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ThesisItem Open Access Application of machine learning technique for diagnosis of powdery mildew disease in wheat(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-10) Negi, Archana; Nain, A.S.Powdery mildew is one of the most common fungal disease of wheat caused by an obligate biotrophic pathogen Blumeria graminis f. sp. tritici. Present investigation was conducted in rabi season 2020-21 at Norman E. Borlaug Crop Research Center, Pantnagar, Uttarakhand. The study was undertaken to create a disease diagnosis model for powdery mildew in wheat. Ten different deep learning approaches namely VGG16 (without augmentation), VGG16 (with under sampling), VGG16 (with over sampling), ResNet50 (without augmentation), ResNet50 (with under sampling), ResNet50 (with over sampling), ResNet50 (with under sampling and augmentation), EfficientNetB3 (with augmentation), EfficientNetB5 (with augmentation) and EfficientNetB7 (with augmentation) were used to check the best model for disease diagnosis. The accuracies attained by these algorithms were 61.7%, 59 %, 77 %, 58-63 %, 55-61 %, 74.7 %, 74%, 74.8 %, 73.6 % and 75.1 %, respectively. Automatic computer system for detecting and classifying of diseases is very important for efficient management. The present study will provide the opportunity for disease management by using advanced learning technologies with least interference of mankind. The study was also conducted to check the influence of weather parameters with disease progress of powdery mildew. Infection rate and PDI were used to analyze the effect of weather variables. PDI was positively correlated with both maximum (r=0.82) and minimum temperature (r=0.61) and positively for bright sunshine hours (r=0.81) while with morning (r=-0.73) and evening relative humidity (r=-0.77), it was negatively correlated. Maximum temperature (r=-0.52) and sunshine hours (r=-0.51) showed a negative correlation with the rate of infection while a positive correlation was seen with morning (r= 0.54) and evening relative humidity (r= 0.61). Step-wise multiple regression analysis was done and a prediction equation was developed (R2=0.45).ThesisItem Open Access Recognition of stripe and leaf rust disease in wheat using artificial intelligence technique(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-10) Vatsala Chand; Nain, A.S.Stripe and leaf rust of wheat are one of the common problems not only in India but also in other wheat-growing areas, which is caused by Puccinia striiformis and Puccinia triticina respectively. Present study was conducted at Norman E. Borlaug Crop Research Center, Pantnagar, Uttarakhand in rabi season 2020-21. The investigation was undertaken to create an auto-detection model for identification of rust disease in wheat using artificial intelligence technique. Seven different deep learning approaches namely ResNet50, VGG16 without augmentation, VGG-16 with augmentation, VGG-16 with augmentation and binary classification, EfficientNetB3 with augmentation, EfficientNetB5 with augmentation and EfficientNetB7 with augmentation were used to check the best algorithm for disease identification. The classification accuracy of 56%, 68%, 70.8%, 74.1%, 69.6%, 70.8%, 71.2% and 73.4% respectively, was attained by algorithm. Automated method for an early detection of a plant disease is vital for precision crop management. The present study provides a groundwork for auto-detection of disease through smartphone. This would be beneficial to the country's farmers, who otherwise faces multiple challenges in diagnosing the disease. The study was also conducted to analyze the relation between weather parameters and leaf rust disease progression. Rate of infection was calculated which show a positively correlated with both maximum and minimum temperature and negative with relative humidity. Regression analysis was done to develop a model for predicting rate of infection, which was found to be quite accurate with R2 value of 0.637.ThesisItem Open Access Studies on Crop Growing Environment Under Climate Change Scenario in Tarai Region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Goel, Shubhika; Singh, R.K.The study is conducted for Tarai region of Uttarakhand regarding trend analysis of weather parameters namely maximum temperature, minimum temperature, rainfall, sunshine hours and evaporation on annual and seasonal basis over the periods from 1981-2020. The moving averages for 5-year, 10-year interval and