Smart Crop Field Monitoring and Identification Using satellite images

The main idea of this project is to identify fields just by looking at the images from satellites. As the harvesting season starts farmers and local land owners as well as government starts doing land surveys for counting the no of fields dedicated to each crop. This will help in estimating the tota

Project Title

Smart Crop Field Monitoring and Identification Using satellite images

Project Area of Specialization

Artificial Intelligence

Project Summary

The main idea of this project is to identify fields just by looking at the images from satellites. As the harvesting season starts farmers and local land owners as well as government starts doing land surveys for counting the no of fields dedicated to each crop. This will help in estimating the total yield of that crop in that year. As per land surveys, they are time consuming and can take up to a month or two depending on the nature of crop. We want to replace all that land survey by a software which can do the field survey by just looking at the real time images from the satellite. This will save all that amount spent on those land surveys in terms of transport or labor.

Pakistan is facing a huge loss of their economy due to mismanagement in agriculture. By using smart ways, we can improvise our current situations regarding agriculture and use it to build a strong economy. Recently image processing and convolutional neural networks have become very efficient and on top of that object detection gives a very good way of exploring the images deeply.

Project Objectives

• Study and understand literature that is based of image processing and Object Detection.
• Identify major causes of mismanagement in field monitoring and come with a solution using artificial intelligence.
• Critically analyze the steps needed in the edge detection of crops for their detection.

Project Implementation Method

Using satellite images of different bands and resolution to analyze the fields in a particular area.

Classification was based on pre-learned material, which the algorithms based their assumptions on when evaluating the imagery. Algorithms were “shown” different features consisting of multiple images representing the parcel in hand, and told what was the crop in question in each feature. First the algorithm was just shown feature of the parcel and they had to “guess” which crop was in it. Eventually as the algorithms were told each time if they were right or wrong, they were able to evolve better and better in guessing the crops.

In the first step the algorithm will identify the boundaries of each field.

After that it will extract the features of that particular field and identity the type of crop. Finally, each field will then be highlighted with different color to differentiate it from other.

Benefits of the Project

The results that will be achieved by this project are beneficial for the government as well as for the person who monitoring the crops and also the landlord of the land. They will be able to monitor the identification of crops using a web application. By this project identification of crops through satellite imaginary become more convenient for the farmers. By having an efficient way of monitoring one can take steps to improvise the agricultural situation, which is ultimately beneficial for the country’s economy also.

Technical Details of Final Deliverable

Data regarding the project. Documentation of the project specification and requirements.

Data regarding the project. Documentation of the project specification and requirements.

A final model with enhanced performance

Verified functionalities and a working model

Tested modules and report

Final Deliverable of the Project

Software System

Core Industry

Agriculture

Other Industries

IT

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Decent Work and Economic Growth, Industry, Innovation and Infrastructure

Required Resources

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