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Early or in-season crop type mapping using remote sensing data is important for crop managements to maximize crop yield. Existing remote sensing-based approaches in the literature rely mainly on the timing and temporal frequency of satellite data acquisition during a growing season to obtain optimal identification feature (OIF) to complete crop mapping. At present, the optimal features for mapping crop types are commonly generated by using different feature selection methods, and thereby are not generic in nature. In this paper, a novel approach based on the OIF knowledge graph is proposed for identification of crop types within the crop growing season. The concept of OIF was proposed based on the one-class classification and optimization theory, which relates a specific crop type to its OIF. The OIF knowledge graph can be created to describe the seasonal trend of OIF in a season. Eventually, the OIF knowledge graphs can be incorporated into the moment-preserving segmentation method to automatically identify crop types within the growing season. To evaluate the performance of the new method, crop mapping was conducted using 2019 Sentinel-2 data in Heilongjiang province, China, and Illinois, USA, respectively. Results demonstrate that integrating the OIF knowledge graph and moment-preserving thresholding method is effective in identifying crop types without ground-truth data. The overall accuracy higher than 90 % (Kappa coefficient greater than 0.90), and the producer's accuracies for corn, soybean, and rice are generally greater than 93 % in Heilongjiang province. The proposed approach has the advantage of simplifying crop identification process while maintaining a satisfactory identification accuracy in shorter time than conventional methods. [ABSTRACT FROM AUTHOR]

This study presents a novel approach in which a dual network (DN) composite, comprising polyvinyl alcohol (PVA) and ribbon-like nanocellulose (RC), was synthesized in one step using the volume exclusion effect involved in enzyme-catalyzed cellulose synthesis. Additionally, the impact of PVA as a crowding reagent during enzymatic catalysis on the in situ formation of nanocellulose and its resulting aspect ratio was explored. In contrast, the other two composites were created by incorporating enzyme-catalyzed synthetic block cellulose (BC) and its acid-hydrolyzed regenerated disc-shaped cellulose (DC) into the PVA. Subsequently, the mechanism by which three distinct types of nanocellulose, varying in morphology and size, was explored to elucidate their contributions to enhancing the properties of PVA. The results demonstrated that PVA/RC outperformed PVA/BC and PVA/DC. The elevated aspect ratio and intricate network structure of RCs not only significantly bolster the mechanical robustness of PVA/RC, leading in an 86.40 % surge in tensile strength and a remarkable 277.03 % rise in tensile modulus in comparison to pure PVA, but also induce a slight enhancement in elongation at break. Moreover, the thermal stability and biodegradability of PVA/RC was enhanced. Collectively, this study introduces an innovative strategy for the efficient fabrication of biodegradable composites with enhanced properties. [Display omitted] [ABSTRACT FROM AUTHOR]

β-Nicotinamide mononucleotide (NMN) is an important precursor in the synthesis of nicotinamide adenine dinucleotide (NAD+) and confers multiple health benefits, resulting in the rapid growth of NMN market capacity in the fields of food and health care. To overcome the drawbacks of NMN production by the existing chemical or microbial fermentation method, there is an urgent need to develop a prospective NMN production strategy with low cost, low pollution, and high yield. In this study, we demonstrated an artificial in vitro multi-enzyme cascade biocatalysis using starch and nicotinamide (Nam) as substrates for the synthesis of NMN in one-pot. This multi-enzyme cascade reaction was optimized in terms of pH value, buffer concentration, inorganic phosphate concentration, enzyme composition, and phosphoenolpyruvate concentration. Under optimized conditions, a high molar yield of 87.8% for NMN was achieved using 3.2 mM Nam as substrate, and a molar yield of 55.37% for NMN was also achieved under the initial Nam concentration of 9.21 mM. This in vitro enzymatic platform provides an environmental friendliness biomanufacturing technology for the production of NMN, showing a highly promising alternative approach for NMN production. • We created an innovative platform for the biomanufacturing of NMN from starch and Nam via in vitro multi-enzyme cascade biosystem. • This NMN production method is environmentally friendly. • The system can efficiently convert maltodextrin and Nam to NMN. [ABSTRACT FROM AUTHOR]

