Soil moisture estimation for sunflower crop by remote sensing imagery using UAV

Moisture content in soil decides strength of soil. Traditional methods Consume more time and man work. The water status in a soil is usually determined by taking multiple punctual measurements over the field, but this method often fails to properly represent the actual field

Project Title

Soil moisture estimation for sunflower crop by remote sensing imagery using UAV

Project Area of Specialization

Artificial Intelligence

Project Summary

Moisture content in soil decides strength of soil.
Traditional methods Consume more time and
man work. The water status in a soil is usually
determined by taking multiple punctual
measurements over the field, but this method
often fails to properly represent the actual field
moisture.The evolution of sensors and Unmanned Aerial Vehicle (UAV), indicators derived from remotely sensed
images allow the characterization of entire
fields with enough resolution and minimum
time to analyze soil/plants.Therefore idea of this project is to use “Remote Sensing Imagery data using
Unmanned Aerial Vehicle (UAV)” in
Estimation of Soil Moisture of Sunflower Crop. The field selected was located at Sindh Agricultural University,
Tandojam.

Project Objectives

• To plan the flight of DJI Spark Drone
• To maximize the yield of sunflower Crop and to estimate soil moisture.
• To develop the model for Soil Moisture estimation using Machine Learning Algorithm.
• To validate the model with the data available at ENGRO lab

Project Implementation Method

At first, the survey of field was done at Sindh Agricultural University,Tandojam. The preparation of Dji spark was done in order to understand its operation. Images of Sunflower crop was captured by Unmanned Aerial Vehicle Drone on  different stages i.e before sowing, after sowing, germination. The next step was image pre-processing of acquired images for balancing the brightness,saturation by applying filters.The next step is segmentation of images according to Region Of Interest. Then the next step is extraction of feautures,for this Red Green Blue (RGB) model will be used. Then the model will be train and test. After this it will be classified .

Benefits of the Project

The moisture of any crop is estimated manually uptil now,which is time consuming. Traditional methodology includes small specimen size, molds with rigid wall which leads to error. Farmers used to apply their old traditional methods and knowledge being passed from their ancesstors..Machine learning techniques and deep learning algorithms are used to detect the soil moisture condition with more accuracy and sensitivity at different environmental conditions.The passive sensors can also be used but predict the soil moisture condition with low resolution that is why DJI Spark UAV Drone is being used for this project.It gives high resolution images ------ and can fly at the height of ---.

Technical Details of Final Deliverable

We had developed model to estimate soil moisture of sunflower crop through digital images with help of Drone and software based model of machine learning which will be evaluated to get the results.

Final Deliverable of the Project

Software System

Core Industry

Agriculture

Other Industries

Core Technology

Artificial Intelligence(AI)

Other Technologies

Sustainable Development Goals

Decent Work and Economic Growth, Industry, Innovation and Infrastructure, Responsible Consumption and Production

Required Resources

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