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20214
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Subject: Agricultural Meteorology × End date: 2021 × Start date: 2021 ×
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    ThesisItemOpen 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).
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    ThesisItemOpen 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.
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    ThesisItemOpen 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.
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    ThesisItemOpen 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 Ranjan
    The 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.