Analysis of UAV Image and estimating the density of Sugarcane Yield

//Analysis of UAV Image and estimating the density of Sugarcane Yield
Analysis of UAV Image and estimating the density of Sugarcane Yield2019-04-22T10:35:34+00:00

Project Description

Project Title: Analysis of UAV Image and estimating the density of Sugarcane Yield

This project aims on developing a GIS plugins for analyzing UAV image and estimating the density of sugarcane.  In this project, a ratio of green pixels in specific size of block are the main factor for classification.  Then, the field work data including height, diameter, and a number stalk will be used to estimate the sugar cane yield. This project aims on developing a GIS plugins for analyzing UAV image and estimating the density of sugarcane.

Project Head

  • Full Name: Sarawut Ninsawat
  • Department: Remote Sensing and Geographic Information Systems
  • Designation: Assistant Professor and Degree Program Committee Chair of RSGIS
  • Achievement: Doctoral of Creative Cities, Urban Information Study Course, Graduate School for Creative Cities, Osaka City University, Japan.
  • Keywords: “Remote Sensing for Agriculture”, “Web GIS” and “Location Based Service”

In this project, a ratio of green pixels in specific size of block are the main factor for classification.  Then, the field work data including height, diameter, and a number stalk will be used to estimate the sugar cane yield.

Since 2015, Asian Institute of Technology (AIT) has been giving full support to MITR PHOL an innovative and research center as they are the only research center who has Research and development (R&D) for Sugarcane production.

The whole project is being funded by MITR PHOL and the total budget for this project is around 2 million baht.

Objectives of this project are:

  1. Finding Sugarcanes all over in Thailand by the use of machine Artificial Intelligence (AI) and Satellites.
  2. Determining if the Sugarcanes are healthy enough for the production or not.
  3. Analyzing how much Sugarcane can they yield in near future.

Duration

Total duration of this project is almost 3 years. There are three phases in this project; the first phase is Monitoring and Managing Sugarcanes all over in Thailand, the second phase is determine how healthy are Sugarcanes and the last phase is analyzing how much sugarcanes can they yield. First Phase has already started from January, 2019 and is expected to finish by October, 2019. In this Phase, Machine AI and Satellites are used to detect and capture images of Sugarcanes in Thailand. In the second phase, special cameras are fixed in satellites which is used to capture vegetation index which tells us if the sugarcanes are healthy enough for production or not. This project is also a first kind of project happening in Thailand and if the expected outputs are positive, it will be further used for detecting corns and other crops for production.

Risks

This project is very challenging and has many risks involved. It is very difficult to detect sugarcanes as other crops like Corn looks the same as Sugarcane. Dr. Sarawut adds, “It is similar as finding diamonds in the ocean”.

Project Details

Skills Needed:

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