This study developed an immunomagnetic separation microfluidic system for rapid isolation and enrichment of Salmonella Typhimurium. The system consisted of a microfluidic chip, a microcontroller, and an electromagnetic separation and mixing module, which had the functions of electromagnetically driven mixing and magnetic separation. It enabled rapid incubation, isolation and enrichment of immunomagnetic beads with Salmonella Typhimurium. Under the optimal conditions, the rapid capture and separation of Salmonella Typhimurium in milk samples in the concentration range from 2 × 10¹ to 2 × 10⁶ CFU/mL were achieved within 13 min, with capture rates between 33.3% and 67.5%. The limit of detection was 20 CFU/mL. This highly integrated immunomagnetic separation microfluidic system can enrich target bacteria from complex food matrices rapidly and accurately, thus providing an effective solution for the rapid detection of foodborne pathogenic bacteria, which is of great significance in addressing public health problems caused by foodborne diseases.

Fractional vegetation cover (FVC) is an essential agronomic index. Quick and accurate acquisition of coverage is very important for the real-time monitoring of crop growth status in breeding and precision agricultural management. Image background segmentation is the key step in canopy cover extraction, and accurately segmenting the image from the background can effectively reduce the error of canopy cover extraction. The performance of traditional segmentation methods largely depends on the quality of the training data set, and is easily affected by the changes in segmentation thresholds and lightintensity during the different growth periods of crops, resulting in image segmentation method with low accuracy anduniversality, which ultimately leads to the problem of unsatisfactory vegetation cover extraction. In order to solve the above issues, in this study, Gaussian Mixture Model clustering was proposed using the Lab color space features enhanced by CLAHE-SV (contrast limited adaptive histogram equalization-saturation value). Taking rice at the late tillering stage as the object, the visible images of rice at 2, 3, 4, and 5 m height were collected by unmanned aerial vehicle (UAV). The saturation (S) and Value (V) components in HSV color space were enhanced by contrast limited adaptive histogram equalization algorithm (CLAHE). Gaussian Mixture Model (GMM) combined with the a-component of Lab color space was applied to segment the image background and extract the rice coverage, and then compared with the GMM-RGB, GMM-HSV, GMM-Lab, and GMM-a. The results show that the two GMM models with the a-component shared a better performance of segmentation than RGB, HSV, and Lab at different heights, where the accuracy of GMM-CLAHE-SV-a was the best. Compared with the GMM-a, the average overall accuracy of GMM-CLAHE-SV-a with image segmentation increased by 2.16, 1.01, 1.03, and 1.26 percentage points, respectively, while the average Kappa coefficient increased by 0.041 4, 0.017 3, 0.019 0, and 0.022 1, respectively, at heights of 2, 3, 4, and 5 m; The average extraction error of coverage decreased by 8.75, 7.01, 5.93, and 5.34 percentage points, respectively, whereas, the fitting accuracies were improved by 0.096 0, 0.050 2, 0.062 2, and 0.190 6, respectively, at heights of 2, 3, 4, and 5 m. The image segmentation and coverage extraction performance of GMM-CLAHE-SV-a were superior to GMM-a, thus effectively reducing the influence of light intensity and reflection. UAV images can be directly processed without labeling the training set or thresholds. The high universality of the improved model can also be expected to quickly segment the rice pixels and extract the fractional vegetation cover information in complex field environments.

