Polyethylene imine-modified photonic crystal microfluidic chip for highly sensitive detection of microbial spores.

To address the lengthy cycles, complex operations, high costs, and insufficient sensitivity of biomarker detection in traditional biological control agents, photonic crystal treated with PEI was developed for highly sensitive detection of Sclerotinia sclerotiorum microbial spores. By incorporating gelatin molecules, photonic crystal is endowed with excellent photothermal stability and high stability in aqueous solutions. The photonic crystal surface is conferred a positive charge by PEI, which can be used to enhance the adsorption of spores. Efficient enrichment of Sclerotinia sclerotiorum and Purpureocillium lilacinum spores is achieved, with coefficients of determination (RYe et al. (2014)2) 0.963 and 0.971, respectively. The detection range is from 102 to 106 spores/ml, and the photonic crystal exhibited good reusability. The prepared photonic crystal enables rapid, non-destructive, and accurate quantitative detection of microbial spores.

An adaptive spacing of root-zone hole fertilization to improve production and fertilizer utilization of rapeseed.

BACKGROUND: Root-zone hole fertilization has a positive impact on enhancing crop production and fertilization efficiency. However, a suitable spacing for hole fertilization in rapeseed cultivation is unclear. To explore an adaptive hole spacing for improving rapeseed yield and fertilization efficiency, field experiments were conducted. Four spacings of hole fertilization were designed: 10 (FD10), 20 (FD20), 30 (FD30) and 40 cm (FD40), using no fertilization (F0) and deep-banded placement of fertilizer (DBP) as controls. The burial depth was 10 cm for FD and DBP treatments. RESULTS: Compared to DBP, hole fertilization impacted soil microenvironment, crop growth and yield components, resulting in a significant increase of 28.4% in seed yield and 25.6% in oil yield. Seed yield in FD20 (4345.43 kg ha-1) increased by 4.3%, 9.4% and 15.1% compared to FD10, FD30 and FD40, respectively. Fertilizer partial factor productivity under FD20 was 4.2%, 8.6% and 13.9% greater than FD10, FD30 and FD40, respectively; whereas the increase for agronomic efficiency was 6.0%, 12.7% and 21.0%, and the increase for N recovery efficiency was 39.5%, 52.5% and 62.9%, respectively. CONCLUSION: Fertilization with a hole spacing of 17 cm is a promising practice to maintain high production and fertilization efficiency when cultivating rapeseed. These results provide a theoretical foundation and scientific basis for improving rapeseed productivity and fertilizer utilization. © 2024 Society of Chemical Industry.

Foam nickel-PDMS composite film based triboelectric nanogenerator for speed and acceleration sensing.

As a new energy conversion technology, triboelectric nanogenerator (TENG) can use the coupling of triboelectrification and electrostatic induction effect to convert tiny mechanical energy into electrical energy, powering small electronic devices. In this paper, a vibration sensing triboelectric nanogenerator (V-TENG) based on a foam nickel-PDMS composite film was prepared, which can convert low frequency and small-amplitude mechanical energy into electrical energy, and the open circuit voltage of V-TENG can reach 3.6V at a vibration frequency of 4 Hz. In addition, the V-TENG can be used as a self-powered speed/acceleration sensor to detect speed changes in the range of 0.3 m/s to 1.5 m/s and acceleration changes in the range of 3 m/s2 to 13 m/s2.

Point clouds segmentation of rapeseed siliques based on sparse-dense point clouds mapping.

In this study, we propose a high-throughput and low-cost automatic detection method based on deep learning to replace the inefficient manual counting of rapeseed siliques. First, a video is captured with a smartphone around the rapeseed plants in the silique stage. Feature point detection and matching based on SIFT operators are applied to the extracted video frames, and sparse point clouds are recovered using epipolar geometry and triangulation principles. The depth map is obtained by calculating the disparity of the matched images, and the dense point cloud is fused. The plant model of the whole rapeseed plant in the silique stage is reconstructed based on the structure-from-motion (SfM) algorithm, and the background is removed by using the passthrough filter. The downsampled 3D point cloud data is processed by the DGCNN network, and the point cloud is divided into two categories: sparse rapeseed canopy siliques and rapeseed stems. The sparse canopy siliques are then segmented from the original whole rapeseed siliques point cloud using the sparse-dense point cloud mapping method, which can effectively save running time and improve efficiency. Finally, Euclidean clustering segmentation is performed on the rapeseed canopy siliques, and the RANSAC algorithm is used to perform line segmentation on the connected siliques after clustering, obtaining the three-dimensional spatial position of each silique and counting the number of siliques. The proposed method was applied to identify 1457 siliques from 12 rapeseed plants, and the experimental results showed a recognition accuracy greater than 97.80%. The proposed method achieved good results in rapeseed silique recognition and provided a useful example for the application of deep learning networks in dense 3D point cloud segmentation.

