Plantation level monitoring

Project Title

Plantation level monitoring

Project Area of Specialization Artificial IntelligenceProject Summary

This project represents the idea of monitoring the rate of greenery or trees around Pakistan, area wise through analyzing the google earth satellite images. This project concentrate on a single tree, groups of trees, or greenery which don’t come under the definition of forest. Karachi is an urban, most populated city. The population of Karachi is increasing every year and with the increase in population, the area of Karachi is becoming highly air polluted due to automobiles' smoke emitted by rickshaws, cars, bikes, buses, other vehicles, burning of garbage, oven fires, and industrial emissions. The trees can reduce air pollution so the attention of the project is to improve the living conditions of people and on how to reduce the environmental affect by monitoring the ratio of trees or greenery or by suggesting the need for more trees in a particular area. The chosen approach is to collect the aerial satellite images from the google earth engine and then various methods of image processing are applied to map the trees on google earth satellite images, then we can calculate the ratio of greenery by comparing the greenery of the specific area with the overall area length. We will also provide a web application where people can also see the ratio of greenery in any area of Karachi.

Project Background

A smart AI based solution  is the next generation of monitoring greenry of particular region, old method and techniques are time taken approaches with not much efficiency as AI can hold, the most popular applications of AI is agriculture monitoring that has been deployed in many rural ares arround the world We just simply putting AI in greenry monitoring of Karachi in which Predictive analytic may help to increase the rate of greenry. This correct knowledge of greenry rate may help us to get ready for any upcomming heat wave crises by giving us approximate idea of how much plantation is required in a particular area for a sustainable environment.

Project Objectives

Cities are struggling to cope with the adverse health consequences as temperatures increase globally, and urban sprawl drives people into concrete spaces. Karachi, the largest city in Pakistan, is growing rapidly because it is Pakistan's largest industrial and commercial hub. The key causes of air pollution in Karachi are rising urban population, degree of industrialization, deploring conditions of civic amenities, and traffic congestion. This involved not only increased pain and fatigue, but breathing issues, headaches, heatstroke, and even mortality associated with heat.

Trees and greenery can help reduce air pollution both by directly removing pollutants and by reducing air temperatures. One way to achieve this is through monitoring trees or greenery ratio in a particular area or by suggesting the need for more trees in a particular area.

Hassan Abbas, an environmentalist, and climate scientist leaned his full support. “Growing plants have the synergistic advantage of cooling urban heat, carbon sequestration, clean air, and aesthetics,” he said.

It’s high time for us to take a step forward and work on such technologies to improve the living condition of our country and its people.

• Objectives
• To determine the areas where greenery ratio are low.
• To develop an application that can monitor the greenery ratio of Karachi area wise.

Project Implementation Method

Methodology and Equipment/Tools

We will use  aerial images data set obtained from google earth engine, than we apply image processing techniques to detect trees and plantation around each pic first we clean the data set if needed than afteapplying filteration on images we combine pixel-level classification followed by global optimiza-tion to generate an image segmentation of tree and non-tree regions. Based on the image segmentation, we adopt template matching to locate each individual tree crown and provide an estimate of its crown size. After that we identify grass areas based on different grass colour. We will than calculate all the  areas of tree and grass region individually than calculate a ratio of greenry by comparing greenry area with the overall area of image. During the process we apply multiple techniques and models of machine learning or deep learning to get maximum possible accuracy after testing we go with the process of developing a web based applications which would tell greenry level based on different areas of karachi.

Benefits of the Project

• Expected Outcome

The users and government can have a visually appealing picture of what is the current situation of plantation in Karachi and what measures they need to take.

• Direct Customers / Beneficiaries of the Project

If this project is expanded to a bigger level, it can used by the government. In this monetary inflation a long time taken and costly survey can not be a good choice for organization or government to do. A quick, accurate and self-improving AI-based software is required to automate the process that can save time and money which can be used for any other important task of people. AI suggestion or recommendation is also something powerful that we can implement furthur in extended version of project to enhance the software at such level where how much and how many  trees are required per area is also recommende by AI best on data and stats

In 2015, South Asia experienced one of its most severe heat waves in living memory, with 3,500 recorded deaths across the subcontinent. In Karachi, Pakistan’s largest city with a population of 20 million, 1,300 people died. The city was ill-prepared to deal with the scale of the crisis.Plantation plays an important role in maintaining the balance in nature. It has a great impact on the environment by reducing incidences of global warming The temperature in Karachi is changing one degree a year! Since the overall population rate due to traffic and unregulated industrial emissions consist of 68.5% air pollution so the level of plantation knowledge certainly provide  us an overview that in which area plantation required more. Organizations that plants thousand trees can know where should more tree required with the help of AI.

Technical Details of Final Deliverable

 Tools

Python

Plotly

Tensorflow

Keras

Deepl Learning techniques:

CNN

Guassian surface fitting

Application backend

Django

Rest framwork api

Python and python libraries

Frontend

Html css mobirise booystrap javascript

Final Deliverable of the Project Software SystemCore Industry ITOther Industries Others Core Technology Artificial Intelligence(AI)Other Technologies Cloud InfrastructureSustainable Development Goals Climate ActionRequired Resources

Item Name	Type	No. of Units	Per Unit Cost (in Rs)	Total (in Rs)
			Total in (Rs)	15000
software subscriptions	Equipment	2	5000	10000
documentation	Miscellaneous	1	5000	5000