Zobrazeno 1 - 10
of 174
pro vyhledávání: '"Qamar U. Zaman"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract While spot spraying has gained increasing popularity in recent years, spot application of granule agrochemical has seen little development. Despite the potential for the technology, there currently exists no commercially available granular a
Externí odkaz:
https://doaj.org/article/f0acf3e823474c07bffebd1a81ac47f9
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Agricultural dykelands in Nova Scotia rely heavily on a surface drainage technique called land forming, which is used to alter the topography of fields to improve drainage. The presence of land-formed fields provides useful information to be
Externí odkaz:
https://doaj.org/article/4194fad104074553b9744fd3b3bbd710
Autor:
Zeeshan Haydar, Travis J. Esau, Aitazaz A. Farooque, Mathieu F. Bilodeau, Qamar U. Zaman, Farhat Abbas, Nauman Yaqoob
Publikováno v:
Smart Agricultural Technology, Vol 8, Iss , Pp 100456- (2024)
Mechanical harvesting of wild blueberries on uneven topography is challenging for the operators, emphasizing the need for harvester header automation. This research aims to develop accurate Digital Elevation Models (DEMs) and map plant height using a
Externí odkaz:
https://doaj.org/article/0e95d7c7c9914ee6885ce92c1d6e1799
Publikováno v:
International Journal of Fruit Science, Vol 23, Iss 1, Pp 229-245 (2023)
ABSTRACTThe profitability of wild blueberry (Vaccinium angustifolium) production using two alternative mechanical harvesters was evaluated under three different harvester ownership/service arrangements commonly used by farmers. Production data for th
Externí odkaz:
https://doaj.org/article/a2e2bd5872734c8da6ec3015c6d2d95e
Autor:
Muhammad Shahzaib, Uzair Muhammad Khan, Muhammad Tehseen Azhar, Rana Muhammad Atif, Sultan Habibullah Khan, Qamar U. Zaman, Iqrar Ahmad Rana
Publikováno v:
PLoS ONE, Vol 19, Iss 1 (2024)
Externí odkaz:
https://doaj.org/article/f690a09106b747a58531581d20babca8
Autor:
Zeeshan Haydar, Travis J. Esau, Aitazaz A. Farooque, Qamar U. Zaman, Patrick J. Hennessy, Kuljeet Singh, Farhat Abbas
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract An operator of a wild blueberry harvester faces the fatigue of manually adjusting the height of the harvester’s head, considering spatial variations in plant height, fruit zone, and field topography affecting fruit yield. For stress-free h
Externí odkaz:
https://doaj.org/article/ebb1c9d14cd84c7a8390e5c049bc0c79
Autor:
Chao Li, Wen Chu, Rafaqat Ali Gill, Shifei Sang, Yuqin Shi, Xuezhi Hu, Yuting Yang, Qamar U. Zaman, Baohong Zhang
Publikováno v:
Genomics, Proteomics & Bioinformatics, Vol 21, Iss 1, Pp 108-126 (2023)
The past decade has witnessed a rapid evolution in identifying more versatile clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) nucleases and their functional variants, as well as in developing precise
Externí odkaz:
https://doaj.org/article/fc87f14f635f4109b9a106a47059a19e
Autor:
Anup Kumar Das, Travis J. Esau, Qamar U. Zaman, Aitazaz A. Farooque, Arnold W. Schumann, Patrick J. Hennessy
Publikováno v:
Smart Agricultural Technology, Vol 4, Iss , Pp 100166- (2023)
Improving fruit quality is important to wild blueberry producers. Plant debris like leaves and stems caused a reduction in fruit quality during mechanical harvesting. A debris detection system was developed using two Logitech C920 webcam cameras moun
Externí odkaz:
https://doaj.org/article/9537683131364fcda6e79038a9242408
Autor:
Niaz Ahmad, Samia Fatima, Muhammad Aamer Mehmood, Qamar U. Zaman, Rana Muhammad Atif, Weijun Zhou, Mehboob-ur Rahman, Rafaqat Ali Gill
Publikováno v:
Frontiers in Plant Science, Vol 14 (2023)
CRISPR-mediated genome editing has emerged as a powerful tool for creating targeted mutations in the genome for various applications, including studying gene functions, engineering resilience against biotic and abiotic stresses, and increasing yield
Externí odkaz:
https://doaj.org/article/e9daa9b287ef4debbc54c2484893f64b
Publikováno v:
Smart Agricultural Technology, Vol 3, Iss , Pp 100099- (2023)
This study looked at the development of six deep learning artificial neural network models for detecting ripeness stage in wild blueberries, along with developing models for yield estimation. The six networks used were YOLOv3, YOLOv3-SPP, YOLOv3-Tiny
Externí odkaz:
https://doaj.org/article/34a5984a263c452783bc1abdfdcd3d56