A copper foam-based surface-enhanced Raman scattering substrate for glucose detection.

Raman spectroscopy can quickly achieve non-destructive, qualitative and quantitative detection, and analysis the molecular structure of substances. Herein, a facile and low-cost method for preparation of highly sensitivity SERS substrates was implemented through the displacement reaction of copper foam immersed in AgNO3 ethanol solution. Due to the 3D structure of copper film and homogenous displacement, the Ag-Cu substrate showed high performance SERS enhancement (1.25 × 107), and the lowest detection concentration for R6G reached 10-10 Mol/L. For glucose detection, mixed decanethiol (DT)/mercaptohexanol (MH) interlayer was used to enable glucose attach to the substrate surface, and the limit of detection reached to 1 uM/L. SERS substrate makes the Ag-Cu SERS substrate promising for biological applications.

Fertilization depth effect on mechanized direct-seeded winter rapeseed yield and fertilizer use efficiency.

BACKGROUND: Deep fertilization is effective for improving crop yield and fertilizer use efficiency. However, its impact on mechanized direct-seeded rapeseed and the optimal fertilization depth are poorly understood. A field experiment was conducted to evaluate the fertilization depth effect on mechanized direct-seeded rapeseed growth. Five treatments were designed: surface broadcast fertilizer, no fertilization, and fertilizer banded placement at soil depths of 5 (D5), 10 (D10), and 15 cm (D15). RESULTS: Compared with surface broadcast fertilizer, deep fertilization generally increased seed yield and partial factor productivity by 11.0%, agronomic efficiency (AE) by 22.7%, and recovery efficiency (RE) by 79.2% due to the increase of root mass density (16.8%), plant height (8.6%), height of the first branch (10.6%), stem diameter (22.4%), shoot biomass (16.1%), and shoot nitrogen (35.7%), phosphorus (29.7%), and potassium (26.2%) uptake. D10 had the highest seed yield, oil yield, fertilizer use efficiency, and economic benefits at different fertilization depth treatments. Compared with D5 and D15 respectively, D10 increased seed yield by 5.4% and 46.0%, oil yield by 7.7% and 50.5%, partial factor productivity by 5.4% and 46.0%, AE by 9.0% and 99.5%, RE of nitrogen by 48.9% and 34.9%, RE of phosphorus by 83.1% and 38.0%, and RE of potassium by 57.5% and 32.5%. The economic benefits of D10 were CNY 867.31 ha-1 and CNY 4864.23 ha-1 higher than D5 and D15 respectively. CONCLUSION: Considering rapeseed growth and its economic benefits, this study shows that 10 cm is an appropriate placement depth with regard to mechanized direct-seeded winter rapeseed production. © 2022 Society of Chemical Industry.

A flexible and stretchable photonic crystal sensor for biosensing and tactile sensing.

Biosensing and tactile sensing are considered to be essential functions for intelligent diagnostic medical robot. In this paper, biosensing and tactile sensing had been demonstrated with a single photonic crystal structure. The flexible and stretchable photonic crystal structure consists of PDMS as the flexible substrate and TiO2 as the guided layer, and the nanograting structure was realized by nanoreplica molding. For biosensing experiment, a sensitivity of 93 nm/RIU is verified with ambient environment RI variance simulation results. For tactile sensing experiment, the highest resolution for strain sensing is 0.1%, and the minimum detected scale of the grating period variation is 0.1 nm. The TiO2/PDMS structure based flexible and stretchable photonic crystal sensor demonstrates highly sensitivity and potentially cost effective for biosensing and tactile sensing, and it is promising in the area of intelligent diagnostic medical robot.

A nanograting-based flexible and stretchable waveguide for tactile sensing.

Based on the related characteristics of optical waveguide and flexible optical materials, a flexible and stretchable optical waveguide structure oriented to tactile perception is proposed. The sensing principle of optical waveguide is based on mechanical deformation caused by output light loss. It overcomes the shortcomings of traditional optical waveguide devices, which are unable to conform to irregular surface. The flexible and stretchable optical waveguide is fabricated with nanoreplica molding method, and it has been applied to the measurement of pressure and strain in the field of tactile sensing. The flexible and stretchable optical waveguide had a strain detection range of 0 to 12.5%, and the external force detection range is from 0 to 23 × 10-3 N.

Response of area- and yield-scaled N2 O emissions from croplands to deep fertilization: a meta-analysis of soil, climate, and management factors.

