Smart Farm Water Requirement Analysis Using Machine Learning

2022 ◽  
pp. 665-673
Author(s):  
R. Akshay Sunny ◽  
R. Jyosthna ◽  
R. Melvin Raj ◽  
M. Manoj
Keyword(s):  
Machine Learning ◽  
Water Requirement ◽  
Requirement Analysis

scholarly journals Water requirement analysis of three strawberry cultivars by using bucket-type lysimeter and its comparative study

2017 ◽  
Vol 2 (4) ◽  
pp. 672-677
Author(s):  
Md Hafizul Islam ◽  
Md Erfanul Haq ◽  
Prajna Paramita Paul ◽  
Amitave Paul ◽  
Ziaul Hoque
Keyword(s):  
Comparative Study ◽  
Potential Evapotranspiration ◽  
Crop Coefficient ◽  
Water Requirement ◽  
Requirement Analysis ◽  
Field Evaporation ◽  
Total Water ◽  
Crop Water ◽  
Strawberry Cultivars ◽  
Evaporation Condition

An experiment was carried out from November, 2014 to February, 2015 at Dinajpur, Bangladesh to quantify the total water requirement of Strawberry for three indigenous cultivars RU-1, RU-2 and RU-3by using 12'' × 11.5'' Bucket-Type Lysimeter. Water requirement in zero evaporation condition for RU-1, RU-2 and RU-3 were 86.25 ± 0.23, 49.22 ± 0.31 and 73.42 ± 0.42mm respectively, which were significantly different (p< 0.01). After adding field evaporation total water requirement RU-1, RU-2 and RU-3 were 351.45 ± 0.23, 324.42 ± 0.31 and 338.61 ± 0.42mm respectively.ET0(Potential evapotranspiration)value ranged between3.21-4.56 (mm/day) while seasonal ET0 was approximately 457 (mm/season).ETc (Evapotranspiration) value measured by using Kc (Crop coefficient) value and equations provided by FAO, (2016a, b) viz. 324.24 (mm/season). As plant only uses less than 1% of its total water uptake for metabolic use, Crop water requirement (CWR) can be easily represented by ETc. However our CWR value is in line with the theoretical ETc which clearly indicates level of accuracy. Therefore, it is highly recommendable for the local Commercial Strawberry growers to get robust yield.Asian J. Med. Biol. Res. December 2016, 2(4): 672-677

scholarly journals A Systematic Literature Review on Federated Machine Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-39
Author(s):  
Sin Kit Lo ◽  
Qinghua Lu ◽  
Chen Wang ◽  
Hye-Young Paik ◽  
Liming Zhu
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Systematic Literature Review ◽  
State Of The Art ◽  
System Development ◽  
Learning Systems ◽  
Learning System ◽  
Requirement Analysis ◽  
Future Trends ◽  
Data Synthesis

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results and identify future trends to encourage researchers to advance their current work.

scholarly journals Water Requirement Analysis of Paddy Field Irrigation System in Diversified Land Use Area

10.5109/24319 ◽  
1999 ◽  
Vol 44 (1/2) ◽  
pp. 175-187
Author(s):  
Hairul Basri ◽  
Yoshisuke Nakano ◽  
Masaharu Kuroda ◽  
Tetsuro Fukuda ◽  
Tamotsu Funakoshi
Keyword(s):  
Land Use ◽  
Paddy Field ◽  
Irrigation System ◽  
Water Requirement ◽  
Requirement Analysis

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions
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