Search results for “CNNS

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2 articles

Relationship Between Household Economic Status and Childhood Micronutrient Deficiency in India: An Evidence from a National Level Representative Survey

Jun 2026 DOI 10.14302/issn.2379-7835.ijn-25-5909

The high prevalence of anemia among children and adolescents in India is still an overwhelming problem. Not only that, there is also a considerable deficiency of various micronutrients such as Vitamin A, Vitamin B12, Vitamin D, serum ferritin, Zinc and Folate etc. in children. These micronutrients have several functional roles for the normal growth and development of children. Unfortunately, recent studies on public health and nutrition intervention have so far focused less on these micronutrition and more on anemia and nutrition. Data for this study obtained from the Comprehensive National Nutrition Survey (CNNS 2016-18), a nationally representative survey covering different age group. Specifically, it includes information on 9767 children aged 1-4 years. Out of these surveyed children biomarker data for hemoglobin, serum ferritin, zinc, folate, vitamin A, vitamin B12 and Vitamin D were collected from 8242 children. Micronutrient deficiencies were identified based on WHO and other established cut-off criteria. Wealth quintiles were computed to identify household economic inequality. The prevalence of anemia at the national level was 40.7%. Among micronutrients, folate (22.9%) and zinc (18.7%) deficiencies were most commonly observed, followed by vitamins A (18.3), vitamin B12 (13.8%) and vitamin D (14.0%). Iron deficiency as measured by ferritin was present in 31.6% of children. It is alarming that nearly one in three children (32.8%) suffer from deficiencies in two or more micronutrients. Clear socioeconomic disparities were observed for all micronutrient deficiencies (MND); children in the poorest groups had significantly higher levels of micronutrient deficiencies than children in the richest groups. Among the states, Gujarat and Madhya Pradesh had the highest overall micronutrient deficiencies, while West Bengal and Kerala had the lowest. This clearly shows that a large proportion of preschool children in India suffer from anemia and MND, with the prevalence being even more alarming in lower socio-economic settings. This study suggests that there is a need to move beyond single-nutrient interventions and implement comprehensive, multi-micronutrient supplementation or food fortification strategies seamlessly into the existing national health and nutrition programs.

Precision Agriculture Open Access

Automated Grassweed Detection in Wheat Cropping System: Current Techniques and Future Scope

May 2024 DOI 10.14302/issn.2998-1506.jpa-24-5058

Wheat is a staple grain crop in the United States and around the world. Weed infestation, particularly grass weeds, poses significant challenges to wheat production, competing for resources and reducing grain yield and quality. Effective weed management practices, including early identification and targeted herbicide application are essential to avoid economic losses. Recent advancements in unmanned aerial vehicles (UAVs) and artificial intelligence (AI), offer promising solutions for early weed detection and management, improving efficiency and reducing negative environment impact. The integration of robotics and information technology has enabled the development of automated weed detection systems, reducing the reliance on manual scouting and intervention. Various sensors in conjunction with proximal and remote sensing techniques have the capability to capture detailed information about crop and weed characteristics. Additionally, multi-spectral and hyperspectral sensors have proven highly effective in weed vs crop detection, enabling early intervention and precise weed management. The data from various sensors consecutively processed with the help of machine learning and deep learning models (DL), notably Convolutional Neural Networks (CNNs) method have shown superior performance in handling large datasets, extracting intricate features, and achieving high accuracy in weed classification at various growth stages in numerous crops. However, the application of deep learning models in grass weed detection for wheat crops remains underexplored, presenting an opportunity for further research and innovation. In this review we underscore the potential of automated grass weed detection systems in enhancing weed management practices in wheat cropping systems. Future research should focus on refining existing techniques, comparing ML and DL models for accuracy and efficiency, and integrating UAV-based mapping with AI algorithms for proactive weed control strategies. By harnessing the power of AI and machine learning, automated weed detection holds the key to sustainable and efficient weed management in wheat cropping systems.

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