BACKGROUND: Nitrous oxide (N2 O) is an important and persistent greenhouse gas making a significant contribution to global climate change. Deep fertilization has been demonstrated to increase crop yield and nutrient use efficiency by decreasing losses of volatilization and surface runoff. However, N2 O emissions from croplands induced by deep fertilization are variable and mitigation strategies remain uncertain. This study aimed to (i) quantify the response of area-scaled (N2 O emissions) and yield-scaled N2 O emissions (N2 O intensity) from croplands to deep fertilization, and (ii) identify the soil, climate, and management factors that mitigate N2 O emissions and N2 O intensity under deep fertilization. RESULTS: Compared with the control, deep fertilization increased N2 O emissions by 18.6% (P < 0.001) but decreased N2 O intensity by 20.1% (P = 0.018). By adopting deep fertilization, N2 O emissions could be significantly mitigated in rice-paddies soils (-48.8%), with fertilizer depth > 10 cm (-33.0%), and with fertilizer N amount > 200 kg N ha-1 (-8.2%). N2 O intensity following deep fertilization significantly decreased in soils with pH ≤6 (-22.5%), at sites with precipitation of 500-1000 mm (-25.5%), in rice-paddies soils (-53.0%), with the method of mixed fertilizer in the control (-21.2%), and with fertilizer depth > 10 cm (-33.6%). CONCLUSION: This study provides a basis for assessing the effect of deep fertilization on N2 O emissions and provides potential measures to mitigate N2 O emissions associated with deep fertilization practices.

Rapeseed Stand Count Estimation at Leaf Development Stages With UAV Imagery and Convolutional Neural Networks.

Rapeseed is an important oil crop in China. Timely estimation of rapeseed stand count at early growth stages provides useful information for precision fertilization, irrigation, and yield prediction. Based on the nature of rapeseed, the number of tillering leaves is strongly related to its growth stages. However, no field study has been reported on estimating rapeseed stand count by the number of leaves recognized with convolutional neural networks (CNNs) in unmanned aerial vehicle (UAV) imagery. The objectives of this study were to provide a case for rapeseed stand counting with reference to the existing knowledge of the number of leaves per plant and to determine the optimal timing for counting after rapeseed emergence at leaf development stages with one to seven leaves. A CNN model was developed to recognize leaves in UAV-based imagery, and rapeseed stand count was estimated with the number of recognized leaves. The performance of leaf detection was compared using sample sizes of 16, 24, 32, 40, and 48 pixels. Leaf overcounting occurred when a leaf was much bigger than others as this bigger leaf was recognized as several smaller leaves. Results showed CNN-based leaf count achieved the best performance at the four- to six-leaf stage with F-scores greater than 90% after calibration with overcounting rate. On average, 806 out of 812 plants were correctly estimated on 53 days after planting (DAP) at the four- to six-leaf stage, which was considered as the optimal observation timing. For the 32-pixel patch size, root mean square error (RMSE) was 9 plants with relative RMSE (rRMSE) of 2.22% on 53 DAP, while the mean RMSE was 12 with mean rRMSE of 2.89% for all patch sizes. A sample size of 32 pixels was suggested to be optimal accounting for balancing performance and efficiency. The results of this study confirmed that it was feasible to estimate rapeseed stand count in field automatically, rapidly, and accurately. This study provided a special perspective in phenotyping and cultivation management for estimating seedling count for crops that have recognizable leaves at their early growth stage, such as soybean and potato.

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery.

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages.

The yield of mechanically harvested rapeseed (Brassica napus L.) can be increased by optimum plant density and row spacing.

To determine the effects of plant density and row spacing on the mechanical harvesting of rapeseed (Brassica napus L.), field experiments were conducted. Higher plant density produced fewer pods and reduced the yield per plant. Wider row spacing at higher plant densities increased seeds per pod and the 1000-seed weight, resulting in a higher yield per plant. The highest yields were achieved at a density of 45 × 10(4) plants ha(-1) (D45) in combination with 15 cm row spacing (R15) because mortality associated with competition increased as both the plant density and row spacing increased. The leaf area index (LAI) and pod area index (PAI) showed similar relations to the yield per hectare, and they were positively correlated with the percentage of intercepted light, whereas the radiation use efficiency (RUE) was positively correlated with population biomass. Reduced plant height and increased root/shoot ratios led to a decreased culm lodging index. Improved resistance to pod shattering was also observed as plant density and row spacing increased. The angle of the lowest 5 branches decreased as row spacing increased under D30 and D45. All of these structural changes influenced the mechanical harvesting operations, resulting in the highest yield of mechanically harvesting rapeseed under D45R15.