Autonomous Real-Time Monitoring of Crops in Controlled Environment Agriculture

2019 ◽  
Author(s):  
Saba Faryadi ◽  
Mohammadreza Davoodi ◽  
Javad Mohammadpour Velni
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Directed Graphs ◽  
Controlled Environment ◽  
Real Time Monitoring ◽  
Collection System ◽  
Image Processing Algorithm ◽  
Ground Vehicle ◽  
Controlled Environment Agriculture ◽  
And Performance

Abstract In this work, we develop a system that can be used for real-time monitoring of multiple important areas in controlled environment agriculture (and in particular greenhouses) using an autonomous ground vehicle (AGV). To model the greenhouse layout, as well as the tasks that should be accomplished by the AGV, we generate two weighted directed graphs. Based on those graphs, an algorithm is then proposed for finding the optimal (in the sense of traveled distance) trajectory of the vehicle with the goal of precisely monitoring important areas in the greenhouse. Furthermore, a data collection system and image processing algorithm is proposed and implemented so that the vehicle: (i) can capture images and detect changes that have occurred on the crops in real time, and (ii) construct (if needed) a map of the plant rows, when arriving at each one of the important areas. Based on this work, the images can either be stitched onboard the vehicle and then sent to a server or be sent directly to the server and then processed (stitched) there. Both simulation and experimental results are provided to demonstrate the effectiveness and performance of the proposed system.

Image Processing Algorithm for Real-Time Crack Inspection in Hole Expansion Test

2019 ◽  
Vol 20 (7) ◽  
pp. 1139-1148 ◽  
Author(s):  
Seungho Choi ◽  
Kwangyoon Kim ◽  
Jaeho Lee ◽  
Sung Hyuk Park ◽  
Hye-Jin Lee ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Processing Algorithm ◽  
Image Processing Algorithm ◽  
Hole Expansion ◽  
Expansion Test ◽  
Hole Expansion Test

scholarly journals Waste Management and Prediction of Air Pollutants Using IoT and Machine Learning Approach

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3930 ◽  
Author(s):  
Ayaz Hussain ◽  
Umar Draz ◽  
Tariq Ali ◽  
Saman Tariq ◽  
Muhammad Irfan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Air Pollutants ◽  
Garbage Collection ◽  
Air Pollutant ◽  
Real Time Monitoring ◽  
Collection System ◽  
The Real ◽  
The Status

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

scholarly journals An IoT Based Efficient Waste Collection System with Smart Bins

2020 ◽  
Author(s):  
Khandaker Foysal Haque ◽  
Rifat Zabin ◽  
Kumar Yelamarthi ◽  
Prasanth Yanambaka ◽  
Ahmed Abdelgawad
Keyword(s):  
Real Time ◽  
Human Life ◽  
Waste Collection ◽  
Real Time Monitoring ◽  
Collection System ◽  
Mount Pleasant ◽  
Human Labor ◽  
Random Location ◽  
Village Life ◽  
The City

<div>Waste collection and management is an integrated</div><div>part of both city and village life. Lack of optimized and efficient waste collection system vastly affect public health and costs more. The prevailing traditional waste collection system is neither optimized nor efficient. Internet of Things (IoT) has been playing a great role in making human life easier by making systems smart, adequate and self sufficient. Thus, this paper proposes an IoT based efficient waste collection system with smart bins. It does real-time monitoring of the waste bins and determines which bins are to emptied in every cycle of waste collection. The system</div><div>also presents an enhanced navigation system that shows the best route to collect wastes from the selected bins. Four waste bins are assumed in the city of Mount Pleasant, Michigan at random location. The proposed system decreases the travel distance by 30.76% on an average in the assumed scenario, compared to the traditional waste collection system. Thus it reduces the fuel cost and human labor making the system optimized and efficient by enabling real-time monitoring and enhanced navigation.</div>

scholarly journals An Automatic Processing Chain for Near Real-Time Mapping of Burned Forest Areas Using Sentinel-2 Data

2020 ◽  
Vol 12 (4) ◽  
pp. 674 ◽  
Author(s):  
Luca Pulvirenti ◽  
Giuseppe Squicciarino ◽  
Elisabetta Fiori ◽  
Paolo Fiorucci ◽  
Luca Ferraris ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Vegetation Index ◽  
Commission Error ◽  
Management Service ◽  
Image Processing Algorithm ◽  
Burned Areas ◽  
Processing Chain ◽  
Sentinel 2 ◽  
Burned Forest

A fully automated processing chain for near real-time mapping of burned forest areas using Sentinel-2 multispectral data is presented. The acronym AUTOBAM (AUTOmatic Burned Areas Mapper) is used to denote it. AUTOBAM is conceived to work daily at a national scale for the Italian territory to support the Italian Civil Protection Department in the management of one of the major natural hazards, which affects the territory. The processing chain includes a Sentinel-2 data procurement component, an image processing algorithm, and the delivery of the map to the end-user. The data procurement component searches every day for the most updated products into different archives. The image processing part represents the core of AUTOBAM and implements an algorithm for burned forest areas mapping that uses, as fundamental parameters, the relativized form of the delta normalized burn ratio and the normalized difference vegetation index. The minimum mapping unit is 1 ha. The algorithm implemented in the image processing block is validated off-line using maps of burned areas produced by the Copernicus Emergency Management Service. The results of the validation shows an overall accuracy (considering the classes of burned and unburned areas) larger than 95% and a kappa coefficient larger than 80%. For what concerns the class of burned areas, the commission error is around 1%−3%, except for one case where it reaches 25%, while the omission error ranges between 6% and 25%.

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