scholarly journals Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)

2022 ◽  
Vol 2161 (1) ◽  
pp. 012059
Author(s):  
Rohan Nigam ◽  
Meghana Rao ◽  
Nihal Rian Dias ◽  
Arjun Hariharan ◽  
Amit Choraria ◽  
...  
Keyword(s):  
Computer Vision ◽  
Primary Source ◽  
Plant Health ◽  
Critical Conditions ◽  
Factors Affecting ◽  
Crucial Information ◽  
Increasing Demand ◽  
Vision Algorithm ◽  
User Friendly ◽  
Monitoring Platform

Abstract Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.

scholarly journals A simple computer vision algorithm as a clinical aid for the pathologist

2019 ◽  
Author(s):  
J.M. Lázaro-Guevara ◽  
B.J. Flores-Robles ◽  
A.E. Murga ◽  
K.M. Garrido
Keyword(s):  
Computer Vision ◽  
Screening Method ◽  
Clinical Work ◽  
Simple Computer ◽  
Risk Of Malignancy ◽  
Cell Shapes ◽  
High Resolution Images ◽  
Clinical Meaning ◽  
Increasing Demand ◽  
Vision Algorithm

AbstractHistological analysis for cancer detection or stratification is performed by observing and examining a small portion of a biopsied tissue under a microscope. Nevertheless, to assign clinical meaning to the findings, the analysis and interpretation of an experienced Pathologist is always necessary. Using high-resolution images, these experts visually examine the sample looking for specific characteristics on the cell shapes and tissue distributions, so they could decide whether tissue regions are cancerous, and establish the malignancy level of it. However, with the increasing demand for work for those pathologists and the importance of accuracy on diagnostics, multiple attempts to simplify their work have been performed. Current Imaging technologies allow novel horizons in the automatized selection of some of the characteristics that indicate malignancy in a biopsy. In this work, we propose a simple computer vision algorithm that can be implemented as a screening method for focusing in histological areas with higher risk of malignancy saving time to the pathologist and helping to perform a more standardized work, an easy observation with the potential to become in an aid to daily clinical work.

scholarly journals From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 355
Author(s):  
Arícia Possas ◽  
Olga María Bonilla-Luque ◽  
Antonio Valero
Keyword(s):  
Production Process ◽  
Physicochemical Characteristics ◽  
Storage Conditions ◽  
Starter Cultures ◽  
Predictive Microbiology ◽  
Factors Affecting ◽  
Modeling Approaches ◽  
Modelling Studies ◽  
Foodborne Outbreaks ◽  
User Friendly

Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, aw, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.

Measurement of cemented carbide-PCD microdrill geometry error based on Computer Vision algorithm

Measurement ◽  
2021 ◽  
pp. 110186
Author(s):  
Siti Nurfadilah Binti Jaini ◽  
Deug-Woo Lee ◽  
Kang-Seok Kim ◽  
Seung-Jun Lee
Keyword(s):  
Computer Vision ◽  
Cemented Carbide ◽  
Vision Algorithm ◽  
Geometry Error

Computer vision algorithm for identifying the post-irradiated nuclear fuel in a hotcell

2021 ◽  
Author(s):  
Helmi Fauzi R. ◽  
Prawito Prajitno ◽  
Sungkono ◽  
Refa Artika
Keyword(s):  
Computer Vision ◽  
Nuclear Fuel ◽  
Vision Algorithm ◽  
Irradiated Nuclear Fuel

Deep Learning and Computer Vision in Smart Agriculture

2020 ◽  
pp. 66-88
Author(s):  
Shiv Kumar ◽  
Agrima Yadav ◽  
Deepak Kumar Sharma
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Life Cycle ◽  
World Population ◽  
Traditional Methods ◽  
Demand And Supply ◽  
The World ◽  
Increasing Demand ◽  
Smart Agriculture ◽  
The Many

The exponential growth in the world population has led to an ever-increasing demand for food supplies. This has led to the realization that conventional and traditional methods alone might not be able to keep up with this demand. Smart agriculture is being regarded as one of the few realistic ways that, together with the traditional methods, can be used to close the gap between the demand and supply. Smart agriculture integrates the use of different technologies to better monitor, operate, and analyze different activities involved in different phases of the agricultural life cycle. Smart agriculture happens to be one of the many disciplines where deep learning and computer vision are being realized to be of major impact. This chapter gives a detailed explanation of different deep learning methods and tries to provide a basic understanding as to how these techniques are impacting different applications in smart agriculture.

Agricultural Growth Accounting and Total Factor Productivity in Jordan

2020 ◽  
pp. 1709-1723
Author(s):  
Samia Nadeem Akroush ◽  
Boubaker Dhehibi ◽  
Aden Aw-Hassan
Keyword(s):  
Total Factor Productivity ◽  
Productivity Growth ◽  
Agricultural Research ◽  
Agricultural Productivity ◽  
Primary Source ◽  
Factor Productivity ◽  
Factors Affecting ◽  
Tfp Growth ◽  
Food And Agriculture ◽  
Food And Agriculture Organization

This article develops new estimates of historical agricultural productivity growth in Jordan. It investigates how public policies such as agricultural research, investment in irrigation capital, and water pricing have contributed to agricultural productivity growth. The Food and Agriculture Organization (FAO) annual time series from 1961 to 2011 of all crops and livestock productions are the primary source for agricultural outputs and inputs used to construct the Törnqvist Index for the case of Jordan. The log-linear form of regression equation was used to examine the relationship between Total Factor Productivity (TFP) growth and different factors affecting TFP growth. The results showed that human capital has positive and direct significant impact on TFP implying that people with longer life expectancy has a significant impact on TFP growth. This article concludes that despite some recent improvement, agricultural productivity growth in Jordan continues to lag behind just about every other region of the world.

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