Optimizing Deficit Irrigation Scheduling Under Shallow Groundwater Conditions in Lower Reaches of Amu Darya River Basin

Water demand for irrigated agriculture is increasing against limited availability of fresh water resources in the lower reaches of the Amu Darya River e.g., Khorezm region of Uzbekistan. Future scenarios predict that Khorezm region will receive fewer water supplies due to climate change, transboundary conflicts and hence farmers have to achieve their yield targets with less water. We conducted a study and used AquaCrop model to develop the optimum and deficit irrigation schedule under shallow groundwater conditions (1.0–1.2 m) in the study region. Cotton being a strategic crop in the region was used for simulations. Capillary rise substantially contributes to crop-water requirements and is the key characteristic of the regional soils. However, AquaCrop does not simulate capillary rise contribution, thereby HYDRUS-1D model was used in this study for the quantification of capillary rise contribution. Alongside optimal irrigation schedule for cotton, deficit strategies were also derived in two ways: proportional reduction from each irrigation event (scenario-A) throughout the growth period as well as reduced water supply at specific crop growth stages (scenario-B). For scenario-A, 20, 40, 50 and 60 % of optimal water was deducted from each irrigation quota whereas for scenario-B irrigation events were knocked out at different crop growth stages (stage 1(emergence), stage 2 (vegetative), stage 3 (flowering) and stage 4 (yield formation and ripening)). For scenario-A, 0, 14, 30 and 48 % of yield reduction was observed respectively. During stress at the late crop development stage, a reduced water supply of 12 % resulted in a yield increase of 8 %. Conversely, during stress at the earlier crop development stage, yield loss was 17–18 %. During water stress at the late ripening stage, no yield loss was observed. Results of this study provide guidelines for policy makers to adopt irrigation schedule depending upon availability of irrigation water.     

A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System

The outbreak of chronic diseases such as COVID-19 has made a renewed call for providing urgent healthcare facilities to the citizens across the globe. The recent pandemic exposes the shortcomings of traditional healthcare system, i.e., hospitals and clinics alone are not capable to cope with this situation. One of the major technology that aids contemporary healthcare solutions is the smart and connected wearables. The advancement in Internet of Things (IoT) has enabled these wearables to collect data on an unprecedented scale. These wearables gather context-oriented information related to our physical, behavioural and psychological health. The big data generated by wearables and other healthcare devices of IoT is a challenging task to manage that can negatively affect the inference process at the decision centres. Applying big data analytics for mining information, extracting knowledge and making predictions/inferences has recently attracted significant attention. Machine learning is another area of research that has successfully been applied to solve various networking problems such as routing, traffic engineering, resource allocation, and security. Recently, we have seen a surge in the application of ML-based techniques for the improvement of various IoT applications. Although, big data analytics and machine learning are extensively researched, there is a lack of study that exclusively focus on the evolution of ML-based techniques for big data analysis in the IoT healthcare sector. In this paper, we have presented a comprehensive review on the application of machine learning techniques for big data analysis in the healthcare sector. Furthermore, strength and weaknesses of existing techniques along with various research challenges are highlighted. Our study will provide an insight for healthcare practitioners and government agencies to keep themselves well-equipped with the latest trends in ML-based big data analytics for smart healthcare.