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Landslide susceptibility maps (LSMs) are valuable tools typically used by local authorities for land use management and planning activities, supporting decision-makers in urban and infrastructure planning. To address this, we proposed a refined method for landslide susceptibility assessment, which comprehensively considered both static and dynamic factors. Neural network methods were used for susceptibility analysis. Land use and land cover (LULC) change and InSAR deformation were then integrated into the traditional susceptibility zoning to obtain a refined susceptibility map with higher accuracy. Validation was conducted on the improved landslide susceptibility map using site landslide data. The results showed that the LULC were proven to be the core driving factors for landslide occurrence in the study area. The GRU model achieved the highest model performance (AUC = 0.886). The introduction of InSAR surface deformation and land use and land cover change data could rationalize the inappropriateness of traditional landslide susceptibility zoning, correcting the false positive and false negative areas in the traditional landslide susceptibility map caused by human activities. Ultimately, 12.25% of the study area was in high-susceptibility zones, with 3.10% of false positive and 0.74% of false negative areas being corrected. The proposed method enabled refined analysis of landslide susceptibility over large areas, providing technical support and disaster prevention and mitigation references for geological hazard susceptibility assessment and land management planning. [ABSTRACT FROM AUTHOR]

When interpreting magnetotelluric (MT) data, because of the inherent anisotropy of the earth, considering electrical anisotropy is crucial. Accordingly, using the edge-based finite element method, we calculated the responses of MT data for electrical isotropic and anisotropic models, and subsequently used the anisotropy index and polar plot to depict MT responses. High values of the anisotropy index were mainly yielded at the boundary domains of anomalous bodies for isotropy cases because the conductive differences among isotropic anomalous bodies or among anomalous bodies and background earth can be regarded as macro-anisotropy. However, they only appeared across anomalous bodies in the anisotropy cases. The anisotropy index can directly differentiate isotropy from anisotropy but exhibits difficulty in reflecting the azimuth of the principal conductivities. For the isotropy cases, polar plots are approximately circular and become curves with a big ratio of the major axis to minor axis, such as an 8-shaped curve for the anisotropic earth. Furthermore, the polar plot can reveal the directions of principal conductivities. However, distorted by anomalous bodies, polar plots with a large ratio of the major axis to minor axis occur in isotropic domains around the anomalous bodies, which may lead to the misinterpretation of these domains as anisotropic earth. Therefore, combining the anisotropy index with a polar plot facilitates the identification of the electrical anisotropy. [ABSTRACT FROM AUTHOR]

The detection and control of fungal spores in greenhouse crops are important for stabilizing and increasing crop yield. At present, the detection of fungal spores mainly adopts the method of combining portable volumetric spore traps and microscope image processing. This method is problematic as it is limited by the small field of view of the microscope and has low efficiency. This study proposes a rapid detection method for fungal spores from greenhouse crops based on CMOS image sensors and diffraction fingerprint feature processing. We built a diffraction fingerprint image acquisition system for fungal spores of greenhouse crops and collected diffraction fingerprint images of three kinds of fungal spores. A total of 13 diffraction fingerprint features were selected for the classification of fungal spores. These 13 characteristic values were divided into 3 categories, main bright fringe, main dark fringe, and center fringe. Then, these three features were calculated to obtain the Peak to Center ratio (PCR), Valley to Center ratio, and Peak to Valley ratio (PVR). Based on these features, logistics regression (LR), K nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classification models were built. The test results show that the SVM model has a better overall classification performance than the LR, KNN, and RF models. The average accuracy rate of the recognition of three kinds of fungal spores from greenhouse crops under the SVM model was 92.72%, while the accuracy rates of the LR, KNN, and RF models were 84.97%, 87.44%, and 88.72%, respectively. The F1-Score value of the SVM model was higher, and the overall average value reached 89.41%, which was 11.12%, 7.18%, and 5.57% higher than the LR, KNN, and RF models, respectively. Therefore, the method proposed in this study can be used for the remote identification of three fungal spores which can provide a reference for the identification of fungal spores in greenhouse crops and has the advantages of low cost and portability. [ABSTRACT FROM AUTHOR]

Abiotic and biotic stresses both decrease the quality and quantity of cultivated plants. In this study, in order to see the responses of cucumber plants to drought stress and cucumber downy mildew infection, downy mildew infestation at different two levels, B1 (disease infestation) and B2 (no disease infestation), along with three fertigation requirement levels, full fertigation T1, moderate nutrient solution deficit T2 and severe nutrient solution deficit T3, were applied in a greenhouse. Thus, six treatments, i.e., B1T1, B1T2, B1T3, B2T1, B2T2 and B2T3, were set. The leaf gas-exchange parameters were significantly increased under CK (control experiment, B2T1: no disease infestation and full irrigation) treatment, and leaf photosynthesis rate, transpiration rate and stomatal conductance were significantly decreased under the B1T1 treatment. Leaf intercellular CO2 concentration was significantly increased under B1T1 treatment. Leaf photosynthesis rate, transpiration rate, intercellular CO2 concentration and stomatal conductance were significantly decreased under B1T2, B1T3, B2T2 and B2T3 treatments. Compared with treatment CK (B2T1), the plant height of cucumber under B1T1, B1T2, B1T3, B2T2 and B2T3 treatments decreased by 11.41%, 19.05%, 27.48%, 7.55% and 10.62%, respectively; the stem diameter of cucumber under B1T1, B1T2, B1T3, B2T2 and B2T3 treatments decreased by 5.70%, 13.45%, 23.03%, 9.46% and 15.74%, respectively; and leaf area of cucumber under B1T1, B1T2, B1T3, B2T2 and B2T3 treatments decreased by 22.79%, 38.68%, 58.28%, 13.76% and 29.96%, respectively. The root–shoot ratio of cucumber under B1T1, B1T2, B1T3, B2T1, B2T2 and B2T3 treatments was 3.16%, 2.99%, 4.11%, 3.92%, 3.13% and 3.63%, respectively. The root–shoot ratio of cucumber was the highest under the B1T3 treatment. [ABSTRACT FROM AUTHOR]