Airborne crop diseases cause great losses to agricultural production and can affect people’s physical health. Timely monitoring of the situation of airborne disease spores and effective prevention and control measures are particularly important. In this study, a two-stage separation and enrichment microfluidic chip with arcuate pretreatment channel was designed for the separation and enrichment of crop disease spores, which was combined with micro Raman for Raman fingerprinting of disease conidia and quasi identification. The chip was mainly composed of arc preprocessing and two separated enriched structures, and the designed chip was numerically simulated using COMSOL multiphysics5.5, with the best enrichment effect at W2/W1 = 1.6 and W4/W3 = 1.1. The spectra were preprocessed with standard normal variables (SNVs) to improve the signal-to-noise ratio, which was baseline corrected using an iterative polynomial fitting method to further improve spectral features. Raman spectra were dimensionally reduced using principal component analysis (PCA) and stability competitive adaptive weighting (SCARS), support vector machine (SVM) and back-propagation artificial neural network (BPANN) were employed to identify fungal spore species, and the best discrimination effect was achieved using the SCARS-SVM model with 94.31% discrimination accuracy. Thus, the microfluidic-chip- and micro-Raman-based methods for spore capture and identification of crop diseases have the potential to be precise, convenient, and low-cost methods for fungal spore detection.

Land preparation is an important component of fragrant rice production. However, the effect of tillage on fragrant rice production is unclear, especially regarding the biosynthesis of 2-acetyl-1-pyrroline (2-AP), which is the main compound of the unique aroma of fragrant rice. This study aimed to explore 2-AP biosynthesis in fragrant rice under different tillage regimes. Three tillage methods were applied in the present study: conventional rotary tillage (CK) as the control, plough tillage (PT), and no-tillage (NT). Compared with CK, the PT treatment increased 2-AP content in grain, upregulated the activity of ornithine aminotransferase (OAT) and increased contents of 1-pyrroline and pyrroline-5-carboxylic acid (P5C). Furthermore, the PT treatment increased the grain yield and nitrogen accumulation of fragrant rice. Meanwhile, the 2-AP content in the grain produced under the NT treatment was significantly higher than that in the grain produced under both the PT and CK treatments due to the enhancement of proline content and the activities of proline dehydrogenase (PDH) and △1-pyrroline-5-carboxylic acid synthetase (P5CS). However, the present study observed that the overall production of fragrant rice under NT conditions was inferior due to lower yield, nitrogen accumulation, and anti-oxidative enzymatic activities. Moreover, the organic matter content and soil microorganism quantity increased due to PT and NT treatments. Compared to CK, PT improved fragrant rice grain yield and nitrogen accumulation and induced an increase in OAT activity and led to an increase in 2-AP concentration. No-tillage also produced increased 2-AP content in grain by enhancing PDH and P5CS activities but limited yields and nitrogen accumulation in fragrant rice.

As rice is one of the world’s most important food crops, protecting it from fungal diseases is very important for agricultural production. At present, it is difficult to diagnose rice fungal diseases at an early stage using relevant technologies, and there are a lack of rapid detection methods. This study proposes a microfluidic chip-based method combined with microscopic hyperspectral detection of rice fungal disease spores. First, a microfluidic chip with a dual inlet and three-stage structure was designed to separate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores in air. Then, the microscopic hyperspectral instrument was used to collect the hyperspectral data of the fungal disease spores in the enrichment area, and the competitive adaptive reweighting algorithm (CARS) was used to screen the characteristic bands of the spectral data collected from the spores of the two fungal diseases. Finally, the support vector machine (SVM) and convolutional neural network (CNN) were used to build the full-band classification model and the CARS filtered characteristic wavelength classification model, respectively. The results showed that the actual enrichment efficiency of the microfluidic chip designed in this study on Magnaporthe grisea spores and Ustilaginoidea virens spores was 82.67% and 80.70%, respectively. In the established model, the CARS-CNN classification model is the best for the classification of Magnaporthe grisea spores and Ustilaginoidea virens spores, and its F1-core index can reach 0.960 and 0.949, respectively. This study can effectively isolate and enrich Magnaporthe grisea spores and Ustilaginoidea virens spores, providing new methods and ideas for early detection of rice fungal disease spores.

The timely monitoring of airborne crop fungal spores is important for maintaining food security. In this study, a method based on microfluidic separation and enrichment and AC impedance characteristics was proposed to detect spores of fungal pathogens that cause diseases on crops. Firstly, a microfluidic chip with tertiary structure was designed for the direct separation and enrichment of Ustilaginoidea virens spores, Magnaporthe grisea spores, and Aspergillus niger spores from the air. Then, the impedance characteristics of fungal spores were measured by impedance analyzer in the enrichment area of a microfluidic chip. The impedance characteristics of fungal spores were analyzed, and four impedance characteristics were extracted: absolute value of impedance (abs), real part of impedance (real), imaginary part of impedance (imag), and impedance phase (phase). Finally, based on the impedance characteristics of extracted fungal spores, K-proximity (KNN), random forest (RF), and support vector machine (SVM) classification models were established to classify the three fungal spores. The results showed that the microfluidic chip designed in this study could well collect the spores of three fungal diseases, and the collection rate was up to 97. The average accuracy of KNN model, RF model, and SVM model for the detection of three disease spores was 93.33, 96.44 and 97.78, respectively. The F1-Score of KNN model, RF model, and SVM model was 90, 94.65, and 96.18, respectively. The accuracy, precision, recall, and F1-Score of the SVM model were all the highest, at 97.78, 96.67, 96.69, and 96.18, respectively. Therefore, the detection method of crop fungal spores based on microfluidic separation, enrichment, and impedance characteristics proposed in this study can be used for the detection of airborne crop fungal spores, providing a basis for the subsequent detection of crop fungal spores.

Several tillage and planting methods have been proposed to enhance the soil bulk density, biological community, and grain yield of rice. In this work, we present the impact of plowing methods with different rice crop establishment approaches, i.e., moldboard plowing with mechanical transplanting (MPMT), rotary tillage with mechanical transplanting (RTMT), moldboard plowing with direct seeding (MPDS), and rotary tillage with direct seeding (RTDS), on soil bulk density, microbial community, enzymatic activities, and grain yield of ratoon rice (RR). The results showed that MPDS improved soil bulk density in 0–30 cm depth in both years and both harvesting times (1H: 1st harvest and 2H: 2nd harvest). The results also showed that microbial community significantly improved under MPDS compared to the other treatments in both years and in 1H and 2H. Additionally, enzymatic activities showed a positive effect under MPDS in both years and in 1H and 2H. MPDS subsequently improved rice grain yield by 18.05% and 17.27% in 2017 (1H and 2H), and 14.86% and 18.64% in 2018 (1H and 2H), respectively. In conclusion, MPDS appears to be a more suitable approach to obtaining high soil eminence and health, as well as sustainable RR production.