Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals Tackling the Risk of Stranded Electricity Assets with Machine Learning and Artificial Intelligence

2021 ◽  
Author(s):  
Joseph Nyangon
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Carbon Budget ◽  
Energy Sector ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Legal Challenges ◽  
Nationally Determined Contributions ◽  
Implementation Level

The Paris Agreement on climate change requires nations to keep the global temperature within the 2°C carbon budget. Achieving this temperature target means stranding more than 80% of all proven fossil energy reserves as well as resulting in investments in such resources becoming stranded assets. At the implementation level, governments are experiencing technical, economic, and legal challenges in transitioning their economies to meet the 2°C temperature commitment through the nationally determined contributions (NDCs), let alone striving for the 1.5°C carbon budget, which translates into greenhouse gas emissions (GHG) gap. This chapter focuses on tackling the risks of stranded electricity assets using machine learning and artificial intelligence technologies. Stranded assets are not new in the energy sector; the physical impacts of climate change and the transition to a low-carbon economy have generally rendered redundant or obsolete electricity generation and storage assets. Low-carbon electricity systems, which come in variable and controllable forms, are essential to mitigating climate change. These systems present distinct opportunities for machine learning and artificial intelligence-powered techniques. This chapter considers the background to these issues. It discusses the asset stranding discourse and its implications to the energy sector and related infrastructure. The chapter concludes by outlining an interdisciplinary research agenda for mitigating the risks of stranded assets in electricity investments.

scholarly journals Formation of a cooperation network in Mato Grosso on Machine Learning and Image Analysis: Diagnosis of COVID-19 in X-ray images

2021 ◽  
Author(s):  
Jeniffer Luz ◽  
Scenio De Araujo ◽  
Caio Abreu ◽  
Juvenal Silva Neto ◽  
Carlos Gulo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Early Diagnosis ◽  
Critical Mass ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Mato Grosso ◽  
X Ray ◽  
Occurrence Matrix

Since the beginning of the COVID-19 outbreak, the scientific communityhas been making efforts in several areas, either by seekingvaccines or improving the early diagnosis of the disease to contributeto the fight against the SARS-CoV-2 virus. The use of X-rayimaging exams becomes an ally in early diagnosis and has been thesubject of research by the medical image processing and analysiscommunity. Although the diagnosis of diseases by image is a consolidatedresearch theme, the proposed approach aims to: a) applystate-of-the-art machine learning techniques in X-ray images forthe COVID-19 diagnosis; b) identify COVID-19 features in imagingexamination; c) to develop an Artificial Intelligence model toreduce the disease diagnosis time; in addition to demonstrating thepotential of the Artificial Intelligence area as an incentive for theformation of critical mass and encouraging research in machinelearning and processing and analysis of medical images in the Stateof Mato Grosso, in Brazil. Initial results were obtained from experimentscarried out with the SVM (Support Vector Machine) classifier,induced on a publicly available image dataset from Kaggle repository.Six attributes suggested by Haralick, calculated on the graylevel co-occurrence matrix, were used to represent the images. Theprediction model was able to achieve 82.5% accuracy in recognizingthe disease. The next stage of the studies includes the study of deeplearning models.

scholarly journals Artificial Intelligence Technologies in Dentistry

2021 ◽  
Vol 38 (SI-2) ◽  
pp. 188-194
Author(s):  
Berkman ALBAYRAK ◽  
Gökhan ÖZDEMİR ◽  
Yeşim ÖLÇER US ◽  
Emir YÜZBAŞIOĞLU
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Oral Cancer ◽  
Disease Diagnosis ◽  
Digital World ◽  
Education Industry ◽  
Cad Cam ◽  
Medicine Today ◽  
Day By Day