pentadal, decadal variation has been studied for the above stated parameters on annual basis. Results revealed that, an increasing trend in maximum and minimum temperature of about 0.0004˚C/year and 0.0180˚C/year respectively by graphical method. A decreasing trend in rainfall, sunshine hours and evaporation is observed of about 1.461 mm/year, 0.042 hr/year and 0.028 mm/year respectively by graphical method. Mann-Kendall test has been also performed for trend analysis of above stated parameters. The results revealed the similar trend in the weather parameters but some changes in the Sen’s slope can be observed i.e., in the magnitude of the trend. Similarly, trend analysis on seasonal basis has also been performed for these parameters and it can be concluded that there is a decreasing trend during monsoon and winter season while increasing trend in post monsoon and summer season for maximum temperature. Increasing trend for minimum temperature was found during all the seasons. Decreasing trend was also observed for rainfall, sunshine hours and evaporation during all the seasons over the periods from 1981-2020 for Tarai region of Uttarakhand. There is a decrease in rainfall of about 28.3 mm during monsoon season over the periods ranging from 1981-2020 by Mann Kendall method. This study also focuses on average weekly water balance and its components for this region based on Thornthwaite Mather model. Results revealed that, there is an increase in the water surplus during 1981-2020 when compared with IMD data for Pantnagar during 1971- 2005. Water surplus is found to be 670.0 mm and water deficit is found to be 440.2 mm. Total potential evapotranspiration is found to be 1339.4 mm, which is calculated by Penman Monteith equation during 1981-2020 and has decreased when compared with the PET calculated by IMD for the year 1971-2005 of about 1463.9 mm. The classification of climate has been done for Tarai region of Uttarakhand based on moisture index, results revealed that humid climate exists in this region and length of growing period is found to be about 225 days which indicated a good crop growth in this region. According to Subramanyam (1982), if MAI (Moisture Adequacy Index) lies below 40% in the region, then crop can only be grown by proving supplemental irrigation to fulfill crop water need.ThesisItem Open Access Performance of CROPGRO model for simulating yield of Mungbean (Vigna radiata) under Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-02) Rana, Smriti; Rajeev RanjanThe present study was conducted at Norman E. Borlaug Crop Research Centre of G.B Pant University of Agriculture and Technology, Pantnagar during kharif 2019 to analyse the effect of different sowing date and row spacing on the growth and development of Mungbean crop as well as study the thermal requirement of the crop in Tarai Region of Uttarakhand. The experiment with two factors; sowing date (31st July, 10th August and 21st August) and row spacing (20 cm×10 cm, 25 cm×10 cm and 30 cm×10 cm) was laid out in Random Block Design with three replications. The variety of mungbean taken for the experiment was Pant Mung-2. All the recommended cultural practices followed during the crop growth period. The various growth parameters under observations were periodically recorded to evaluate the treatment effect on growth and yield of crop. The heat unit requirement study shows that various thermal indices i.e., GDD, HTU and PTU reduced with delay in planting dates. Higher values of thermal units were positively correlated with good crop growth and yield of crop. It is observed that timely sown crop (D1,31st July)exhibit best growth and yield as the favourable environmental conditions coincided with heat unit requirement of different phenophases of mungbean. The experiment analysis suggests that the crop sown on 31st July with 30 cm×10 cm performed better among all treatment. The yield contributing parameters and grain yield were found to be reducing with delay in sowing date and narrow row spacing. The CROPGRO Model was successfully calibrated against the emergence, plant height, physiological maturity, straw yield, biological yield and grain yield. The observed grain yield was comparable with the simulated grain yield with % RMSE of 5.72. Among pulses, the green gram [Vigna radiata (L.) Wilczek] is one of the most important and extensively cultivated pulse crops. The weather is an important factor affecting the production the mungbean. The sensitivity analysis of the CROPGRO Model for