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This paper proposes a novel deep learning based method towards the identification of a pistachio tree cultivar from its image.

The investigated scope of this study includes Iranian commercial pistachios (Jumbo, Long, Round and Super long) trees. Effective use of high-resolution images with standard deep models is addressed in this study. A novel image patches extraction method is also used to boost the number of samples and dataset augmentation. In the proposed method, handcrafted ORB features are used to detect and extract patches which may contain identifiable information. An innovative algorithm is proposed for searching and extracting these patches. After extracting patches from initial images, a Convolutional Neural Network, named EfficientNet-B1, was fine-tuned on it. In the testing phase, several patches were extracted from the prompted image using the ORB-based method, and the results of their prediction were consolidated. In this method, patch prediction scores were in descending order, sorted by the highest score in a list, and finally, the average of a few list tops was calculated and the final decision was made.

Examining the proposed method on the test images led to an achievement of a recognition rate of 97.2% accuracy. Investigation of decision-making in the test dataset could reveal that this method outperformed human experts.

Cultivar identification using deep learning methods, due to their high recognition speed, lack of specialist requirement, and independence from human decision-making error is considered as a breakthrough in horticultural science. Variety cultivars of pistachio trees possess variant characteristics or traits, accordingly recognising cultivars is crucial to reduce the costs, prevent damages and harvest the optimal yields.

It can be said that the most important function of the health system is to provide health-care services. This directly affects society’s health and justice. To the best of the authors’ knowledge, this study is the first study that has meta-analyzed the percentage of health services utilization among Iranians to answer this question “How much is the percentage of health-care services utilization in Iran?”

This study is a systematic review and meta-analysis conducted in 2020. The literature review was done by searching the databases of Google Scholar, Scopus, PubMed, Web of Science, Science Direct, SID, Magiran and Irandoc from January 2010 to August 2020. Pooled utilization percentage (UP) was estimated by STATA 15 through random-effects meta-analysis with 95% confidence interval. The I² statistic was used to investigate the possibility of heterogeneity of articles (I² ≥ 50% indicate heterogeneity). A forest plot was applied to report the results. A funnel plot exploited due to publication bias assessment.

Approximately 22% of the reviewed articles have been done at the national level of Iran. Most studies have examined the utilization of outpatient and inpatient services. Based on the random effects method, the pooled value of UP was obtained at 47.82%, (95% CI: 39.33–56.31). Also, the pooled percentage of utilization was statistically significant (Test of UP = 0, Z = 456.44, p = 0.000).

Policymakers can improve the level of utilization from health services by expanding universal coverage, expanding insurance coverage and removing direct and indirect barriers.

To the best of the authors’ knowledge, this is the first systematic review and meta-analysis on health services utilization in Iran that completely considers the utilization rate in all health services and states the practical solutions to cope with utilization deficiencies and barriers.