Current Science

Preety Dagar ¹, Alka Arora ¹, Mrinmoy Ray ¹, Sudhir Kumar ², Himanshushekhar Chourasia ³, Mohit Kumar ¹, Sudeep Marwaha ¹, Rajni Jain ¹, Viswanathan Chinnusamy ²

Affiliations
1 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 012, IN
2 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
3 ICAR-Central Institute for Research on Cotton, Mumbai 400 019, IN

Abstract

In this study, a pipeline has been proposed where colour image and depth information of wheat plants are captured using an red green blue-depth (RGB-D) camera; later these two are combined to create a three-dimensional point cloud of the plant. The point clouds were processed to calculate the plant height. The results were then statistically analysed with the help of machine learning algorithms, viz. linear regression, support vector machine and artificial neural network (ANN). The comparison of the results shows that ANN performed better than the other two models with mean squared error 189.94, root mean squared error 13.70, mean absolute error 11.40 and mean absolute percentage error 18.73. The proposed study shows a high-precision and low-cost technology that can be widely used for non-destructive measurement of phenotyping parameters for wheat and other crops.

Keywords

3D reconstruction, image processing, Open3D, plant phenotyping, RGB-D imaging.

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