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ThesisItem Open Access Soil Moisture Sensor System Development and evaluation for irrigation scheduling in subsurface drip irrigated Sugarcane(Punjab Agricultural University, Ludhiana, 2022) Raheja, Amina; Garg, SunilA study was conducted in Punjab Agricultural University on Soil Moisture Sensor System Development and evaluation for irrigation scheduling in subsurface drip irrigated Sugarcane. The sugarcane crop was sown for two consecutive years as plant (2019-20) and ratoon crop(2020-21). The irrigation to sugarcane crop was given by sub surface drip irrigation installed at three different depths-20 cm, 25 cm, and 30 cm depth. Water application was based on evapotranspiration of crop (ETc) for first year and sensor-based irrigation for ratoon crop. The low-cost capacitive sensor system with four moisture sensing probes was developed, calibrated, and tested both in laboratory and field to measure the sensor-to-sensor variability due to the placement of sensors in soil at different depths. The exponential calibration curve and two-point model was compared for developed low-cost sensor system and found to be accurate and precise. (Mean Absolute Error, Root Mean Square Error, and Relative Absolute Error of 1.56%, 0.36, and 0.65 respectively). The field calibrated soil sensing system was compared with a commercial SM150T sensor for measuring VMC in a sugarcane field. The sensor position in root zone plays a significant role in irrigation scheduling. Therefore, HYDUS 2D model was used for calibration, validation, and simulation of moisture movement in the root zone. It was found that the sensor could be installed within 10 cm periphery of the emitter irrespective of the drip depth. The developed sensor system was installed in the sugarcane for irrigation scheduling. The water requirement of sensorbased irrigation was at par with the irrigation based on ETc. The sugarcane yield was 8% and 10% more in drip depth of 25 and 30 cm respectively as compared to 20 cm drip depth. Water deficit up to 10% produced sugarcane yield like ET based irrigation for plant crop. However, water deficit beyond 10% significantly reduced the sugarcane yield both in plant and ratoon crop. Hence, the low-cost capacitive soil moisture sensor system consistently worked very well for the entire period of field testing with no practical issues, so can be used in atomization of the irrigation system.ThesisItem Restricted Sensor Based Crop Water Stress Index (Cwsi) for Irrigation Scheduling in Subsurface Drip Irrigated MaizeWheat Cropping Sequence(Punjab Agricultural University, 2022) Susanta Das; Samanpreet KaurA study was conducted at Punjab Agricultural University, Ludhiana to develop a canopy temperature-based sensor-system to estimate the crop water stress index (CWSI) for irrigation scheduling in subsurface drip irrigated maize-wheat cropping sequence. Rabi wheat and kharif maize crop was sown for two consecutive years as (2019-20) and (2020-21). The crops were irrigated with different irrigation levels (60, 70, 80, 90 and 100% ETc) and varying irrigation interval (1-day, 2-day, and 3-day) through sub surface drip, installed at 20 cm depth as well as surface drip (100% ETc). Based on the field observations of rabi wheat (2019-20) and kharif maize (2020), the upper and lower baselines of canopy-air temperature were established for computation of Crop Water Stress Index (CWSI). The threshold value of CWSI was determined from the relationship between CWSI, grain yield, water use efficiency. For the entire growing period, a threshold value of CWSI was found to be 0.254, and 0.365 for wheat and maize respectively. Also, growth stagewise CWSI values have been estimated for maize-wheat. An IoT based sensor system was developed and programmed with Arduino for collection of temperature- humidity and cloud-based estimation of CWSI. The developed sensor system was deployed in the field during rabi wheat (2020-21) and kharif maize (2021) and based on the CWSI irrigation has been applied. Growth stage 3 was found, the most crucial stage for both wheat and maize from crop water stress point of view. Irrigation at 90 % of ETc with 3-day irrigation interval found to be the best and was recommended for SDI in maize-wheat cropping system. The irrigation scheduling by IoT based sensor system was found to be beneficial, as it lowers the crop water stress and enhance the yield and water use efficiency. Irrigation water saving by 15.43% and 19.87 % and yield enhancement was observed in 5.2 % and 6.4 % in wheat and maize, respectively as compared to 100 % ETc based sub surface drip irrigation. This developed sensor system has a great scope for adoption and can be calibrated for threshold CWSI for other crops.ThesisItem Open Access Optimizing Lateral Depth and Spacing for Subsurface Drip Irrigated Rice-Wheat Cropping System under Conservation Agriculture Through Simulation of Water and Nitrate Movement(Punjab Agricultural University, Ludhiana, 2021) Bajpai, Arpna; Kaushal, ArunThe research study was conducted at Borlaug Institute for South Asia, Ludhiana and Punjab Agricultural University Ludhiana, Punjab to optimize lateral depth and spacing for subsurface drip irrigated