mungbean was performed to see the effect of changing weather variables i.e., mean temperature and CO2 concentration (ppm) on the grain yield of mungbean. The results showed that the crop was highly sensitive to temperature and CO2 concentration. The simulated yield increases with increase in CO2 concentration and vice-versa. In case of mean temperature, the simulated yield decreases with increase in mean temperature and vice- versa.ThesisItem Open Access Calibration of CERES-Maize model and study of thermal requirements of Sweet corn under Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-12) Rawat, Sushmita; Singh, R.K.The present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B Pant University of Agriculture and Technology, Pantnagar during kharif 2019 to analyze the effect of different sowing dates and nitrogen levels on the growth and development of Sweet corn as well as to study the thermal requirements of the crop in Tarai region of Uttarakhand. The experiment with two factors; sowing dates (8th August, 23rd August and 7th September) and nitrogen levels (150 kg N ha-1, 120 kg N ha-1, 90 kg N ha-1 and 60 kg N ha-1) was laid out in Split Plot Design with three replications keeping the sowing dates in main plot and nitrogen levels in sub-plot. The variety taken for the experiment was Sweet corn hybrid Sweet 77. All recommended cultural practices were followed during the crop growth period. The various growth parameters under observations were periodically recorded to evaluate the treatment effect on the growth and yield of crop. The heat unit requirement study shows that the various thermal indices i.e., GDD, HTU, PTU and HUE reduced with delay in planting dates. Higher values of thermal units were positively correlated with good crop growth parameters and higher grain production. Experimental analysis suggests that the crop sown on 8th August with 120 kg N ha-1 performed better among all the treatments. The growth parameters such as crop height, leaf area index and dry matter accumulation were at par in N1 (150 kg N ha-1) and N2 (120 kg N ha-1) treatments. The yield contributing parameters and grain yield were found to be reducing with delay in sowing date and lowering nitrogen doses. The CERES-Maize model was satisfactorily calibrated against the emergence (DAS), anthesis (DAS) and dry matter grain yield. The simulated dry matter grain yield was comparable with the actual observation with R2 of 0.96 and % RMSE of 9.14. Maize crop is grown throughout the country according to the climatic suitability. Weather variables play an important role in deciding the crop growth and productivity. The sensitivity analysis of the CERES-Maize model was performed to see the effect of the changing weather variables i.e., mean air temperature (°C), CO2 concentration (ppm) and solar radiation (MJ m-2 day-1) on the dry matter grain yield production of the crop. The results showed that the crop was highly sensitive to the changes in mean air temperature and solar radiation and responded differently under different growing environment. The simulated grain yield increased with increase in CO2 concentration and vice-versa.ThesisItem Open Access Study of Energy Balance of Sugarcane (Saccharum Officinarum L.) using Remote Sensing and Crop Simulation model in Tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-09) Sharma, Neha; Nain, A.S.The present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar to Study the energy balance of sugarcane using surface energy balance algorithm and CANEGRO model during 2015 and 2016. The sugarcane variety selected for the study was Co-Pant 5224. The performance of the CANEGRO model was reasonably well when compared with the observed crop parameters like Leaf Area Index, fresh cane yield (t/ha), Dry weight Yield of Cane (t/ha) etc. during the period of study. Predicted values through CANEGRO model were very close to the observed values in the experimental year. The model performance was tested on statistical ground on the basis of Index of agreement (d) and RMSE (%). The d value for the analysis was 0.86 and RMSE (%) was 17.28 %, which shows that there was limited error in the predicted values as compared to the observed values. The model was found to be more sensitive to the effect of temperature either decreasing or increasing it than mean temperature, CO2 concentration and Irrigation amount (mm) and Radiation (MJ/m2/day). Calibrated CANEGRO simulation model was also used to analyze the impact of climate change on