Data acquisition from greenhouses by using autonomous mobile robot

Durmuş, Halil; Güneş, Ece Olcay; Kırcı, Mürvet

doi:10.1109/agro-geoinformatics.2016.7577696

Cited by 14 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both the CNN architecture performed well, although the performance of Alexnet was slightly better. Further, they suggested that these neural networks can be added to the robot [4] for real time disease detection capability.…”

Section: Literature Surveymentioning

confidence: 99%

An Agri vigilance System based on Computer Vision and Deep Learning

Owalekar¹

2021

J. Infor. Electr. Electron. Eng.

View full text Add to dashboard Cite

In an agricultural-based country like India, farming and farming activities play a vital role in the growth of the economy as it is the main source of GNI (Gross National Income). This dependence of GNI on agriculture makes it important to address the issues faced by the farmers. The main area of concern for farmers revolves around crops and livestock. Precise farming techniques like cattle counting and crop disease detection are the need of the hour. The introduction of computer vision and deep learning has enabled us to make improvements in farming techniques. To accomplish this, a computer vision-based system is proposed which will be implemented using ResNet and YOLOv3-tiny. The proposed system will take images and videos as input and run them on the inference. The output will be updated in the database and the farmer will be notified in case of any inconsistency. The detailed report can be accessed by government agencies. The system will increase efficiency in farming processes like crop monitoring, livestock tracking, crop disease detection by providing fast and efficient solutions for the problems faced by the farmers.

show abstract

Section: Literature Surveymentioning

confidence: 99%

An Agri vigilance System based on Computer Vision and Deep Learning

Owalekar¹

2021

J. Infor. Electr. Electron. Eng.

View full text Add to dashboard Cite

show abstract

“…e JDL data fusion model is well designed for the flows of perception, data processing, and evaluation of associate intelligence including sensing data. Other studies on the acquisition and processing of sensing data on robotics applications have demonstrated that diverse aspects and objectives exist depending on how robotic devices can provide solutions to issues regarding automation, control, and management [42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

An Extensible Data Enrichment Scheme for Providing Intelligent Services in Internet of Things Environments

Park

Choi

2021

Mobile Information Systems

View full text Add to dashboard Cite

Recent technologies in the Internet of Things (IoT) environment aim to provide intelligent services to users. Intelligent services can be managed and executed by systems that handle context information sets. Handling intelligent services leads to three major considerations: objects in the real world that should be described as metadata, a data enrichment procedure from sensing values for representing states, and controlling functionalities to manage services. In this study, an extensible data-enrichment scheme is proposed. The proposed scheme provides a way to describe profiles, data abstraction procedures, and functionalities that support the building of context information sets derived from raw datasets in the manner of a semantic web stack. Finally, data enrichment will help any system that uses context information by providing improved, understandable, and readable datasets to the service developers or the systems themselves.

show abstract

“…Wang et al [61] tested the robustness of deep learning algorithms in mango orchard trees using real-world ground vehicles equipped with 2D and depth cameras. Durmus et al [34] implemented a custom real-world vehicle and tested it in a greenhouse environment using the ROS platform. The mobile robot was using a depth camera for mapping the greenhouse environment and was navigating in the greenhouse by using the generated map.…”

Section: Physical Space: Real-world Implementationmentioning

confidence: 99%

AgROS: A Robot Operating System Based Emulation Tool for Agricultural Robotics

2019

View full text Add to dashboard Cite

This research aims to develop a farm management emulation tool that enables agrifood producers to effectively introduce advanced digital technologies, like intelligent and autonomous unmanned ground vehicles (UGVs), in real-world field operations. To that end, we first provide a critical taxonomy of studies investigating agricultural robotic systems with regard to: (i) the analysis approach, i.e., simulation, emulation, real-world implementation; (ii) farming operations; and (iii) the farming type. Our analysis demonstrates that simulation and emulation modelling have been extensively applied to study advanced agricultural machinery while the majority of the extant research efforts focuses on harvesting/picking/mowing and fertilizing/spraying activities; most studies consider a generic agricultural layout. Thereafter, we developed AgROS, an emulation tool based on the Robot Operating System, which could be used for assessing the efficiency of real-world robot systems in customized fields. The AgROS allows farmers to select their actual field from a map layout, import the landscape of the field, add characteristics of the actual agricultural layout (e.g., trees, static objects), select an agricultural robot from a predefined list of commercial systems, import the selected UGV into the emulation environment, and test the robot’s performance in a quasi-real-world environment. AgROS supports farmers in the ex-ante analysis and performance evaluation of robotized precision farming operations while lays the foundations for realizing “digital twins” in agriculture.

show abstract

Data acquisition from greenhouses by using autonomous mobile robot

Cited by 14 publications

References 10 publications

An Agri vigilance System based on Computer Vision and Deep Learning

An Agri vigilance System based on Computer Vision and Deep Learning

An Extensible Data Enrichment Scheme for Providing Intelligent Services in Internet of Things Environments

AgROS: A Robot Operating System Based Emulation Tool for Agricultural Robotics

Contact Info

Product

Resources

About