scholarly journals Image analysis aplications in precision agriculture

2018 ◽  
Vol 11 (2) ◽  
pp. 200-210
Author(s):  
Wilson Fernando Moreno ◽  
Héctor Iván Tangarife ◽  
Andrés Escobar Díaz
Keyword(s):  
Image Analysis ◽  
Precision Agriculture ◽  
Unmanned Aircraft ◽  
Aerial Images ◽  
Vegetation Monitoring ◽  
Vegetation Growth ◽  
Current State ◽  
Livestock Sector ◽  
Soil Preparation

Unmanned Aircraft Vehicles (UAVs) are currently used for multiple applications in various fields: forestry, geology, the livestock sector and security. Among the most common applications, it is worth to stand out the image acquisition, irrigation, transport, surveillance and others. The study that one presents treats of the implementations that are realized by means of aerial images acquired with UAVs directed to the farming. Images acquired until recent years had been using satellites, however due to the high costs that are incurred and low accessibility to these technologies, UAVs, have become a tool for greater precision and scope for making decisions in agriculture. Information from databases of international magazines, groups and research centers is taken to determine the current state of implementations in Precision Agriculture (PA). This article describes tasks such as: soil preparation; limits and land areas, vegetation monitoring; classification of vegetation, growth, height, plant health; diseases management, pests and weeds, fertilization and inventory developed from analysis of aerial images acquired with UAVs.

scholarly journals AirSurf-Lettuce: an aerial image analysis platform for ultra-scale field phenotyping and precision agriculture using computer vision and deep learning

2019 ◽  
Author(s):  
Alan Bauer ◽  
Aaron George Bostrom ◽  
Joshua Ball ◽  
Christopher Applegate ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Precision Agriculture ◽  
Large Scale ◽  
Vegetation Index ◽  
Aerial Images ◽  
Aerial Image ◽  
Analysis Platform ◽  
Aerial Image Analysis

AbstractAerial imagery is regularly used by farmers and growers to monitor crops during the growing season. To extract meaningful phenotypic information from large-scale aerial images collected regularly from the field, high-throughput analytic solutions are required, which not only produce high-quality measures of key crop traits, but also support agricultural practitioners to make reliable management decisions of their crops. Here, we report AirSurf-Lettuce, an automated and open-source aerial image analysis platform that combines modern computer vision, up-to-date machine learning, and modular software engineering to measure yield-related phenotypes of millions of lettuces across the field. Utilising ultra-large normalized difference vegetation index (NDVI) images acquired by fixed-wing light aircrafts together with a deep-learning classifier trained with over 100,000 labelled lettuce signals, the platform is capable of scoring and categorising iceberg lettuces with high accuracy (>98%). Furthermore, novel analysis functions have been developed to map lettuce size distribution in the field, based on which global positioning system (GPS) tagged harvest regions can be derived to enable growers and farmers’ precise harvest strategies and marketability estimates before the harvest.

The current state of the legislative implementation of criminal law policy

2020 ◽  
pp. 111-118
Author(s):  
Oleksandr Ostrohliad
Keyword(s):  
Law Enforcement ◽  
Criminal Code ◽  
Practical Significance ◽  
New Approach ◽  
Public Discussion ◽  
Current State ◽  
Criminal Offenses ◽  
Legal Technology ◽  
Logical Semantic

Purpose. The aim of the work is to consider the novelties of the legislative work, which provide for the concept and classification of criminal offenses in accordance with the current edition of the Criminal Code of Ukraine and the draft of the new Code developed by the working group and put up for public discussion. Point out the gaps in the current legislation and the need to revise individual rules of the project in this aspect. The methodology. The methodology includes a comprehensive analysis and generalization of the available scientific and theoretical material and the formulation of appropriate conclusions and recommendations. During the research, the following methods of scientific knowledge were used: terminological, logical-semantic, system-structural, logical-normative, comparative-historical. Results In the course of the study, it was determined that despite the fact that the amendments to the Criminal Code of Ukraine came into force in July of this year, their perfection, in terms of legal technology, raises many objections. On the basis of a comparative study, it was determined that the Draft Criminal Code of Ukraine needs further revision taking into account the opinions of experts in the process of public discussion. Originality. In the course of the study, it was established that the classification of criminal offenses proposed in the new edition of the Criminal Code of Ukraine does not stand up to criticism, since other elements of the classification appear in subsequent articles, which are not covered by the existing one. The draft Code, using a qualitatively new approach to this issue, retains the elements of the previous classification and has no practical significance in law enforcement. Practical significance. The results of the study can be used in law-making activities to improve the norms of the current Criminal Code, to classify criminal offenses, as well as to further improve the draft Criminal Code of Ukraine.

Colour image analysis for the classification of turkey breast meat according to cooking yield

2003 ◽  
Vol 23 (1) ◽  
pp. 124-127
Author(s):  
Isabel Sebastáan ◽  
V Santé ◽  
G Le Pottier ◽  
Pascale Marty-Mahé ◽  
P Loisel ◽  
...  
Keyword(s):  
Image Analysis ◽  
Breast Meat ◽  
Colour Image ◽  
Cooking Yield

REFLECTION OF THE CURRENT STATE OF PUBLICISTIC JOURNALISM GENRES IN THE SLOVAK MEDIA ENVIRONMENT

2020 ◽  
Author(s):  
Zuzana Kvetanová
Keyword(s):  
Discourse Analysis ◽  
Point Of View ◽  
Media Theory ◽  
Logical Analysis ◽  
Periodical Press ◽  
Media Environment ◽  
Current State ◽  
Journalistic Practice ◽  
Opinion Journalism

The submitted study addresses the topic of the current state of the opinion journalism and its genres in the Slovak periodical press. The author draws attention to the question of classification of the opinion journalism of a rational and emotional type from the genre categorization point of view and, simultaneously, reflects on its application in the present journalistic practice. This brings a certain rate of confrontation between the defined theoretical premises and their subsequent practical (non-)implementation. The main objective of the study is to clarify the presence of genres of analytical and literary opinion journalism stated by media theory in the environment of the Slovak periodicals. Presentation of the basic terminological axis and the related explication of journalism genres included in the opinion journalism constitute the secondary objectives of the paper. For the purposes of achieving the set objectives, the author uses methods of logical analysis of text in combination with discourse analysis. Similarly, she predicts the evident presence of the phenomenon of hybridization in the Slovak journalistic practice.

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 952
Author(s):  
Lia Duarte ◽  
Ana Cláudia Teodoro ◽  
Joaquim J. Sousa ◽  
Luís Pádua
Keyword(s):  
Open Source ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Data Interpretation ◽  
Vegetation Monitoring ◽  
Distribution Maps ◽  
Monitoring Process ◽  
Multi Temporal ◽  
The Creation ◽  
The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

scholarly journals Classification of total carotene and quality of chili pepper (Capsicum frutescens) based on image analysis

2021 ◽  
Vol 733 (1) ◽  
pp. 012005
Author(s):  
Y Hendrawan ◽  
R Utami ◽  
D Y Nurseta ◽  
Daisy ◽  
S Nuryani ◽  
...  
Keyword(s):  
Image Analysis ◽  
Chili Pepper ◽  
Capsicum Frutescens
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