An antibacterial cellulose oligomer was synthesized by cellodextrin phosphorylase (CDP) from glucose 1-phosphate (G1P) using a glucoside with an antibacterial group as a primer, then a hydrophilic cellulose molecular coating with excellent anti-fouling and anti-bacterial properties on PVDF membrane surface was carried out by dissolving the synthetic cellulose into a homogeneous solution. [Display omitted] • The antibacterial cellulose was synthesized from G1P in one step catalyzed by CDP. • Hydrophilic coating fabricated in one step showed high water flux. • Cellulose coating showed excellent anti-fouling and anti-bacterial performances. Synthetic cellulose prepared by enzyme has the advantages of easy functionalization, easy preparation, and biocompatibility, making it an ideal material for constructing hydrophilic coating with functionalities. In this study, β-D glucose with phenolic group (arbutin) as a primer reacted with glucose 1-phosphate (G1P) under the catalysis of Clostridium thermocellum (CtCDP) to synthesize antibacterial cellooligomer. Then, the synthetic cellooligomer was dissolved into a homogeneous organic solution to prepare a functional hydrophilic coating composed of cellulose molecular chains on the surface of polyvinylidene fluoride (PVDF) membranes. The molecular structure of enzymatic antibacterial cellooligomer was characterized by fourier transform infrared spectroscopy (FTIR) and 1H nuclear magnetic resonance spectroscopy (1H NMR). ATR-FTIR confirmed the successful preparation of cellulose coatings. The performance test results showed that the strong interaction at the interface between cellooligomer and PVDF membrane promoted the uniform distribution of cellulose molecules on the membrane surface, which greatly improved the hydrophilicity of PVDF membrane and enhanced the stability of the coating bonded to the substrate. Moreover, the cellulose molecular coating had excellent antifouling performance, outstanding antibacterial activity, and anti-adhesion to Staphylococcus aureus (S. aureus). This work provides a versatile platform for the time-saving and cost-effective fabrication of antifouling and antibacterial membranes. [ABSTRACT FROM AUTHOR]

Hydrophilic PVDF membrane was fabricated in single step via cellulose molecular coating prepared by homogeneous solution of cellulose oligomer synthesized by enzyme catalysis (EC)/Trifluoroacetic acid (TFA). Both smooth and rough coatings can be prepared by regulating the aggregative structure of cellulose molecular coatings. The chemical structures of the cellulose and its coating were confirmed by 1H Nuclear Magnetic Resonance Spectroscopy (1H NMR) and Fourier Transform Infrared Spectroscopy (FTIR). The aggregation structure of the cellulose coating was characterized by wide-angle X-ray diffraction (WAXD). X-ray Photoelectron Spectroscopy (XPS) studies unveiled the strong binding of cellulose molecular to PVDF membranes at the interface. Scanning Electron Microscopy (SEM) morphologies show that the cellulose molecules were uniformly coated on the surface and cross section of the membranes. Water contact angle measurement suggested that cellulose molecular coating possessed high hydrophilicity and wetting rate, giving the PVDF membranes high water flux and excellent anti-fouling performances. The coated membranes also show excellent adsorption and desorption performances for crystal violet (CV). The superior performances of incompletely crystallized cellulose coating over amorphous cellulose coating were attributed to the increased specific surface area resulting from the surface roughness. The construction of cellulose molecular coating with controlled aggregation structure provides a novel efficient technology in the field of purification of wastewater, opening up a new approach to the microstructural manipulation of the hydrophilic coating. A novel strategy to prepare the cellulose molecular coating with versatile functions. [Display omitted] • Hydrophilic PVDF membrane was fabricated in one step via cellulose molecular coating. • The surface roughness of coating can be regulated based on the aggregation state. • Cellulose coating showed high water flux and excellent anti-fouling performances. [ABSTRACT FROM AUTHOR]

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture. [ABSTRACT FROM AUTHOR]

Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (VIgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition. [ABSTRACT FROM AUTHOR]

Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction. [ABSTRACT FROM AUTHOR]

Early crop identification can provide timely and valuable information for agricultural planting management departments to make reasonable and correct decisions. At present, there is still a lack of systematic summary and analysis on how to obtain real-time samples in the early stage, what the optimal feature sets are, and what level of crop identification accuracy can be achieved at different stages. First, this study generated training samples with the help of historical crop maps in 2019 and remote sensing images in 2020. Then, a feature optimization method was used to obtain the optimal features in different stages. Finally, the differences of the four classifiers in identifying crops and the variation characteristics of crop identification accuracy at different stages were analyzed. These experiments were conducted at three sites in Heilongjiang Province to evaluate the reliability of the results. The results showed that the earliest identification time of corn can be obtained in early July (the seven leaves period) with an identification accuracy up to 86%. In the early stages, its accuracy was 40~79%, which was low, and could not reach the satisfied accuracy requirements. In the middle stages, a satisfactory recognition accuracy could be achieved, and its recognition accuracy was 79~100%. The late stage had a higher recognition accuracy, which was 90~100%. The accuracy of soybeans at each stage was similar to that of corn, and the earliest identification time of soybeans could also be obtained in early July (the blooming period) with an identification accuracy up to 87%. Its accuracy in the early growth stage was 35~71%; in the middle stage, it was 69~100%; and in the late stage, it was 92~100%. Unlike corn and soybeans, the earliest identification time of rice could be obtained at the end of April (the flooding period) with an identification accuracy up to 86%. In the early stage, its accuracy was 58~100%; in the middle stage, its accuracy was 93~100%; and in the late stage, its accuracy was 96~100%. In terms of crop identification accuracy in the whole growth stage, GBDT and RF performed better than other classifiers in our three study areas. This study systematically investigated the potential of early crop recognition in Northeast China, and the results are helpful for relevant applications and decision making of crop recognition in different crop growth stages. [ABSTRACT FROM AUTHOR]