scholarly journals Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

2018 ◽  
Vol 153 ◽  
pp. 69-81 ◽  
Author(s):  
Diego Inácio Patrício ◽  
Rafael Rieder
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Grain Crops

scholarly journals Applications of Artificial Intelligence for Precision Agriculture

EDIS ◽  
2018 ◽  
Vol 2018 (6) ◽  
Author(s):  
Yiannis Ampatzidis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Remote Sensing ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Agricultural Systems ◽  
Disease Identification ◽  
Technological Advances

Technological advances in computer vision, mechatronics, artificial intelligence and machine learning have enabled the development and implementation of remote sensing technologies for plant/weed/pest/disease identification and management. They provide a unique opportunity for developing intelligent agricultural systems for precision applications. Herein, the Artificial Intelligence (AI) and Machine Learning concepts are described, and several examples are presented to demonstrate the application of the AI in agriculture. Available on EDIS at: https://edis.ifas.ufl.edu/ae529

scholarly journals Artificial intelligence: A cutting edge technology in agriculture

2021 ◽  
Vol 17 (1) ◽  
pp. 114-120
Author(s):  
Sidhant Allawadi ◽  
Jayaty ◽  
Parmod Sharma ◽  
Kapil Rohilla ◽  
Gopal Deokar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Computer Vision ◽  
Cutting Edge ◽  
High Quality ◽  
High Expectations ◽  
Advanced Technologies ◽  
The Common ◽  
Smart Technologies

Attention is currently being paid to the use of smart technologies. Agriculture has provided an important source of food for humans over thousands of years, including the development of appropriate farming methods for the cultivation of different crops. The emergence of new advanced technologies has the potential to monitor the agricultural environment to ensure high-quality produce. In this context, a systematic review that aimsto study the application of various technologies and algorithms in Artificial Intelligence (AI) with the latest solutions to make the farming more efficient remains one of the greatest imperatives. Artificial intelligence can be applied directly in the field of agriculture for various operations. Amid high expectations about how AI will help the common personand transform his mindset, thoughts and attitude towards the benefits that it may bring. There are certain concerns about the ill effects of such sophisticated technologies as well.This review also focuses on the activation of perceptive technologies and application of computer vision and machine learning in agriculture.

scholarly journals Artificial Intelligence Approach for Tomato Detection and Mass Estimation in Precision Agriculture

2020 ◽  
Vol 12 (21) ◽  
pp. 9138
Author(s):  
Jaesu Lee ◽  
Haseeb Nazki ◽  
Jeonghyun Baek ◽  
Youngsin Hong ◽  
Meonghun Lee
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Precision Agriculture ◽  
Vision System ◽  
Percentage Error ◽  
Measuring Instruments ◽  
Detection Accuracy ◽  
Mass Detection ◽  
Mass Estimation ◽  
Physical Relation

Application of computer vision and robotics in agriculture requires sufficient knowledge and understanding of the physical properties of the object of interest. Yield monitoring is an example where these properties affect the quantified estimation of yield mass. In this study, we propose an image-processing and artificial intelligence-based system using multi-class detection with instance-wise segmentation of fruits in an image that can further estimate dimensions and mass. We analyze a tomato image dataset with mass and dimension values collected using a calibrated vision system and accurate measuring devices. After successful detection and instance-wise segmentation, we extract the real-world dimensions of the fruit. Our characterization results exhibited a significantly high correlation between dimensions and mass, indicating that artificial intelligence algorithms can effectively capture this complex physical relation to estimate the final mass. We also compare different artificial intelligence algorithms to show that the computed mass agrees well with the actual mass. Detection and segmentation results show an average mask intersection over union of 96.05%, mean average precision of 92.28%, detection accuracy of 99.02%, and precision of 99.7%. The mean absolute percentage error for mass estimation was 7.09 for 77 test samples using a bagged ensemble tree regressor. This approach could be applied to other computer vision and robotic applications such as sizing and packaging systems and automated harvesting or to other measuring instruments.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review

2021 ◽  
Vol 11 (7) ◽  
pp. 3253
Author(s):  
Umile Giuseppe Longo ◽  
Sergio De Salvatore ◽  
Vincenzo Candela ◽  
Giuliano Zollo ◽  
Giovanni Calabrese ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Virtual Reality ◽  
Augmented Reality ◽  
Orthopaedic Surgery ◽  
Resident Training ◽  
Comprehensive Search ◽  
Virtual And Augmented Reality ◽  
Orthopaedic Training ◽  
Meta Analyses

Background: The application of virtual and augmented reality technologies to orthopaedic surgery training and practice aims to increase the safety and accuracy of procedures and reducing complications and costs. The purpose of this systematic review is to summarise the present literature on this topic while providing a detailed analysis of current flaws and benefits. Methods: A comprehensive search on the PubMed, Cochrane, CINAHL, and Embase database was conducted from inception to February 2021. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were used to improve the reporting of the review. The Cochrane Risk of Bias Tool and the Methodological Index for Non-Randomized Studies (MINORS) was used to assess the quality and potential bias of the included randomized and non-randomized control trials, respectively. Results: Virtual reality has been proven revolutionary for both resident training and preoperative planning. Thanks to augmented reality, orthopaedic surgeons could carry out procedures faster and more accurately, improving overall safety. Artificial intelligence (AI) is a promising technology with limitless potential, but, nowadays, its use in orthopaedic surgery is limited to preoperative diagnosis. Conclusions: Extended reality technologies have the potential to reform orthopaedic training and practice, providing an opportunity for unidirectional growth towards a patient-centred approach.

scholarly journals Prediction Models for Public Health Containment Measures on COVID-19 Using Artificial Intelligence and Machine Learning: A Systematic Review

2021 ◽  
Vol 18 (9) ◽  
pp. 4499
Author(s):  
Anil Babu Payedimarri ◽  
Diego Concina ◽  
Luigi Portinale ◽  
Massimo Canonico ◽  
Deborah Seys ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Systematic Review ◽  
Public Transportation ◽  
Prediction Models ◽  
Positive Impact ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Commercial Activities

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

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