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Gaikwad, B. B.
- Automation in Transplanting:A Smart Way of Vegetable Cultivation
Abstract Views :266 |
PDF Views:80
Authors
Affiliations
1 ICAR-Central Institute of Agricultural Engineering, Bhopal 462 038, IN
2 College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur 313 001, IN
3 ICAR-National Institute of Abiotic Stress Management, Baramati 413 115, IN
1 ICAR-Central Institute of Agricultural Engineering, Bhopal 462 038, IN
2 College of Technology and Engineering, Maharana Pratap University of Agriculture and Technology, Udaipur 313 001, IN
3 ICAR-National Institute of Abiotic Stress Management, Baramati 413 115, IN
Source
Current Science, Vol 115, No 10 (2018), Pagination: 1884-1892Abstract
Vegetable transplanting is a labourious and timeconsuming field operation when performed manually. The semi-automatic vegetable transplanters are cumbersome to operate due to limitations on manual feeding rates of seedlings which vary with respect to work duration and skill of the operator. Automation in the field of vegetable transplanters has provided opportunities for savings in labour and time required for transplanting operation in open field and controlled environmental structures, i.e. shade nets or polyhouse. The advent and recent advances in transplanting technologies suggest ample scope of working on automated seedling pickup and drop mechanisms using robotics. Use of seedling pickup mechanism in automatic transplanters can repeatedly extract single seedling automatically from the seedling pro-tray with the help of a pair of pins or forks and drop at predefined location. In general, these systems comprise either a machine vision system or end-effector mechanism for extracting the seedling; gripper and a manipulator; indexing drum-type seedling removal device with ejector; or a pick-up system, feeding system and a planting system. Such automated systems have helped ease the transplanting operation and efficient planting of seedlings by maintaining the accuracy, precision and effectiveness in planting seedlings with minimum human intervention. This study highlights the research gaps and developments in smart transplanting technologies used in the field of vegetable cultivation.Keywords
Agriculture, Automation, Smart Farming, Vegetable Transplanting.Full Text
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- Yield Prediction in Wheat (Triticum aestivum L.) using Spectral Reflectance Indices
Abstract Views :182 |
PDF Views:71
Authors
N. S. Chandel
1,
P. S. Tiwari
1,
K. P. Singh
1,
D. Jat
1,
B. B. Gaikwad
1,
H. Tripathi
1,
K. Golhani
1
Affiliations
1 ICAR-Central Institute of Agricultural Engineering, Bhopal - 462 038, IN
1 ICAR-Central Institute of Agricultural Engineering, Bhopal - 462 038, IN
Source
Current Science, Vol 116, No 2 (2019), Pagination: 272-278Abstract
Influence of nitrogen on vegetative growth of wheat is significant, and can be monitored and assessed using vegetation indices derived from canopy reflectance at different phenological growth stages. The aim of the present work was to establish a regression model for yield prediction of wheat using spectral reflectance indices (SRIs), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and soil adjusted vegetation index (SAVI) for selected phenological growth stages of wheat. The canopy spectral reflectance was recorded during three winter seasons (2014–2017) for irrigated wheat. A hyperspectral library of canopy reflectance was developed, which enables the study of spectra independent of different nitrogen management practices. It indicated that the precise level of nitrogen for irrigated wheat may be 90 kg ha-1 in vertisols under agro-climatic of central India. Coefficient of variation (CV) was determined based on significance test between eight levels of nitrogen and SRI values. On the basis of CV, NDVI and NDWI were selected among the four spectral indices for the study of correlation between grain and biomass yields and nitrogen levels for four growth stages, viz. tillering, booting, heading and milking. A regression model was developed to find the best representative stage for yield prediction among the four stages. The regression model indicated that the relations of NDVI with grain and biomass yields were stronger in the heading stage, and it resulted in 96% accurate estimation of grain and biomass yields in irrigated wheat.Keywords
Nitrogen Management, Spectral Reflectance, Vegetation Indices, Wheat, Yield Estimation.Full Text
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