Development of a user‐friendly automatic ground‐based imaging platform for precise estimation of plant phenotypes in field crops

Author:

Gatkal Narayan1,Dhar Tushar2,Prasad Athira3,Prajwal Ranganath2,Santosh 4,Jyoti Bikram5,Roul Ajay Kumar5,Potdar Rahul5,Mahore Aman5,Parmar Bhupendra Singh5,Vimalsinh Vala5

Affiliation:

1. Department of Farm Machinery and Power Engineering Dr. Annasaheb Shinde College of Agricultural Engineering and Technology, MPKV Rahuri Maharashtra India

2. Department of Farm Machinery and Power Engineering, Division of Agricultural Engineering ICAR‐IARI New Delhi India

3. Department of Farm Machinery and Power Engineering, Kelappaji College of Agricultural Engineering and Technology Kerala Agricultural University Tavanur Kerala India

4. Department of Farm Machinery and Power Engineering, College of Agricultural Engineering University of Agricultural Sciences Raichur Karnataka India

5. Agricultural Mechnization Division ICAR‐Central Institute of Agricultural Engineering Bhopal Madhya Pradesh India

Abstract

AbstractPlant phenotyping is the science to quantify the quality, photosynthesis, development, growth, and biomass productivity of different crop plants. In the past, plant phenotyping employed methods such as grid count and regression models. However, the grid count method proved to be labor‐intensive and time‐consuming, while the regression model lacked accuracy in calculating leaf area. To address these challenges, a portable automatic platform was developed for precise ground‐based imaging of field plots. This platform consisted of a frame, an RGB camera, a stepper motor, a control board, and a battery. The RGB camera captured images, which were then processed using MATLAB software. Statistical analysis was performed to compare the results obtained from the grid count, regression model, and image processing techniques. The correlation coefficient (r) between the image processing technique and the regression model for sunflower was found to be 0.98 and 0.97, respectively, whereas for kidney bean it was 0.99 and 0.96, respectively. The minimum and maximum values for leaf area density (LAD) of all selected sunflower leaves were determined to be 0.132 and 0.714 m²/m³, respectively. For kidney bean leaves, the minimum and maximum mean LAD values were found to be 0.081 and 0.239 m²/m³, respectively. Ergonomic aspects of the developed automatic system were studied. The developed system had lower physiological parameters, such as working heart rate of 99 beats/min, work pulse of 18 beats/min, oxygen consumption of 786 mL/min, and energy consumption of 11.5 kJ/min compared to the grid count method. Thus, developed automatic ground‐based imaging system would significantly reduce physiological workload and associated hazards. Therefore, the developed method proved satisfactory in comparison to other techniques, offering a quick, efficient, and user‐friendly approach for determining plant phenotypes.

Publisher

Wiley

Subject

Computer Science Applications,Control and Systems Engineering

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