rice-wheat cropping system under conservation agriculture through simulation of water and nitrate movement during 2017-20. Experiments were laid out in randomized block design with eight treatments, (six drip irrigated (T1-T6), which were combination of lateral spacing X emitter spacing X dripline buried depth) and two control treatments (T7-T8) with three replication i.e. T1 (67.5 X 30 X 0 cm), T2 (45 X 40 X 0 cm), T3 (67.5 X 30 X 15 cm), T4 (45 X 40 X 15 cm), T5 (67.5 X 30 X 20 cm), T6 (45 X 40 X 20 cm), T7 (conventional flood irrigation method) and T8 (flood irrigation method with conservation agriculture). Grain yield and water productivity were maximum in treatment T4 (Wheat, 6.513 t/ha, 3.832 kg/m3 and Rice, 8.178 t/ha, 1.203 kg/m3 ) and minimum in T7 (Wheat, 5.620 t/ha, 1.406 kg/m3 and Rice, 7.410 t/ha, 0.492 kg/m3 ). Statistically T4 treatment was significantly at par with T6 treatment and higher than all other treatments for both rice and wheat crops. Most uniform soil moisture and nitrate movement were observed under T4. HYDRUS-2D model showed successful performance with Nash-sutcliffe model efficiency coefficient of 0.885 and coefficient of determination as 0.906. Economic analysis showed that treatment T4 (B:C-3.286) was economically viable and significantly higher than treatment T7 (B:C-3.157) only with 95% of drip irrigation subsidy.ThesisItem Restricted Developing Artificial Intelligence Algorithm for Identification and Classification of Abiotic Stresses in Maize(Punjab Agricultural University, 2023) Goyal, Pooja; Sharda, RakeshCereal crops like maize are seriously affected by the abiotic plant stresses, which cause substantial reduction in crop yields. New age Artificial Intelligence (AI) and Deep Learning (DL) techniques offer a non-invasive and a practical way for precise detection of these stresses in real time. The present study was planned to develop an AI-based algorithm for identification and classification of abiotic stresses in maize. Three consecutive experimental trials i.e. spring maize (2021), kharif maize (2021) and spring maize (2022) were conducted at the Research Farm of Department of Soil and Water Engineering, PAU, Ludhiana in a split-plot design with varieties in main plot and fertigation treatments in sub-plot. The subplots covered three types of nutritional deficiencies of nitrogen (N), phosphorus (P) and potassium (K), one water deficit plot and one plot with 100% irrigation and fertigation. Data regarding the plant growth and yield attributes were observed at different growth stages and a total of 6035 RGB images were captured throughout the crop growing seasons. These images were classified into five classes according to stress levels to build a DL-based Convolutional Neural Network (CNN) model. Two classification models were built, the first was a binary classification model for drought stress detection, and the second was a multi-class classification model to classify all the abiotic stresses. Further, 2 modelling approaches i.e. custom-CNN model and a transfer learning (TL) approach using 5 state-of-the-art architectures were used. Both ResNet50 and EfficientNet121 models performed the best, achieving a test accuracy and F1-score of 99.26% and 99.22% respectively in case of binary classification model. The ResNet50 model also achieved the highest average weighted accuracy and F1-score of 98.36% and 98.35% respectively for overall abiotic stress detection in maize. The custom-CNN model, with much lesser number of parameters compared to the transfer-learned networks, achieved an acceptable F1-score of 98.44% and 97.04% for drought and abiotic stress classification respectively. Therefore, the custom-CNN model was recommended for real-time abiotic stress assessment in maize crop especially on resource constrained devices.ThesisItem Open Access Techno-Socio and Economic Study of Solar Operated Community Based Micro Irrigation Project at Talwara, Hoshiarpur(Punjab Agricultural University, Ludhiana, 2022) Gladwin Cutting, Nikhil; Siag, MukeshThis study was carried out in Talwara block of Hoshiarpur district, Punjab, where the Department of Soil and Water Conservation, Punjab has established a lift irrigation project integrated with micro-irrigation systems and operated with solar power. The project covers 664 hectares of 14 villages and benefits around 1200 farmers. Software in Python language was developed for equitable distribution of irrigation water. The irrigation cycle taken was six days for sprinkler systems and every second day for drip irrigation. A provision for buffer time has been made for rescheduling the irrigation in case of disruption due to cloudy conditions or any technical breakdown. An android application was developed to facilitate the two-way communication between the farmers and the management covering aspects like irrigation and maintenance request, canal water status, network, and water user associations details, etc. An economic analysis was also done, three hundred farmers were randomly selected and their socio-economic status was recorded for pre-project (2016-17) and post-project (2019-20) years. The farmers were categorized as marginal, small, and semi-medium based on landholdings. Compared to pre-project status, the