Faculty Publications
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Item Performance evaluation of reference evapotranspiration equations across a range of Indian climates(2006) Nandagiri, L.; Kovoor, G.M.Reference crop evapotranspiration (ET0) is a key variable in procedures established for estimation of evapotranspiration rates of agricultural crops. In recent years, there is growing evidence to show that the more physically based FAO-56 Penman-Monteith (PM) combination method yields consistently more accurate ET0 estimates across a wide range of climates and is being proposed as the sole method for ET0 computations. However, other methods continue to remain popular among Indian practitioners either because of traditional usage or because of their simpler input data requirements. In this study, we evaluated the performances of several ET0 methods in the major climate regimes of India with a view to quantify differences in ET0 estimates as influenced by climatic conditions and also to identify methods that yield results closest to the FAO-56 PM method. Performances of seven ET0 methods, representing temperature-based, radiation-based, pan evaporation-based, and combination-type equations, were compared with the FAO-56 PM method using historical climate data from four stations located one each in arid (Jodhpur), semiarid (Hyderabad), subhumid (Bangalore), and humid (Pattambi) climates of India. For each location, ET0 estimates by all the methods for assumed hypothetical grass reference crop were statistically compared using daily climate records extending over periods of 3-4 years. Comparisons were performed for daily and monthly computational time steps. Overall results while providing information on variations in FAO-56 PM ET0 values across climates also indicated climate-specific differences in ET0 estimates obtained by the various methods. Among the ET0 methods evaluated, the FAO-56 Hargreaves (temperature-based) method yielded ET0 estimates closest to the FAO-56 PM method both for daily and monthly time steps, in all climates except the humid one where the Turc (radiation-based) was best. Considering daily comparisons, the associated minimum standard errors of estimate (SEE) were 1.35, 0.78, 0.67, and 0.31 mm/day, for the arid, semiarid, subhumid, and humid locations, respectively. For monthly comparisons, minimum SEE values were smaller at 0.95, 0.59, 0.38, and 0.20 mm/day for arid, semiarid, subhumid, and humid locations, respectively. These results indicate that the choice of an alternative simpler equation in a particular climate on the basis of SEE is dictated by the time step adopted and also it appears that the simpler equations yield much smaller errors when monthly computations are made. In order to provide simple ET0 estimation tools for practitioners, linear regression equations for preferred FAO-56 PM ET0 estimates in terms of ET0 estimates by the simpler methods were developed and validated for each climate. A novel attempt was made to investigate the reasons for the climate-dependent success of the simpler alternative ET0 equations using multivariate factor analysis techniques. For each climate, datasets comprising FAO-56 PM ET0 estimates and the climatic variables were subject to factor analysis and the resulting rotated factor loadings were used to interpret the relative importance of climatic variables in explaining the observed variabilities in ET0 estimates. Results of factor analysis more or less conformed the results of the statistical comparisons and provided a statistical justification for the ranking of alternative methods based on performance indices. Factor analysis also indicated that windspeed appears to be an important variable in the arid climate, whereas sunshine hours appear to be more dominant in subhumid and humid climates. Temperature related variables appear to be the most crucial inputs required to obtain ET0 estimates comparable to those from the FAO-56 PM method across all the climates considered. © 2006 ASCE.Item Effect of hydraulic conductivity on soil moisture uptake under saline conditions for wheat crop(2013) Devatha, C.P.; Ojha, C.S.P.; Hari Prasad, K.S.; Thalla, A.K.Salinity in soil can decrease plant available water and cause plant stress. The pattern of root water uptake for wheat was studied for saline as well as non-saline condition using non-linear root water uptake model. Experiments have been conducted using a salinity level of 4 dS/m and freshwater condition. The effect of salinity on soil moisture has been studied by varying the crop coefficient as well as hydraulic conductivity. The correction factor to the crop coefficient approach is found to be unsuccessful. However, the correction factor to the hydraulic conductivity for a non-saline condition improves the simulation of soil moisture uptake in case of saline soils. The exponential form of the equation is established for the hydraulic conductivity to soil moisture relationship under salinity level and freshwater sample. The present work also substantiates that the non-linearity parameter of root water uptake model (O-R model) is successful in simulation of soil moisture depletion in the crop root zone and does not vary more than 10% in case of saline soils. © 2013 Indian Society for Hydraulics.Item An extreme learning machine approach for modeling evapotranspiration using extrinsic inputs(Elsevier B.V., 2016) Patil, A.P.; Deka, P.C.Precise estimation of evapotranspiration is crucial for accurate crop-water estimation. Recently machine learning (ML) techniques like artificial neural network (ANN) are being widely used for modeling the process of evapotranspiration. However, ANN faces issues like trapping in local