Lodging has posed a serious threat to the yield and quality of rice. This study aims to explore the effect of mechanical and physicochemical properties on the lodging resistance of hybrid japonica rice. The test subjects were selected as the curved panicle hybrid japonica rice Liaoyou 2006 (LY2006), Liaoyou 5218 (LY5218), and Liaoyou 5273 (LY5273) in northern China. The control object was selected as the conventional japonica rice Nonglin 313 (NL313(CK)) easy to fall. Then the agronomic characters and microstructure of the plant were measured to establish the mechanical evaluation index for the lodging resistance of japonica hybrid rice, including the maximum bending force, breaking moment, bending section coefficient, single stem weight mass moment, bending strength, Young's elastic modulus and inertia moment, according to the bending and tensile test. Finally, the correlation between the physicochemical and mechanical properties and lodging index was also studied to investigate the effects of mechanical and physicochemical properties on the lodging resistance of hybrid japonica rice under natural cultivation conditions in the field. Four results were obtained. 1) The type of plant was used to improve the structure of rice plants. Thus, ventilation and light transmission were provided for the plants to create a microclimate, thereby strengthening the individual stems and their lodging resistance. The plant height and panicle weight of hybrid japonica rice LY2006, LY5218, and LY5273 were significantly higher than those of conventional japonica rice NL313, whereas, their lodging resistance was greater than that of NL313. Therefore, the shorter the plant height was, and the lighter the panicle was, the stronger the lodging resistance was under suitable cultivation conditions. The structure of the rice plant type was optimized for the unity of high-stem and high-yield. 2) The length of the basal internodes and leaf sheaths of hybrid japonica rice shared a significant impact on the lodging resistance. The lengths of the second and third internodes of hybrid japonica rice LY2006, LY5218, and LY5273 were significantly lower than those of conventional japonica rice NL313, while the lengths of the second and third leaf sheaths were significantly higher. Therefore, the short basal internodes and the long leaf sheaths effectively enhanced the lodging resistance of rice plants. 3) The lodging resistance depended on the thickness of cells and tissues, the area of vascular bundles, as well as the content of cellulose, lignin, and potassium in the stems of hybrid japonica rice. The hybrid japonica rice LY2006, LY5218, and LY5273 presented the strong lodging resistance, indicating the relatively larger cell and tissue thickness, vascular bundle area, as well as the higher content of cellulose, lignin, and potassium. The cultivation and genetic regulation were optimized during rice growth. 4) The mechanical properties of hybrid japonica rice stems also played a decisive role in the lodging resistance of rice plants. The breaking resistance, bending moment, bending section coefficient, single stem weight mass moment, bending strength, Young's modulus, and inertia moment were used as the evaluation indexes for the lodging resistance of hybrid japonica rice. It was found that there was a significant positive correlation between lodging resistance and the maximum breaking resistance, bending moment, bending section coefficient, bending strength, and Young's modulus (P<0.01). There was a significant negative correlation between single stem weight mass moment and inertia moment (P<0.01). The finding can provide comprehensive data support and theoretical support to improve the lodging resistance varieties and agronomic traits in northern japonica hybrid rice.

Phosphorus plays a vital role in the growth and development of rice in the cold northern regions, affecting the yield and quality of rice. The phosphorus content of leaves can indicate the nutritional status of rice. Rapid and accurate acquisition of the phosphorus content in leaves is the basis for ensuring healthy rice growth and maintaining stable and high rice yield. Hyperspectral technology can reflect the shape of rice leaves and then evaluate the phosphorus content in the leaves, so hyperspectral technology has the potential to estimate the phosphorus content in plant leaves quickly and accurately. The hyperspectral data of the rice leaves were pretreated using the SG smoothing method. The spectral characteristics of pretreated spectral data were extracted using principal component analysis (PCA) and linear discriminant analysis (LDA). Extreme learning machine (ELM) and Bat algorithm optimized extreme learning machine (BA-ELM) were constructed to retrieve the phosphorus content in rice leaves. The results show that there are seven feature vectors produced by the two methods, and the feature vectors selected by the two methods are used as inputs, respectively. The verification sets R² and RMSE of the two models constructed using the feature reflectivity chosen by the LDA algorithm as input were between 0.603 and 0.604, and 0.025 and 0.032, respectively. Under the condition of the same inversion model, the model constructed by using the reflectivity of the features selected by the PCA algorithm as input has a better prediction effect, and the verification set R² of the two models was between 0.685-0.765, and RMSE was between 0.022-0.038. In addition, when using the features selected by these two algorithms to model, comparing the prediction results of the two models, it was found that the accuracy of the BA-ELM was higher than that of ELM. Its determination coefficient R² and RMSE of the verification set were 0.765 and 0.022, respectively. Because of this, the ELM optimized by principal component analysis and BA has certain advantages in the hyperspectral inversion of phosphorus content in rice leaves in cold regions, and can provide some reference for rapid and accurate detection of phosphorus content in rice leaves.