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Aitazaz A. Farooque
Researcher at University of Prince Edward Island
Publications - 120
Citations - 1404
Aitazaz A. Farooque is an academic researcher from University of Prince Edward Island. The author has contributed to research in topics: Environmental science & Computer science. The author has an hindex of 14, co-authored 86 publications receiving 582 citations. Previous affiliations of Aitazaz A. Farooque include Dalhousie University & Nova Scotia Agricultural College.
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Crop Yield Prediction through Proximal Sensing and Machine Learning Algorithms
TL;DR: In this paper, four ML algorithms, namely linear regression (LR), elastic net (EN), k-NN, and support vector regression (SVR), were used to predict potato (Solanum tuberosum) tuber yield from data of soil and crop properties collected through proximal sensing.
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A review of soil carbon dynamics resulting from agricultural practices
Farhat Abbas,Hafiz Mohkum Hammad,Wajid Ishaq,Aitazaz A. Farooque,Hafiz Faiq Bakhat,Zahida Zia,Shah Fahad,Wajid Farhad,Artemi Cerdà +8 more
TL;DR: It is found that agricultural management in arid and semi-arid regions, which have specific characteristics related to high temperatures and low rainfall conditions, requires different practices for maintenance and restoration of SOC and for control of soil erosion compared to those used in Mediterranean, tropical regions.
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Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning
TL;DR: In this paper, a multilayer perceptron (MLP), long short term memory (LSTM), and a convolutional neural network (CNN) with four different input variable combinations for two watersheds (Baltic River and Long Creek) in Prince Edward Island, Canada.
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Original papers: Development of prototype automated variable rate sprayer for real-time spot-application of agrochemicals in wild blueberry fields
Qamar U. Zaman,Travis Esau,Arnold W. Schumann,David C. Percival,Young K. Chang,Scott M. Read,Aitazaz A. Farooque +6 more
TL;DR: In this article, an automated variable rate (VR) sprayer was developed for control of 8 individual nozzles on a 6.1m sprayer boom for in-season, site-specific application of agrochemicals on weeds.
Journal ArticleDOI
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks.
Sujan Ghimire,Zaher Mundher Yaseen,Zaher Mundher Yaseen,Aitazaz A. Farooque,Ravinesh C. Deo,Ji Zhang,Xiaohui Tao +6 more
TL;DR: In this article, a CNN-LSTM model was proposed to predict the hourly streamflow at Brisbane River and Teewah Creek, Australia. And the proposed model outperformed all the benchmarked conventional AI models as well as ensemble models for all the time intervals.