Diversity & Team Performance

Why of interestest?

Size and complexity of tasks requires a team approach

Managing teams challenging

How can we choose / develop effective teams?

How can we get the best performance from teams?

What sorts of diversity might affect team performance?

knowledge/skills/experience

finding 1

finding 2

finding 3

age

sex

finding 1

finding 2

ethnic background

to task

finding 1

finding 2

to working in teams

to time management

How many diversity affect team performance?

Direct effects

diversity of views can reduce "groupthink"

greater array of ideas and skills can improve problem solving

too much diversity in views can lead to "information overload"

* Can training with Method X help overcome this problem?

some differences in views can be hard to reconcile

Indirect effects

surface level diversity can trigger stereotyping and prejudices which can lead to poor social cohesion

Differences in attitudes towards time management and the task can lead to interpersonal conflict

Student groups often have troble meeting due to differing timetables and work commitments

Theoretical Perpectives

Cognitive load theory

Theory of Group Dynamics

Cross cultural of communication

Etc.

How can the impact of diversity on team performance be measured?

Observational methods

Advantages:

Disadvantages:

Self-report methods

+:

-:

Psychological scales

Types

A

B

C

+/-

How can potential problems be overcome?

* Can training in online group project management tools help overcome getting together issues in students group?

training in groupwork

setting positive groups norms (e.g. fastering team psychological safety)

Allowing enough time for teams to reconcile differences

Faster frequent collaboration to build team bonds

Application of UAVs to monitor crop growth and crop water stress for informed decision making in agriculture

What sorts of diversity might affect UAVs images?

Specification of UAVs cameras.

What sorts of diversity might affect crop water stress?

Cropping pattern

Soil surface temperature.

Cropping pattern.

Soil strutures

Soil textures

Irrigation

Evaporation

Why of interestest?

The increasing trend of young smart farmers will be the owners of their farms.

Lack of labor in large-scale farming.

Need technologies use in smart farming.

Using precision agriculture to predict crop productivities.

Using information to support decision-making in agriculture.

The satellite data imageries are expensive.

How much data affect UAVs images?

Sunlight

The different altitude flights

Season

Cropping pattern

Theoretical Perpectives

Image processing

Soil-Water Properties

Crop Water Requirement

Surface System Design for Irrigation

Tools & Software

DJI GO 4

Pix4Dcapture

WebODM

Pix4D

QGIS

Python

MS Office

Power BI

How are you going to do it?

Why do you want to do research on this topic?

What do you want to do research?

Gerneral Topic

Why did you choose the topic?

What do you want to find out?

Why is it important to investigate it?

What is your opinion about the topic?

Do you have any previous knowledge about the topic that you are going to study?

Literature Survey Matrix

Author

1. Number/letter/classification or code

2. Key concepts and their descriptions/definations

3. Research questions/Objectives of the study/Hypotheses

4. Main ideas/main Finding

5. Methods and instruments used

6. Data quality qualitative or quantitative

7. Conclusions made

8. Explain how this research is useful for writing your research

ChenWang et al., 2019 (chen.wang@hutton.ac.uk)

1. Number/letter/classification or code

https://doi.org/10.1016/j.ijdrr.2019.101139

2. Keywords

Sponge city

Flood risk management

3D visualization

GIS

Low impact development

3. Research questions/Objectives of the study/Hypotheses

1. Subtopic

2. Subtopic

3. Subtopic

4. Main ideas/main Finding

Paragraph : sponge city design features.............................. ........................................................................................... ........................................................................................... ...........................................................................................

Compare

Analyse

5. Methods and instruments used

the 3D model were PC-based

3D virtual components

LID scenarios

VR environment

exported into a viewer (Octaga)

coded in JavaScript

The numerical model

6. Data quality qualitative or quantitative

Fengxi new town (study area)

7. Conclusions made

2D simulation results with design rainstorm

Flood simulation and LID scenarios in VR

Paragraph: Should related in research question 1.................. ....................................................................................................... .......................................................................................................

Paragraph: Should related in research question 2.................. ....................................................................................................... .......................................................................................................

Paragraph: Should related in research question 3.................. ....................................................................................................... .......................................................................................................

8. Explain how this research is useful for writing your research

Bipul Neupane, Teerayut Horanont*, Nguyen Duy Hung (*teerayut@siit.tu.ac.th)

1. Number/letter/classification or code

https://doi.org/10.1371/journal.pone.0223906

2. Key concepts and their descriptions/definations

A deep learning (DL) based method to precisely detect and count banana plants.

Using high resolution RGB aerial images collected from UAV.

Improving the result of sample images of a commercial banana farm in Thailand (previous resulted 78%).

3. Research questions/Objectives of the study/Hypotheses

The automatic counting of banana plants during early stage of growrh

4. Main ideas/main Finding

The normal RGB images resulted less than 78.8%

5. Methods and instruments used

Using three processing method to improve the previous result.

Linear Contrast Stretch

Synthetic Color Transform

Triangular Greeness Index

Summary

To enhance the vegetative properties of orthomosaic.

To generate multiple variants of orthomosaic.

Convolutional Neural Network (CNN)

A deep learning (DL) based method to precisely detect and count banana plants.

Parameters : Altitudes : 40m : 50m: 60m

Speed (m/s)

Shooting Angle

Front lap

Resolution (cm/px)

Number of Flight

Time of Flight

Flight Duration (minute: second)

Number of Images Collected

Area (acres)

6. Data quality qualitative or quantitative

7. Conclusions made

96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters.