minima, slow learning and tuning of meta-parameters. In this study an improved extreme learning machine (ELM) algorithm was used to estimate weekly reference crop evapotranspiration (ETo). The study was carried out for Jodhpur and Pali meteorological weather stations located in the Thar Desert, India. The study evaluated the performance of three different input combinations. The first input combination used locally available maximum and minimum air temperature data while the second and third combination used ETo values from another station (extrinsic inputs) along with the locally available temperature data as inputs. The performance of ELM models was compared with the empirical Hargreaves equation, ANN and least-square support vector machine (LS-SVM) models. Root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE) and threshold statistics (TS) were used for comparing the performance of the models. The performance of ELM model was found to be better than the Hargreaves and ANN model. The LS-SVM and ELM displayed similar performance. ELM3 models, with 36 and 33 neurons in hidden layer were found to be the best models (RMSE of 0.43 for Jodhpur and 0.33 for Pali station) for estimating weekly ETo at Jodhpur and Pali stations respectively. The results showed that ELM is a simple yet efficient algorithm which exhibited good performance; hence, can be recommended for estimating weekly ETo. Furthermore, it was also found that use of ETo values from another station can help in improving the efficiency of ML models in limited data scenario. © 2016 Elsevier B.V.Item Assessment of soil moisture uptake under different salinity levels for paddy crop(American Society of Civil Engineers (ASCE) onlinejls@asce.org, 2016) Devatha, C.P.; Shankar, V.; Ojha, C.S.P.The core of salinity problems starts from the fact that irrigation waters contain some amount of dissolved salts. Soil moisture salinity is dependent on soil type, climate, water use, and irrigation. The root water-uptake pattern for paddies is studied for saline as well as nonsaline conditions in the present study using a nonlinear root water uptake model. Field crop experiments are carried out using irrigation water with two different levels of salinity (4 and 6.25 dS=m) and fresh water. The effect of salinity on soil moisture uptake is studied by two approaches, i.e., effect on crop coefficient and effect on hydraulic conductivity. Based upon the experimental observations for lowsaline (4 dS=m), high-saline (6.25 dS=m), and freshwater conditions, an exponential form of an equation is established for the hydraulic conductivity. The results obtained for soil moisture depletion in the crop root zone show significant improvement in prediction of soil moisture uptake for saline cases with the use of the obtained nonlinearity parameter. © 2016 American Society of Civil Engineers.Item Effect of local calibration on the performance of the hargreaves reference crop evapotranspiration equation(IWA Publishing, 2021) Niranjan, S.; Nandagiri, L.Obtaining accurate estimates of reference crop evapotranspiration (ET0) using limited climatic inputs is essential in data-short situations where the preferred FAO-56 Penman–Monteith (PM) equation cannot be implemented. Among several available for ET0 estimation, the empirical temperature-based Hargreaves–Samani (HG) equation remains a popular alternative. However, accurate HG estimates can be obtained by local calibration and replacing the mean daily temperature with the effective daily temperature. Therefore, the present study was taken up to evaluate the effects of site-specific calibration of model parameters and the use of effective air temperature on the accuracy of ET0 estimates by the HG model. For this purpose, climate records for the historical period 2006–2016 of 67 stations located across 10 agro-climatic zones of Karnataka State, India, were used and the analysis was carried out using a monthly time step. Calibration and statistical performance evaluation was performed using FAO-56 PM ET0 estimates as a reference. Overall results showed significant improvement in HG estimates across all zones with the use of locally calibrated parameters, whereas the use of effective air temperature did not lead to any significant gain in prediction accuracies. The derived information on the spatial distribution of calibrated parameters will help obtain accurate ET0 estimates with only air temperature inputs. © 2021 The Authors.Item Cardamom Plant Disease Detection Approach Using EfficientNetV2(Institute of Electrical and Electronics Engineers Inc., 2022) Sunil, C.K.; Jaidhar, C.D.; Patil, N.Cardamom is a queen of spices. It is indigenously grown in the evergreen forests of Karnataka, Kerala, Tamil Nadu, and the northeastern states of India. India is the third largest producer of cardamom. Plant diseases cause a catastrophic influence on food production safety; they reduce the eminence and quantum of agricultural products. Plant diseases may cause significantly high loss or no harvest in dreadful cases. Various diseases and pests affect the growth of cardamom plants at different stages and crop yields. This study concentrated on two diseases of cardamom plants, Colletotrichum Blight and Phyllosticta Leaf Spot of cardamom and three diseases of grape, Black Rot, ESCA, and Isariopsis Leaf Spot. Various methods have been proposed for plant disease detection, and deep learning has become the preferred method because of its spectacular accomplishment. In this study, U2-Net was used to remove the unwanted background of an input image by selecting multiscale features. This work proposes a cardamom plant disease detection approach using the EfficientNetV2 model. A comprehensive set