The views of Dr. Vedprakash Mishra, Chairman, Academic Committee of the Medical Council of India, New Delhi

Mishra, Vedprakash

doi:10.1016/j.cmrp.2016.05.012

Cited by 3 publications

(5 citation statements)

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“…auriculiformis, B. flabellifer, C. nucifera, C. exarillata, D. pentagyna, E. guineensis, H. ponga, M. indica, P. pterocarpum, P. pauciflorum, S. oleosa, T. paniculata, T. connaroides and M. calabura. This is in accordance with Mishra (1995), Sharma (2011) and Shubharani et al (2012) where they reported that bees highly prefer trees rather than shrubs and herbs. Apart from this, the height of the plant is also not a barrier to bees for collecting pollen and nectar as they are found to visit both the tall trees (C. nucifera) and small plants (M. pudica).…”

Section: Pollen Type Pollen Morphologysupporting

confidence: 89%

Melissopalynology studies on the Indian honey bee (Apis cerana indica Fab.) in southern Kerala

Aswini,

Amritha,

Devanesan

2015

Entomon

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Melissopalynology studies of the Indian honey bee (Apis cerana indica Fab.) in southern Kerala was undertaken to study the potential pollen and nectar sources of Indian honey bee across the seasons in southern Kerala. A detailed characterization of all the honey and pollen samples showed the presence of 69 different pollen types; of which twenty four pollen types were identified up to species level. They were distributed among 19 families of these, the pollen types from the families of Arecaceae (3) and Asteraceae (3) were best represented in honey and pollen load samples. Honey bees were found to collect pollen from the tall trees (Cocos nucifera) as well as the small plants (Mimosa pudica) irrespective of their height.

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Section: Pollen Type Pollen Morphologysupporting

confidence: 89%

Melissopalynology studies on the Indian honey bee (Apis cerana indica Fab.) in southern Kerala

Aswini,

Amritha,

Devanesan

2015

Entomon

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“…Weeds can be generally classified as annual, biennial, and perennial [ 3 , 4 , 5 ]. Annual weeds germinate, bloom, and die within one year, while biennial weeds have a life cycle of two years, with germination and blooming happening in the first year and dying out in the second year.…”

Section: Background Knowledgementioning

confidence: 99%

“…Another serious problem that plagues crop farming is the growth of weeds which leads to significant crop wastage annually. Hence, weed management and removal practices have been adopted for several decades to control weed growth [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…For several decades weeds have been managed, detected, and controlled manually [ 3 , 4 , 5 ]. The most common method of weed detection is manual surveillance by hiring crop scouts or by tasking crop farmers to do the same, which is expensive, difficult to manage, and infeasible to execute in unfavorable weather conditions Scouts only work on a sample of the field and have to follow a pre-determined randomized pattern (e.g., zigzag).…”

Section: Introductionmentioning

confidence: 99%

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Weed Detection Using Deep Learning: A Systematic Literature Review

Murad

Mahmood

Forkan

et al. 2023

Sensors

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Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area.

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“…Insurance is defined as cooperative device to spread the loss caused by a particular risk over a number of persons who are exposed to it and who agree to ensure themselves against the risk. It is referred as a social device to accumulate funds to meet the uncertain losses arising through a certain risk to a person insured against the risk (Mishra, 2004). Insurance is the way of reducing uncertainty of occurrence of an event.…”

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confidence: 99%

Determinants of Demand for Life Insurance Policy in Kapilvastu District

Jnawali

Jaiswal

2022

Lumbini J. Bus. & Econ.

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This study aims to achieve the key objectives of the factors affecting the demand for life insurance in Kapilvastu district. It is a novel study in the field of the insurance industry in the context of Kapilvastu district, as this study incorporates socio-demographic variables, saving motives, and financial literacy as a determinant of demand for life insurance. Primary data has been collected through convenience sampling from a sample of 384 individuals; the study has analyzed the influence of socio-demographic variables, saving motives, and financial literacy on life insurance demand. To determine the relationship between the demographic factors and life insurance demand way ANOVA tests have been conducted. Similarly, to test the association between categorical variables chi-square test has been used. The relationship between financial literacy and saving motives (precautionary, bequest, life cycle, and wealth accumulation motives) with life insurance demand is then analyzed using multiple regression. Results have confirmed that among all the socio-demographic variables, the number of dependents, level of education, and level of income have a significant effect on the demand for life insurance. Similarly, bequest motives and financial literacy were positive contributors to life insurance policy demand. However, it is worth noting that although marital status has an insignificant effect on life insurance policy demand but unmarried individuals still tend to have more life insurance in comparison to married and widowed respondents. Other determinants of saving motives like precautionary motives, life cycle motives, and wealth accumulation motives have an insignificant effect on life insurance demand.

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The views of Dr. Vedprakash Mishra, Chairman, Academic Committee of the Medical Council of India, New Delhi

Cited by 3 publications

References 0 publications

Melissopalynology studies on the Indian honey bee (Apis cerana indica Fab.) in southern Kerala

Melissopalynology studies on the Indian honey bee (Apis cerana indica Fab.) in southern Kerala

Weed Detection Using Deep Learning: A Systematic Literature Review

Determinants of Demand for Life Insurance Policy in Kapilvastu District

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