One of the most important actors in the digitization process of our age has been the applications of artificial intelligence (AI). While the weak and strong AI sub-concepts and the different AI models within them are being utilized in many fields such as education, industry and medicine today, the interest of the dentistry field, which has started its integration into the digital world with CAD/CAM technology, in AI is increasing day by day. In different branches of dentistry; AI provides services to clinicians and researchers in many fields such as disease diagnosis, evaluation of the occurrence or recurrence of diseases such as oral cancer, and prediction of success in surgical and prosthetic treatments. In this article, studies in which AI models such as machine learning, convolutional neural network have found research and usage areas on the basis of different branches of dentistry are reviewed.

scholarly journals Arecanut Crop Disease Prediction using IoT and Machine Learning

2020 ◽  
Vol 5 (3) ◽  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Environmental Factors ◽  
Disease Prediction ◽  
Wind Flow ◽  
Prediction System ◽  
Major Development ◽  
The Future ◽  
Crop Disease ◽  
Agricultural Industries

A prevailing recession in the agricultural goods sector is evident from the present scarcity and lack of food supplies. A major reason for this scarcity is the inherent growth of diseases in essential crops. A major development is thus required in this field for avoiding these problems in the future. This development is intended to simplify the management tasks of different roles in agricultural industries. A proper intimation of the importance of disease prediction and environmental factors must be done to the less aware farmers. To address these challenges, we have proposed a disease prediction system that takes into consideration temperature (°C), humidity(%), rainfall(cm), wind flow(m/s) and soil moisture (%) around the region of crop and developed a model to predict the occurrence of disease. This system will provide information prior to the occurrence of disease by analyzing different relationships among environmental factors.

The future of cherry production.

2021 ◽  
pp. 377-380
Author(s):  
Lynn E. Long ◽  
Gregory A. Lang ◽  
Clive Kaiser
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Invasive Species ◽  
Significant Contribution ◽  
The Future ◽  
Production Technologies ◽  
New Challenges ◽  
Electronic Sensing ◽  
And Robotics

Abstract This chapter provides information on significant contribution of various advances in horticultural production technologies, including electronic sensing, autonomous orchard equipment, machine learning and artificial intelligence and robotics to future cherry production trends. New challenges due to invasive species, climate change and the ever unpredictable geopolitical landscape are also discussed.

scholarly journals Machine learning and artificial intelligence to aid climate change research and preparedness

2019 ◽  
Vol 14 (12) ◽  
pp. 124007 ◽  
Author(s):  
Chris Huntingford ◽  
Elizabeth S Jeffers ◽  
Michael B Bonsall ◽  
Hannah M Christensen ◽  
Thomas Lees ◽  
...  
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Climate Change Research

scholarly journals Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Genes ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1438
Author(s):  
Biljana Stankovic ◽  
Nikola Kotur ◽  
Gordana Nikcevic ◽  
Vladimir Gasic ◽  
Branka Zukic ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Inflammatory Bowel Disease ◽  
Bowel Disease ◽  
Disease Diagnosis ◽  
Omics Data ◽  
Current Application ◽  
Inflammatory Bowel ◽  
Future Outcomes ◽  
Clinical Records

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

scholarly journals An Analysis of the Applications of Natural Language Processing in Various Sectors

2021 ◽  
Author(s):  
Priya B ◽  
Nandhini J.M ◽  
Gnanasekaran T
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Health Sector ◽  
Automated System ◽  
Human Language ◽  
Agriculture Sector ◽  
Prediction Of Diabetes

Natural Language processing (NLP) dealing with Artificial Intelligence concept is a subfield of Computer Science, enabling computers to understand and process human language. Natural Language Processing being a part of artificial intelligence provides understanding of human language by computers for the purpose of extracting information or insights and create meaningful response. It involves creating algorithms that transform text in to words labeling With the emerging advancements in Machine learning and Deep Learning, NLP can contributed a lot towards health sector, education, agriculture and so on. This paper summarizes the various aspects of NLP along with case studies associated with Health Sector for Voice Automated System, prediction of Diabetes Millets, Crop Detection technique in Agriculture Sector.

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