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20162
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Subject: Farm, Machinery and Power × End date: 2016 × Start date: 2016 × Type: Thesis × Thesis Type: M.Tech ×
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    Ergonomic evaluation of walk behind type self propelled paddy transplanter
    (Punjab Agricultural University, Ludhiana, 2016) Goyal, Gagandeep; Chhuneja, N. K.
    The self propelled walk behind type paddy transplanters are gaining popularity as they require comparatively lesser initial investment and are easy to operate and maintain as compared to riding type. The ergonomic aspects of walk behind paddy transplanter are of great importance as the operator has to walk behind the machine for a distance of about 10-20 km for 8-hours a day and that also under puddled field conditions. Besides walking in field, stress due to mechanical vibrations, human workload, noise, etc. also affect performance of the operator. The research was planned to study the effect of operational parameters of walk behind self propelled paddy transplanter on physiological parameters, noise, vibrations and work-rest schedule. Hand-arm vibrations were the maximum along x-axis (vertical) and the minimum along y-axis (lateral). Soil type had non-significant effect on vibrations acceleration. However, vibrations increased with increase in forward speed of the paddy transplanter. The mean values of vibrations total value varied from 8.6 to 14.0 m/s2 among all the treatments. The equivalent 8-hours vibrations exposure was found to be ranging between 7.4 and 12.0 m/s2, which was very much higher than the limiting value of 2.8 m/s2 for safe operation of machine. The mean values of sound pressure level varied from 74.0 to 85.7 dB(A) among all the treatments. The mean values of oxygen consumption varied between 607.6 and 1052.6 ml/min with corresponding relative load between 18.1 and 37.3% among all the treatments. The mean values of energy expenditure rate varied between 12.7 and 22.0 kJ/min. The physiological workload was found to be in the category of light work at 1.3 and 1.8 km/h of forward speed of paddy transplanter; but, as moderate work at a forward speed of 2.3 km/h. The physiological responses viz. heart rate, volume of oxygen consumption, discomfort ratings and noise were within the desired limits at the maximum available forward speed of 2.3 km/h of the machine, which also gives the maximum possible field capacity. At this forward speed, a work schedule of about 45 minutes of operating the paddy transplanter followed by a rest of about 15 minutes is desired, which can be easily achieved if the two workers interchange their work after every 45 minutes.
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    ThesisItemOpen Access
    Development of Relationship between Remotely Sensed Data at Different Crop Growth Stages and Yield Monitor Data for Maize Crop
    (Punjab Agricultural University, Ludhiana, 2016) Sanodiya, Rajeshwar; Manjeet Singh
    The average yield of maize in India is only 2.47 tonnes/ha as against the global average of 5.47 tonnes/ha. To meet the food grain requirement of 480 million tonnes by the year 2050 in India, with the increasing challenges of biotic-abiotic stresses experienced by crops, introduction and adoption of modern technologies in Indian agriculture is inevitable. Precision farming is one of the technology which helps to find these goals. Monitoring of crop growth and forecasting its yield well before harvest is very important for better crop and food management. Hence, the study has been carried out to develop the empirical relationship between remotely sensed data at different crop growth stages and yield data for maize crop. Spectroradiometer, infrared camera, N-Tester and chlorophyll content meter (CCM) were used to collect data at different growth stages of the crop to develop relationship with the yield monitor data. The near infrared (NIR) camera was mounted on parrot AR. Drone 2.0 frame for image acquisition. Maize field was harvested by the combine harvester mounted with yield monitor to generate the yield map of the field. The average yield of the field recorded by yield monitor was 3913.9 kg/ha with standard deviation of 390.12 kg/ha and coefficient of variation of 9.33 %. The data revealed that the grid size has non-significant effect on yield and error at 5 % level of significance. Statistical linear regression models were used to develop empirical relationship between the sensor data and yield at three growth stages of maize crop. The yield prediction equations have maximum coefficient of determination (R²) i.e. 0.90, 0.84, 0.86 for NDVI (R630-690 and R760-900), N-Tester and NDVI (NIR camera) respectively at silking stage (R1). While for CCI, the maximum coefficient of determination i.e. 0.87 was observed at dough stage (R4). All sensor values like NDVI, CCI and N-tester values were positively correlated with yield data at all growth stages of maize. The data revealed a close linear relationship between NDVI (NIR camera) and NDVI (R630-690 and R760-900) with coefficient of determination (R2) value 0.80. Similarly there was a close relationship between CCI and N-tester values with coefficient of determination (R2) 0.79. It was concluded that the silking stage (R1 stage) i.e. 55 DAP was the most prominent stage for yield prediction using NDVI. Yield can be predicted 48 days before harvesting using reflectance data captured by spectroradiometer.