Chapter 8 Machine learning for sustainable agriculture

2021 ◽  
pp. 129-146
Author(s):  
Ashok Bhansali ◽  
Swati Saxena ◽  
Kailash Chandra Bandhu
Keyword(s):  
Machine Learning ◽  
Sustainable Agriculture

Cognitive Computing For Sustainable Agriculture

2019 ◽  
Vol 8 (3) ◽  
pp. 32-34
Author(s):  
T. Manjula ◽  
T. Sudha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sustainable Agriculture ◽  
Food Supply ◽  
Green Revolution ◽  
Modern Society ◽  
Cognitive Computing ◽  
Cognitive Computation ◽  
Ancient Times ◽  
Modern Technologies

Cognitive computing in agriculture is going to be a big revolution like the green revolution. Agriculture is a big step that accompanied the humanity to evolve from the ancient times to the modern days and has fulfilled the basic need for food supply. Today still remains it’s at most importance. Cognitive computing uses cognitive technologies in agriculture that help to understand, learn from experiences and environment, reason, interact and thus increase the efficiency. Civilization has led to more urbanization. There are more people than available food. There is a great necessity to increase the per meter yield, So many techniques have been for seen in agriculture in terms of usage of pesticides and fertilizers, use of hybridization and green revolution to increase the production in agriculture. Now the use of modern technologies such as artificial intelligence and cognitive computation is going to bring a new big revolution for sustainable agriculture. The present paper focuses on the problems faced by the modern society in agriculture and how the cognitive computation provides an ultimate solution to the problems. We also discuss some illustrations for the usage of cognitive technologies and machine learning in the field of agriculture.

scholarly journals Krishimitr: A Cloud Computing and Platform for Disease Detection in Agriculture

2019 ◽  
Vol 8 (12) ◽  
pp. 2967-2970
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Sustainable Agriculture ◽  
Hybrid Model ◽  
Plant Diseases ◽  
Disease Detection ◽  
Model Combining ◽  
Comprehensive Framework

Automating disease detection is a cornerstone in the journey to achieving sustainable agriculture. We describe a framework utilizing Machine Learning, Cloud Computing and Internet-of-Things which brings experts to farmers, allowing for timely detection of diseases. This innovative and comprehensive framework provides agronomists and farmers with a solution for diagnosing plant diseases. By leveraging modern ICT capabilities, this extensible framework is currently trained for over 15 plant types and more than 51 disease types. Our framework employs a hybrid model combining use of both online and offline resources to provide up-to-date information to farmers even in case of patchy connectivity

The DARA Big Data Project

2018 ◽  
Vol 14 (A30) ◽  
pp. 569-569
Author(s):  
Anna M. M. Scaife ◽  
Sally E. Cooper
Keyword(s):  
Machine Learning ◽  
South Africa ◽  
Big Data ◽  
Sustainable Agriculture ◽  
South African ◽  
Health Data ◽  
The South ◽  
Data Intensive ◽  
Training Courses ◽  
The Uk

AbstractThe DARA Big Data project is a flagship UK Newton Fund & GCRF program in partnership with the South African Department of Science & Technology (DST). DARA Big Data provides bursaries for students from the partner countries of the African VLBI Network (AVN), namely Botswana, Ghana, Kenya, Madagascar, Mauritius, Mozambique, Namibia and Zambia, to study for MSc(R) and PhD degrees at universities in South Africa and the UK. These degrees are in the three data intensive DARA Big Data focus areas of astrophysics, health data and sustainable agriculture. The project also provides training courses in machine learning, big data techniques and data intensive methodologies as part of the Big Data Africa initiative.

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
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