Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Sapna Nigam"'
Autor:
Md. Ashraful Haque, Sudeep Marwaha, Chandan Kumar Deb, Sapna Nigam, Alka Arora, Karambir Singh Hooda, P. Lakshmi Soujanya, Sumit Kumar Aggarwal, Brejesh Lall, Mukesh Kumar, Shahnawazul Islam, Mohit Panwar, Prabhat Kumar, R. C. Agrawal
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of ma
Externí odkaz:
https://doaj.org/article/6e06f5391a9447b88d36ca458f56815b
Autor:
Md. Ashraful Haque, Sudeep Marwaha, Alka Arora, Chandan Kumar Deb, Tanuj Misra, Sapna Nigam, Karambir Singh Hooda
Publikováno v:
Frontiers in Plant Science, Vol 13 (2022)
Maydis leaf blight (MLB) of maize (Zea Mays L.), a serious fungal disease, is capable of causing up to 70% damage to the crop under severe conditions. Severity of diseases is considered as one of the important factors for proper crop management and o
Externí odkaz:
https://doaj.org/article/40be5631f75f48b48dbb3e0a9ea11732
Autor:
SAPNA NIGAM, RAJNI JAIN, SUDEEP MARWAHA, ALKA ARORA, VAIBHAV KUMAR SINGH, AVESH KUMAR SINGH, RANJIT KUMAR PAUL, KINGSLY IMMANUELRAJ T
Publikováno v:
The Indian Journal of Agricultural Sciences, Vol 91, Iss 9 (2021)
Plant disease has long been one of the major threats to world food security due to reduction in the crop yield and quality. Accurate and precise diagnosis of plant diseases has been a significant challenge. Cost-effective automated computational syst
Externí odkaz:
https://doaj.org/article/96aa246d150a45babf9d54750452973c
Autor:
SAPNA NIGAM, RAJNI JAIN
Publikováno v:
The Indian Journal of Agricultural Sciences, Vol 90, Iss 2 (2020)
The paper reviews various classification techniques exclusively used for plant disease identification. Early stage plant disease identification is extremely important as that can adversely affect both quality and quantity of crops in agriculture. For
Externí odkaz:
https://doaj.org/article/97c6ec71c03a498a86180f239e0a8b9d
Publikováno v:
Neural Computing and Applications. 35:7407-7421
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbb28e9c5c8151f17d89b85782d31a23
https://doi.org/10.1007/s00521-022-08003-9
https://doi.org/10.1007/s00521-022-08003-9
Autor:
Sapna Nigam, Rajni Jain, Sudeep Marwaha, Alka Arora, Md. Ashraful Haque, Akshay Dheeraj, Vaibhav Kumar Singh
Publikováno v:
Ecological Informatics. 75:102068
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df358b34ab6150586ed514b5b3ceb054
https://doi.org/10.1016/j.ecoinf.2023.102068
https://doi.org/10.1016/j.ecoinf.2023.102068
Autor:
Shbana Begam, Subhrajit Satpathy, Samarth Godara, Sapna Nigam, Akshay Dheeraj, I. Yuvaraj, Md. Ashraful Haque, Sanchita Naha
Publikováno v:
Bhartiya Krishi Anusandhan Patrika.
The next generation sequencing (NGS) technology generates a large amount of genomic data. Bioinformatics has made it a priority to deal with genomic data in all of its forms. The main focus of this article is on the use of data mining in bioinformati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d44df17d7718ff9cf1e49b62b3cb2658
https://doi.org/10.18805/bkap428
https://doi.org/10.18805/bkap428
Autor:
Sapna Nigam, Rajni Jain, Surya Prakash, Sudeep Marwaha, Alka Arora, Vaibhav Kumar Singh, Avesh Kumar Singh, T. L. Prakasha
Publikováno v:
Internet of Things and Connected Technologies ISBN: 9783030945060
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::826935657e4128f4d9159be65e1c71d3
https://doi.org/10.1007/978-3-030-94507-7_18
https://doi.org/10.1007/978-3-030-94507-7_18
Autor:
Md. Ashraful Haque, Sudeep Marwaha, Chandan Kumar Deb, Sapna Nigam, Alka Arora, Karambir Singh Hooda, P. Lakshmi Soujanya, Sumit Kumar Aggarwal, Brejesh Lall, Mukesh Kumar, Shahnawazul Islam, Mohit Panwar, Prabhat Kumar, R. C. Agrawal
Publikováno v:
Scientific reports. 12(1)
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bda3bb81cb6378f3b08211ad1ff86b36
https://pubmed.ncbi.nlm.nih.gov/35428845
https://pubmed.ncbi.nlm.nih.gov/35428845