of experiments was carried out to ascertain the performance of the proposed approach and compare it with other models such as EfficientNet and Convolutional Neural Network (CNN). The experimental results showed that the proposed approach achieved a detection accuracy of 98.26%. © 2013 IEEE.Item Role of coconut shell biochar and earthworm (Eudrilus euginea) in bioremediation and palak spinach (Spinacia oleracea L.) growth in cadmium-contaminated soil(Academic Press, 2022) Noronha, F.R.; Manikandan, S.K.; Nair, N.The contamination of soil with heavy metals is known to affect the yield the soil fertility, which in turn affects the growth of agricultural crops. This study investigates the role of coconut shell biochar (CSB) and earthworms (Eudrilus euginea) in the bioremediation and growth of Palak spinach (Spinacia oleracea L.) in cadmium (Cd) contaminated soil. The soils were amended with different combinations of CSB and earthworms and incubated for 35 days. Later, the soil samples were analyzed for the changes in the soil properties, soil enzyme activity, and heavy metal contents. It is observed that the treatments with both CSB and earthworms resulted in the improvement of soil properties and soil enzyme activity which was directly related to soil fertility. Meanwhile, the maximum removal of 94.38% of total Cd content in the soil was obtained for the soil sample contain both CSB and earthworms. The improved soil properties resulted in a higher germination percentage of Spinacia oleracea L. seeds in the Cd contaminated soil. © 2021 Elsevier LtdItem Multivariate analysis of concurrent droughts and their effects on Kharif crops—A copula-based approach(John Wiley and Sons Ltd, 2022) Muthuvel, D.; Mahesha, M.Apart from creating an ecological imbalance, drought events could affect an agrarian country's economy and food security by reducing crop yields. The antecedent meteorological droughts could prolong into hydrological and (or) agricultural droughts and may co-exist as concurrent droughts. The current study aims to comprehensively study Indian concurrent droughts, their effects on crop yield, and possible teleconnection with ENSO (El Niño–Southern Oscillation), adopting a copula-based multivariate approach. The copula functions can replicate the correlation among the variables and keep the dependence structure intact. The concurrent drought characteristics are computed using a multivariate standardized drought index that incorporates the three primary drought indices using the Gaussian copula. Some of the severe concurrent drought years such as 2002, 1987, 1972, and 1965 caused considerable yield losses in Kharif season crops of groundnut, millet, and rice. This prompts to construct quad-variate models involving the crop yield and the three drought indices using the vine copulas that perform better than the elliptical and symmetric Archimedean copula. Though the isolated forms of droughts could cause mild yield losses, the probability of concurrent droughts causing high to exceptional losses is more. Further, the ENSO teleconnection with the concurrent monsoon droughts is analysed and mapped. The above-normal warming of the Nino 3.4 region over the tropical Pacific during the months leading up to the monsoon could signal concurrent monsoon droughts in the areas under the Ganga-Brahmaputra basin at a probability of around 45%. These results could be helpful in drought mitigation measures and policymaking. © 2021 Royal Meteorological Society.Item An optimum datasets analysis for monitoring crops using remotely sensed Sentinel-1A SAR data(Taylor and Francis Ltd., 2023) Salma, S.; Keerthana, N.; Dodamani, B.M.To effectively monitor crops, it is necessary to select extremely redundant satellite images and to know the number of acquisitions required for a specific period to analyse cropping patterns, thereby reducing analysis time. In this paper, we have examined an empirical analysis for the optimum dataset (OptD) selection required to monitor the crops. Sentinel-1 dual-polarized SAR datasets were used in this study to illustrate the effectiveness of optimum datasets required for the considered crops (ginger, tobacco, rice, cabbage, and pumpkin). In this work, at first, the entropy and alpha bands were treated as cluster centres for crop decomposition and its scattering mechanism using the cluster-based K-means unsupervised classification technique. The clusters are plotted on the H-α plane to get the H-α plot of dual-polarization SAR data for target decomposition. To understand the dominance of scattering type with crop growth stage, the obtained scattering distribution from the H-alpha plot is scaled to a percentage analysis. Based on qualitative observations of the percent scattering distribution of crop pixels over a h-alpha plot and backscattering coefficient behaviour at different crop growth stages, an empirical approach has been used to select dataset reduction. It has been suggested that the combination of successive repeated data with similar scattering analysis of combined h-alpha plots and backscattering analysis is the best reduced dataset selection for effective crop monitoring. From the analysis, the optimum dataset required for monitoring Ginger (from the flourishing stage), Tobacco, Paddy, Cabbage, and Pumpkin has been identified, and found that the tobacco crop requires fewer datasets, whereas the rice crop requires a greater number of datasets for monitoring. Despite the challenges associated with, p-bias for the crops was achieved at good levels, given that, lowering the datasets to obtain the optimal number without significantly reducing the accuracy of the data. © 2023 Informa UK Limited, trading as Taylor & Francis Group.