net incomes of the farmers increased by 114.52%, 108.03%, and 79.00% for marginal, small, and semi-medium farmers respectively. The food and non-food expenditures also increased post-project and the increase in expenditures was 83.91%,74.91, and 63.64% for marginal, small, and semi-medium farmers respectively. The net assets increased by 32.60%, 58.10%, and 27.81% for marginal, small, and semi-medium farmers respectively. The increase in net incomes, total assets, and the expenditure pattern indicates a clear positive impact of the project on the economic status of the beneficiary farmers.ThesisItem Restricted Simulation of Nitrate-N movement through subsurface drainage system under cropping conditions(Punjab Agricultural University, Ludhiana, 2021) Dar, Mehraj U Din; Singh, J. P.The study was conducted to simulate the NO3-N movement through subsurface drainage in a field under cropping conditions using DRAINMOD-NII model and for predicting optimum depth and spacing for minimum nitrate leaching. The nitrogen balance was estimated using DNDC model for the area installed with the subsurface drainage system at Thehri village Muktsar district of Punjab. Both the models of DRAINMOD NII (v. 6.1) and DNDC (v.9.5) (Denitrification and Decomposition model) were calibrated and validated for the 2 years study period from 2018-2020. The reliability of the models was evaluated by comparing observed and simulated values of drain flow, nitrogen loss, water table depths and relative yields for the rice-wheat cropping system. The validated DRAINMOD-NII model was used to predict the optimal depth and spacing for minimum nitrate losses. The nitrogen balance for the area under subsurface drainage system was estimated using DNDC model for rice-wheat cropping system. The statistical performance of DRAINMOD-NII model was carried out and the range of statistical parameters viz, root mean square error (RMSE), Nash Sutcliffe modeling efficiency (NSE) and correlation coefficient (R2) were obtained to be 0.12 to 9.66, 0.47 to 0.88 and 0.53 to 1 respectively during the calibration and validation periods. The values of statistical parameters for DNDC model viz; RMSE, NSE and R2 ranged from 6.8 to 8.5, 0.78 to 0.88 and 0.84 to 0.90 respectively during the calibration and validation periods. The optimal depth and spacing of subsurface drainage system for the study area was obtained to be 1.3 m and 42 m respectively using DRAINMOD-NII model for minimum nitrate loss. The overall nitrogen balance was found to be -99.44 kg N ha-1 yr-1(negative) and 69.1 kg N ha-1 yr-1 (positive) for rice and wheat crops respectively using DNDC model. DRAINMODNII model and DNDC model performed a better simulation, and could be used for efficient nitrogen management in the region.ThesisItem Restricted Development of groundwater indices for water resource management in Central Punjab(Punjab Agricultural University, Ludhiana, 2022) Singla, Chetan; Aggarwal, RajanCentral Punjab is worst affected zone of Punjab in terms of groundwater exploitation as all the blocks are over-exploited in this zone. This study was carried out in Central Punjab to analyze the factors affecting groundwater depletion during the study period 1998-2019 and to find suitable management strategies for sustainability. The trends analysis was quantified by non-parametric Sen’s slope and their significances were assessed by Mann-Kendall test at 5% level of significance. The abrupt change in different parameters was analyzed using Pettitt’s test. Rainfall and minimum temperatures did not show any trend in Central Punjab. The maximum temperature showed significantly increasing trend in Fatehgarh Sahib, Kapurthala and Patiala. Potential evapotranspiration (PET) showed significantly increasing trend. Groundwater level were found to be decreasing all over Central Punjab, varying from 0.3– 1.07 m annually. Sangrur was found to be worst affected in terms of exploitation in Central Punjab. Artificial neural network (ANN) and multiple linear regression (MLR) were used as modeling tool to develop groundwater model. The composite groundwater sustainability index (GSI) was developed using analytical hierarchy process (AHP). Pettitt’s test revealed that paddy area and tubewell density showed change point in 2008. ANN performed better as compared with the MLR statistically in Central Punjab. Potential management strategies identified and evaluated were shifting area under paddy to non-paddy crops, shifting area under long duration variety to area under short duration variety of paddy and increasing canal irrigated area for improving GSI. Presently, GSI was found to be in poorly sustainable (level III). It can be upgraded to level II i.e. moderately sustainable by adopting 4% paddy diversification for 2 years annually in conjunction with adoption of 5% area under short duration variety of paddy each year.ThesisItem Restricted Development of Nutrient Uptake Model of Hydroponically Grown Strawberry (Fragaria x ananassa) in Greenhouse(Punjab Agricultural University, Ludhiana, 2022) Sharma, Pankaj; Sharda, RakeshIn present study nutrient uptake models developed for predicting the nutrient uptake of hydroponically grown strawberry (Fragaria x ananassa) in greenhouse. The strawberry