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Obtaining accurate and real-time spatial distribution information regarding crops is critical for enabling effective smart agricultural management. In this study, innovative decision fusion strategies, including Enhanced Overall Accuracy Index (E-OAI) voting and the Overall Accuracy Index-based Majority Voting (OAI-MV), were introduced to optimize the use of diverse remote sensing data and various classifiers, thereby improving the accuracy of crop/vegetation identification. These strategies were utilized to integrate crop/vegetation classification outcomes from distinct feature sets (including Gaofen-6 reflectance, Sentinel-2 time series of vegetation indices, Sentinel-2 time series of biophysical variables, Sentinel-1 time series of backscatter coefficients, and their combinations) using distinct classifiers (Random Forests (RFs), Support Vector Machines (SVMs), Maximum Likelihood (ML), and U-Net), taking two grain-producing areas (Site #1 and Site #2) in Haixi Prefecture, Qinghai Province, China, as the research area. The results indicate that employing U-Net on feature-combined sets yielded the highest overall accuracy (OA) of 81.23% and 91.49% for Site #1 and Site #2, respectively, in the single classifier experiments. The E-OAI strategy, compared to the original OAI strategy, boosted the OA by 0.17% to 6.28%. Furthermore, the OAI-MV strategy achieved the highest OA of 86.02% and 95.67% for the respective study sites. This study highlights the distinct strengths of various remote sensing features and classifiers in discerning different crop and vegetation types. Additionally, the proposed OAI-MV and E-OAI strategies effectively harness the benefits of diverse classifiers and multisource remote sensing features, significantly enhancing the accuracy of crop/vegetation classification. [ABSTRACT FROM AUTHOR]

Few studies have been conducted to explore medication non-adherence in persons with schizophrenia (PWS) and its influencing factors in rural China. This study aimed to investigate the medication non-adherence and its influencing factors among PWS in rural China. A total of 269 PWS and their family caregivers in Xinjin district, Chengdu, China were investigated on medication adherence and related factors. Logistic regression was employed to identify the influencing factors. The results showed that 37.6% of PWS had medication non-adherence. PWS living with family caregivers had significantly lower rate of medication non-adherence (34.7%) than those not living with family caregivers (60.0%) (p < 0.01). Family caregivers' affiliate stigma of mental illness, knowledge of mental illness and self-esteem were significantly related to patients' medication non-adherence (p < 0.05). PWS' employment status, living with family caregiver, present mental status and social support were significantly related to medication adherence. This study shows medication non-adherence is severe among PWS in rural China. Both patient- and family-related factors affect patients' medication adherence seriously. Except improving patients' treatment and mental status, development of family caregiving, social support network and intervention on reducing stigma of mental illness should be crucial for enhancing PWS' medication adherence. [ABSTRACT FROM AUTHOR]