growth and development parameters of Sugarcane. Leaf Area Index, dry weight yield of cane (kg/ha) and time taken for emergence were found to decrease in the future climatic scenarios (2030-2090). SEBAL is a surface energy balance algorithm predicting evapotranspiration using remote sensing technique. It calculates ET through a series of procedures that generates residual energy flux as precursor of ET. In this study, LANDSAT-8 (OLI+TIRS) satellite images for the crop period (2015-16 and 2016-17) have been utilized for extraction of various components of SEBAL in sugarcane crop. The parameters required for SEBAL procedure includes surface albedo, emmissivity, land surface temperature (LST), NDVI, LAI, Vegetative Fraction, momentum roughness length, canopy height, and elevation represented by SRTM-1 arc sec (DEM). The Daily ET computed through SEBAL was later validated by DSSAT computed ET. The results revealed the mean bias error (MBE) of 0.62 mm/day for SEBAL, and R2 of 0.702, represents a higher similarity between the remotely sensed and model estimated evapotranspiration values.ThesisItem Open Access Studying the impact of modified microclimate on growth & development of chickpea (Cicer arietinum L.) in tarai region of Uttarakhand(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2020-09) Satpathi, Anurag; Nain, A.S.Chickpea, an important rabi legume crop of India mostly cultivated in Madhya Pradesh region of the country. A field experiment was conducted in rabi season of 2018-19 at N.E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar with three dates of sowing and four modified microclimatic conditions (Degree of perforation on covered plastic material). The experiment was laid out in strip plot design. The experiment with three dates of sowing: D1-12 December 2018, D2-22 December 2018 and D3-02 January 2019 as main plot treatments and the four microclimatic regimes: T1-Open field, T2-Open roof, T3-Perforated roof and T4-Closed or packed typeas sub plot treatments was laid to analyse the impact of microclimatic modification on growth and development of chickpea. The experimental data-set of the field was also used to calibrate and validate the CROPGRO (DSSAT v4.7) model for chickpea (Cicer arietinum L.) for variety Pusa-362 under Tarai region. The major finding of the study is that sowing on 12 Dec (D1) resulted into highest biological as well as seed yield i.e. 1200 kg ha-1followed by 22 Dec (D2)i.e. 845 kg ha-1and 02 Jan (D3)i.e. 638 kg ha-1. This may be mainly attributed to congenial weather conditions i.e. Tmin ranged from 2.7 to 17.8°C while Tmax ranged from 22.2 to 35.7°C as compared to the weather that prevailed during the sowing on 22 Dec2018 and 02 Jan 2019.By studying the role of weather variables on chickpea in terms of seed yield, it is noticed that best performance of Packed subset (T4)i.e. 1044 kg ha-1was observed in all dates of sowing followed by Perforated (T3)i.e. 955 kg ha-1, Open roof (T2)i.e. 813 kg ha-1 and Open field (T1)i.e.765 kg ha-1. However, crops under packed and perforated conditions experienced high temperature stress during flowering and maturity stage but crop experienced congenial temperature range during germination and vegetative stage, which leads to higher dry matter production and ultimately higher seed yield. Tmin ranged from 8.7 to 23.5°C and 5.9 to 21.5°C for packed (T4) and perforated (T3) conditions respectively. Similarly, Tmax ranged from 25.5 to 40.4°C and 23.4 to 38.9°C for packed (T4) and perforated (T3), respectively. The overall performance of the model based on the test criterion to evaluate the CROPGROChickpea model for phenology and yield attributes of three dates of sowing 12 Dec (D1), 22 Dec (D2) and 02 Jan (D3) and four microclimatic conditions open field (T1), open roof (T2), perforated (T3) and closed (T4) type clearly indicated that simulation for seed yield was better with reasonable error (Seed yield RMSE 5.93%).The decrease in seed yield with delayed sowing as observed in experiment was well simulated by the model. Under different microclimatic conditions, for the packed condition (T4), the model simulated higher simulated seed yield as compared to observed yield of experiment. The model output showed that the simulated values of phenology, growth parameters and yield of chickpea were quite close to the corresponding observed values. Hence, the model can be used to predict the yield accurately at different scales.ThesisItem Open Access Evapotranspiration studies on chickpea (Cicer arietinum L.) and moong (Vigna radiata L. Wilczek) in a mollisol of tarai region of Uttaranchal(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2006-05) Ahmed, Gulrez Jahangeer; Suman KumarThis study was conducted at the Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar for quantifying evapotranspiration losses of chickpea and moong under Uttaranchal tarai conditions and to select some suitable mathematical methods based on meteorological parameters for estimating chickpea and moong ET of this type of agroclimatic conditions. Evapotranspiration of chickpea and moong were measured with weighing type lysimeters. Data on pan evaporation measured with USWB class A pan evaporimeter and various chickpea parameters for the corresponding period were collected from Meteorological Observatory. Evapotranspiration of chickpea and moong was also estimated by using mathematical methods of Thornthwaite, Turc, Stephens-Stewart, Jensen-Haise, Blaney-Criddle and Modified Penman. The relationship of measured ET with pan evaporation and ET estimated by different mathematical methods was studied by linear regression and simple correlation. It can be concluded that the evapotranspiration of chickpea and moong under tarai conditions is about 342.1 mm and 340.8 mm respectively. The average total rainfall during chickpea and moong season is 119.4 mm and 1028.4 mm respectively. Thus supplementary irrigation is required during chickpea season due to low rainfall but not for moong season due to sufficient rainfall. As the Pan evaporation did not give accurate estimate of chickpea and moong ET, both on seasonal and as well as weekly basis. So Pan evaporation does not seem to be good criteria for ET estimation in chickpea and moong in this region and Modified Penman and Jensen-Haise methods are very suitable for estimation of ET in this tarai region of Uttaranchal.ThesisItem Open Access Application of ceres-rice model embedded in DSSAT 4.7 for district level rice yield forecast(G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2019-08) Chauhan, Pritam Singh; Ravi KiranThe present study was conducted at the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture and Technology, Pantnagar during the kharif season 2018. For district level rice yield forecast and Impact of climate change on rice yield under RCP 4.5 and RCP 6.0 by using CERES-Rice model in Tarai region of Uttarakhand. The experiment was laid out with two prominent cultivars (Pant Basmati-1 and HKR-47), three transplanting dates (29th June, 09th July and 19th July) and two levels of irrigation (100mm and 75mm) to calibrate the CERES-Rice model so that model could be used for district level rice yield forecast and to study the impact of climate change on rice yield under RCP 4.5 and RCP 6.0. Experimental analysis suggested that Pant Basmati-1 and HKR-47 both varieties performed better when transplanted on 29th June as compared to 09th July and 19th July. The performance of the model CERES-Rice was satisfactory for all transplanting dates, both irrigation levels and both varieties during the period of study for almost all crop characters. %RMSE for observed and simulated data for Panicle initiation, Anthesis, Physiological maturity and grain yield were found 4.09, 5.54, 3.99 and 4.92, respectively for Pant Basmati-1. While in case of HKR 47 %RMSE for Panicle initiation, Anthesis, Physiological maturity and Grain yield were found 5.4, 2.67, 3.94 and 5.48, respectively. The sensitivity analysis of crop simulation model suggests that the grain yield decreased with increasing temperatures by 1, 2, 3°C, increased with increasing CO2concentration by 25, 50, 75, 100 ppm, increased with increasing in Solar Radiation by 1, 2, 3 MJ/m2/d, increased with increasing Nitrogen by 25, 50, 75% and vice versa across all transplanting dates. The model was found to be highly sensitive to the change in temperature and Nitrogen. The district level rice yield prediction for a period of 11 years (2006 to 2016) shows quite good agreement between observed rice yield and predicted yield with %RMSE 5.24 %. Similarly district level rice yields were also forecasted for two years (2017 to 2018) by adopting same approach. The simulation result shows that increase in daily average temperature can slow down rice phonological development in Udham Singh Nagar under both RCPs. The yield of both varieties (Pant Basmati 1 and HKR 47) would decrease in the future and decreases were hiegher under RCP 6.0 then RCP 4.5. (Ravi Kiran) (