crop (cv. Camarosa) was grown for two consecutive years (2017-18 and 2018-19) using the nutrient film technique. The nutrient solution was supplied at three levels of Hoagland solution (T1=120%, T2=100% and T3=80%) and replicated thrice. Agronomical parameters (number of leaves, leaf area index and plant spread), fruit yield (g/plant) and fruit quality parameters were measured during the study. The plant supplied with 100% nutrient solution was found most effective to enhance the plant growth parameters viz. number of leaves, leaf area index, plant spread and also achieved higher yield (374.9 g/plant) followed by 120% and 80% of nutrient solution. The different nutrient concentration significantly influenced number of fruits/plant. Fruits quality parameters were also improved with different nutrient solution concentrations. TSS content of fruit for two successive years was maximum in T1 (9.68 and 9.62 Brixﹾ) followed by T2 (9.49 and 9.37 Brixﹾ) and T3 (8.63 and 8.56 Brixﹾ). The average plant water uptake was observed maximum in T2 (0.084 l/day) followed by T1 (0.081 l/day) and T3 (0.079 l/day). The nutrient uptake was assessed by following changes in the ion concentration of the recirculating nutrient solution. Empirical models were developed for nutrient uptake (nitrate, phosphorus, potassium, calcium and magnesium) using linear regression. The average uptake of nitrate, phosphorus, potassium, calcium and magnesium was 129.92, 15, 122.94, 112.02 and 26.97 mg/l respectively. The predicted values derived from the linear regressions were plotted over the observed values showing the strong relationship. The developed models would be helpful in managing the fertigation strategy on the basis of crop growth period. The benefit cost ratio (BCR) with and without subsidy were computed to be 1.69 and 1.39 respectively without considering runner production from the plants. The break-even point for greenhouse and NFT system was achieved after 5.8 years without any subsidy and with 50% subsidy, it was 3.27 years.ThesisItem Restricted Development of customized automated fertigation system for soilless media in protected cultivation(Punjab Agricultural University, Ludhiana, 2021) Pandey, Kusum; Singh, K. G.A study was undertaken for the development of a low-cost customized automated fertigation system for the closed soilless system under protected conditions and tested for cucumber crop grown in different soilless media under naturally ventilated polyhouse at Dr. S. D. Khepar Laboratories of the Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana. This system consisted of mainly four units i.e. controller unit, sensor unit, fertigation unit, and pipeline unit. The controller unit comprises the programmable logic controller (PLC) which is used to automate the whole fertigation process i.e. to prepare and supply the fresh nutrient solution to the crop and reuse the drained nutrient solution after making the balance solution through the filtration from the polyhouse. The developed system was calibrated for dozing pumps, EC, pH sensors precision analysis, and time taken by the dozing pump with their respective treatments. The experiment was set in a factorial split-plot design included 9 treatments each under the developed automated and conventional fertigation systems with three fertigation levels viz. T1 = 100% fresh solution, T2 = 90% fresh solution with 10% leachate and T3 = 80% fresh solution with 20% leachate. Cucumber hybrid Multistar were planted under three different substrates of cocopeat i.e. S1 = 50% cocopeat with 50% cocochips, S2 = 70% cocopeat with 30% cocochips and S3 = 100% cocopeat in three replicates during three growing seasons. The performance of the system was measured by crop growth parameters (vine length, leaf area index, stem diameter, leaf chlorophyll content), yield parameters (fruit weight, fruit length, fruit diameter, yield per plant), and quality parameters (vitamin C, total phenol content, antioxidant capacity, total chlorophyll content, firmness). The economic analysis was carried out for a developed automated system with 50% subsidy on polyhouse and without subsidy and compared with conventional fertigation system. Customized automated fertigation system recorded significantly higher 17.1%, 10.8%, and 12.1% fruit yield per plant as compared to the conventional fertigation system in seasons 1, 2, and 3 respectively. Out of three different growing media, all the plant growth, yield, and quality parameters were recorded significantly higher under substrate S2 followed by S3 and S1. Among the fertigation treatments, T1 recorded significantly higher all the plant growth parameters as compared to T2 and T3 while there was no significant difference found in the fruit yield and quality parameters. For a customized automated fertigated system, BCR was found 2.16 & 2.19 (with subsidy) and 1.73 & 1.77 (without subsidy) for years 1 and 2. While, for conventional fertigation system, BCR was 1.89 & 2.0 (with subsidy) and 1.45 &1.59 (without subsidy) for years 1 and 2 respectively. Overall, the developed customized automated fertigation system crop grown under substrate S2 and with fertigation treatment T3 is recommended for achieving a statistically similar yield.
