Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Dharmendra Saraswat"'
Publikováno v:
Smart Agricultural Technology, Vol 5, Iss , Pp 100315- (2023)
Deep learning (DL) advancements have contributed to the success of vision-based tasks for solving real-world problems. DL applications in agriculture are increasing as researchers find it valuable for developing solutions to ensure global food securi
Externí odkaz:
https://doaj.org/article/af740181894044409a2ad9b387de10ae
Publikováno v:
IEEE Access, Vol 11, Pp 9042-9057 (2023)
Identifying corn diseases under field conditions is crucial for implementing effective disease management systems. Deep learning (DL)-based plant disease identification using deep neural networks (DNN) has been successfully implemented in recent year
Externí odkaz:
https://doaj.org/article/e81741e8485f44b0b4f4ef55b92c95d0
Publikováno v:
IEEE Access, Vol 10, Pp 111985-111995 (2022)
Plant diseases lead to severe losses in crop yield worldwide. The conventional approach for diagnosing diseases relies on manual scouting. In recent years, advances in convolutional neural networks have motivated the use of deep learning-based comput
Externí odkaz:
https://doaj.org/article/77fc0ef4b7894657ab86189a0070d4a6
Publikováno v:
Smart Agricultural Technology, Vol 3, Iss , Pp 100108- (2023)
It is important to develop accurate disease management systems to identify and segment corn disease lesions and estimate their severity under complex field conditions. Although deep learning techniques are becoming increasingly popular to identify si
Externí odkaz:
https://doaj.org/article/a2c0376541074ca2934661fba51d0810
Publikováno v:
Smart Agricultural Technology, Vol 3, Iss , Pp 100083- (2023)
Several factors associated with disease diagnosis in plants using deep learning techniques must be considered to develop a robust system for accurate disease management. A considerable number of studies have investigated the potential of deep learnin
Externí odkaz:
https://doaj.org/article/00b09bd1a540404cac36a89fd60bf452
Autor:
Shoobhangi Tyagi, Xiang Zhang, Dharmendra Saraswat, Sandeep Sahany, Saroj Kanta Mishra, Dev Niyogi
Publikováno v:
Earth's Future, Vol 10, Iss 11, Pp n/a-n/a (2022)
Abstract This paper reviews the Flash Drought concept, the uncertainties associated with FD prediction, and the potential of Machine Learning (ML) and Deep learning (DL) for future applications. For this, 121 relevant articles covering different aspe
Externí odkaz:
https://doaj.org/article/26bcc25f583b47cfb32c55cb7ef5e50e
Publikováno v:
Remote Sensing, Vol 14, Iss 17, p 4140 (2022)
Deep learning-based solutions for precision agriculture have recently achieved promising results. Deep learning has been used to identify crop diseases at the initial stages of disease development in an effort to create effective disease management s
Externí odkaz:
https://doaj.org/article/68d0da9edcfd41f28759be9b83e7397c
Publikováno v:
Remote Sensing, Vol 13, Iss 24, p 5182 (2021)
Current methods of broadcast herbicide application cause a negative environmental and economic impact. Computer vision methods, specifically those related to object detection, have been reported to aid in site-specific weed management procedures for
Externí odkaz:
https://doaj.org/article/5cf37007b2c148ba814123cfa0090938
Autor:
Shoobhangi Tyagi, Sandeep Sahany, Dharmendra Saraswat, Saroj Kanta Mishra, Amlendu Dubey, Dev Niyogi
Water, food, and energy security are the major climate risks of global warming. The Paris Agreement proposed an ambitious target of limiting the rise in global mean surface temperature to well below 20C, and preferably to 1.50C, compared to the pre-i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e63e5d6c0032b658b5a7f4ca85e72424
https://doi.org/10.5194/egusphere-egu23-11183
https://doi.org/10.5194/egusphere-egu23-11183
Publikováno v:
Journal of the ASABE. 65:1433-1442
Highlights An approach using deep learning was proposed for identifying diseased regions in UAS imagery of corn fields with 97.23% testing accuracy using the VGG16 model. Disease types were identified within the diseased regions with a testing accura
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfcf5e603b7e53068f1626a15f6ffaca
https://doi.org/10.13031/ja.14895
https://doi.org/10.13031/ja.14895