Merging the detection results of 40 and 50 meters dataset could detect the plants missed by each other, increasing recall upto 99%

8. Explain how this research is useful for writing your research

Gilles Rabatel et,al

1. Number/letter/classification or code

J.A. Lozano, J.A. G´amez, and J.A. Moreno (Eds.): CAEPIA 2011, LNAI 7023, pp. 333–342, 2011. c Springer-Verlag Berlin Heidelberg 2011

2. Keywords

NDVI

aerial

imaging

multispectral

near-infrared band

3. Key concepts and their descriptions/definations

To obtain simultaneously the near-infrared and red bands from standard RGB camera

The camera has been removed the near-infrared blocking filter.

The two new generation SLR cameras

Canon 500D

Sigma SD14

NDVI values obtained from these devices have been compared with reference values for a set of soil and vegetation luminance spectra.

4. Research questions/Objectives of the study/Hypotheses

How a single modified camera associated with a low-pass filter can be extend to the newest generation of commercial RGB imaging sensors, in order to combine robust spectral information on vegetation with very high spatial resolution?

5. Main ideas/main Finding

The quality of the results shows that NDVI bands can now be acquired with high spatial resolution 2D imaging devices, opening new opportunities for crop monitoring applications.

6. Methods and instruments used

The BSOP (Band Simulation by Orthogonal Projection) Theoretical Approach

7. Data quality qualitative or quantitative

8. Conclusions made

9. Explain how this research is useful for writing your research

Kulbacki, M., Segen, J., Knieć, W., Klempous, R., Kluwak, K., Nikodem, J., … Serester, A. (2018)

Survey of Drones for Agriculture Automation from Planting to Harvest

Number/letter/classification or code

https://doi.org/10.1109/INES.2018.8523943

INES 2018 - IEEE 22nd International Conference on Intelligent Engineering Systems, Proceedings (2018) 000353-000358

Key concepts and their descriptions/definations

Research questions/Objectives of the study/Hypotheses

Remote Sensing (RS) technologies combined with Unmanned Aerial System (UAS) platforms to support and develop selected agriculture operations like map or sensor-based Variable Rate Application (VRA).

Main ideas/main Finding

Methods and instruments used

Data quality qualitative or quantitative

Conclusions made

Explain how this research is useful for writing your research

Chapter 1 Introduction

1. Background of the study

Context of this problem, the situation or environment where it can be observed.

Subtopic

Current trends related to the research problem or topic; available or identifified facts.

Subtopic

How the problem was/is addressed or what has been done so far.

Subtopic

Definitions/explanation of terms/key words.

Subtopic

Other important details that are important for the readers to understand the topic or problem you are researching about.

Subtopic

2. Statement of the problem

Managing and utilizing resources ensuring a sustainable natural environment

Laborious operations under conditions not conducive to human productivity

Advancement of technologies in other industries -attract labor force away

An increased demand for higher product quality

The necessity to modernize agriculture using technology

The trend towards organic farming and

To employ human intelligence and machine power in a sustainable and economical manner.

3. The rationale of the research

เหตุผลของการวิจัย

Who-Why-How

What do you want to do research on?

What do you want to find out?

What is your opinion about the topic?

Why do we need to do research on that topic?

Why did you choose the topic?

Why is it important to investigte it?

How are we going to do research on that topic?

Do you have any previous knowledge about the topic that you are going to study?

4. Objectives

Research Questions

Hypothesis

requireds a scientific method to test or prove

Assumtions

don't need to test it

5. Scpoe of the study

6. Limitation of the research

7. Conceptual Framework

Chapter 2 Literature Review

1. An Introduction of Drones in Agriculture

Type of Drones

Role of Drones in Farming

Challenges and Opportunities in Agriculture

2. UAVs image processing RGB

3. Vegetration Indics

4. Soil moisture stress index

5. Irrigation requirements

6. Upscale with satellite images

Chapter 3 Materials and Methods

1. An Introduction of Tomato Plant

Plant characteristic

Flower and fruit characteristic

Field condition

2. Toamto growth stages and development

Variety of tomato

3. Plant nutrient and crop production

Nitrogen

Phosphorus

Potassium

Calcium

4. Drought stress

Plant response to drought stress

Phtosynthesis

Morphological and anatomial characteristics

Oxidative stress as a secondary stress

Some benefits of drought stress

5. Silicon

Form of silicon

Silicon uptake and transport in plants

Si mediated alleviation of drought stress

6. Seed proming

Seed proming with silicon

Thesis Topic

Pix4Dmapper

Route

Time & Duration

Croping Pattern

Start

Middle

Havesting

Flight plans

Soil Moisture Content

Land Surface Temperature (LST)

NIR Images

Instruments

SM

Irrigation management

Crop yield

Correlation

Vegetation Indices (VI)

ENDVI

NDVI

NDVI (Reverse)

Land Surface Temperature (LST)

Soil Moisture Contct

Water Stress Index

Image processing

WebODM

Docker Desktop

K850 (NIR Images)

Orthomosaic

NDVI algorithm

Filter: NRG

NIR

Red

Green

Color: RdYlGn

Red

Yellow

Green

Mapping NDVI

HMR (RGB Images)

Orthomosaic

ENDVI algorithm

Filter: NGB

NIR

Green

Red

Color: RdYlGn (Reverse)

Red

Yellow

Green

Mapping ENDVI

QGIS

Python Code

Mapping NDVI

Adjust color

Mapping LST

UAV

DJI Mavic 2 Pro

RGB images

NIR images

Fullspectrum converted camera

Filters

K665 (Red 668 nm)

K590 (Green 560 nm)

K550 (Green 560 nm)

K850 (NIR 840 nm)

K720 (RedEgde 717 nm)

IR Chrome (...... nm)

Camera settings

Resolution

pixel

Speed

Altitudes

Raw Images

Objective 3

Objective 2

Objective 1