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Chinnusamy, Viswanathan
- Design and evaluation of an automatic control system for hydroponics cultivation of mint
Abstract Views :66 |
Authors
Ajay N. Satpute
1,
N. Patel
2,
A. K. Mishra
3,
D. K. Singh
3,
Viswanathan Chinnusamy
4,
Cini Varghese
5
Affiliations
1 ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284 003, IN
2 National Institute for Transforming India, New Delhi 110 001, IN
3 Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
4 Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
5 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 002, IN
1 ICAR-Indian Grassland and Fodder Research Institute, Jhansi 284 003, IN
2 National Institute for Transforming India, New Delhi 110 001, IN
3 Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
4 Division of Plant Physiology, ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
5 ICAR-Indian Agricultural Statistics Research Institute, New Delhi 110 002, IN
Source
Current Science, Vol 127, No 11 (2024), Pagination: 1335-1343Abstract
Hydroponics is emerging as a beneficial technique to augment food production in which the quality and yield of a crop are improved by accurately maintaining the desired electrical conductivity (EC), pH and dissolved oxygen (DO) in the nutrient solution. This article describes the development of an automatic control system for mint cultivation in hydroponics that consists of EC, pH, and DO sensors which continuously monitor the respective parameters and control their value in nutrient solution with the help of microcontroller and actuators. The developed system maintained the EC, pH and DO of the nutrient solution within their desired limits of 2.0–2.5 dS m–1, 6.0–6.5 and above 8 ppm respectively. The plant height, average leaf area per plant and total chlorophyll content of the mint plants varied between 18.9–20.3 cm, 15.69–18.45 cm2 and 23–30 (SPAD units) respectively. The results of this study emphasize the system’s effectiveness in monitoring and controlling the parameters of the nutrient solution thereby maintaining favourable growth conditions in the hydroponic cultivation of mint.Keywords
Automatic control system, hydroponics, nutrient solution, sensors.Full Text

- Genome editing: a boon for plant biologists, breeders and farmers
Abstract Views :349 |
PDF Views:182
Authors
Affiliations
1 National Academy of Agricultural Sciences, New Delhi 110 012, IN
2 ICAR-National Rice Research Institute, Cuttack 753 006, IN
3 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
1 National Academy of Agricultural Sciences, New Delhi 110 012, IN
2 ICAR-National Rice Research Institute, Cuttack 753 006, IN
3 ICAR-Indian Agricultural Research Institute, New Delhi 110 012, IN
Source
Current Science, Vol 123, No 1 (2022), Pagination: 15-19Abstract
No abstract.Keywords
No keywords.References
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- High-resolution reconstruction of images for estimation of plant height in wheat using RGB-D camera and machine learning approaches
Abstract Views :98 |
Authors
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
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
Source
Current Science, Vol 127, No 12 (2024), Pagination: 1440-1446Abstract
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.Full Text
