV-based imagery, but color features have been rarely considered as additional variables. In this study, the VIs and color moments (color feature) were calculated from UAV-based RGB images, then partial least square regression (PLSR) and random forest regression (RF) models were established to estimate PNC through fusing VIs and color moments. The results demonstrated that the fusion of VIs and color moments as inputs yielded higher accuracies of PNC estimation compared to VIs or color moments as input; the RF models based on the combination of VIs and color moments (R² ranging from 0.69 to 0.91 and NRMSE ranging from 0.07 to 0.13) showed similar performances to the PLSR models (R² ranging from 0.68 to 0.87 and NRMSE ranging from 0.10 to 0.29); Among the top five important variables in the RF models, there was at least one variable which belonged to the color moments in different datasets, indicating the significant contribution of color moments in improving PNC estimation accuracy. This revealed the great potential of combination of RGB-VIs and color moments for the estimation of rice PNC.

The behavior of a metal-organic framework (MOF) compound synthesized in hydrothermal reaction conditions and rich in N, P, and Fe nutrients was explored in the field. The attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy and laser induced breakdown spectroscopy (LIBS) characterization results showed that the chemical structures changed during the degradation process in crop field soil. The scanning electron microscope images showed that the micro-rod of the MOF peeled off and degraded in layers. During the growth period of wheat, the MOF degraded by 50.9%, with the degradation rate being closely related to soil temperature. It was also found that the degradation rate increased with soil temperature. Moreover, the nutrient concentration of the soil indicated that the MOF had stable nutrients release efficiencies and could provide a continuous supply of nutrients throughout the wheat growth period, which showed a great alternative for MOF as a fertilizer both benefiting agricultural production and environmental protection.

Accurate and timely detection of phenology at plot scale in rice breeding trails is crucial for understanding the heterogeneity of varieties and guiding field management. Traditionally, remote sensing studies of phenology detection have heavily relied on the time-series vegetation index (VI) data. However, the methodology based on time-series VI data was often limited by the temporal resolution. In this study, three types of ensemble models including hard voting (majority voting), soft voting (weighted majority voting) and model stacking, were proposed to identify the principal phenological stages of rice based on unmanned aerial vehicle (UAV) RGB imagery. These ensemble models combined RGB-VIs, color space (e.g., RGB and HSV) and textures derived from UAV-RGB imagery, and five machine learning algorithms (random forest; k-nearest neighbors; Gaussian naïve Bayes; support vector machine and logistic regression) as base models to estimate phenological stages in rice breeding. The phenological estimation models were trained on the dataset of late-maturity cultivars and tested independently on the dataset of early-medium-maturity cultivars. The results indicated that all ensemble models outperform individual machine learning models in all datasets. The soft voting strategy provided the best performance for identifying phenology with the overall accuracy of 90% and 93%, and the mean F1-scores of 0.79 and 0.81, respectively, in calibration and validation datasets, which meant that the overall accuracy and mean F1-scores improved by 5% and 7%, respectively, in comparison with those of the best individual model (GNB), tested in this study. Therefore, the ensemble models demonstrated great potential in improving the accuracy of phenology detection in rice breeding.