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Abstract Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station’s data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+–HCO3 − types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3 − + CO3 2−) outperform strong acids (Cl− + SO4 2−). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p

Background: Pus-like aspirates (PLAs) are not uncommon in day-to-day cytology practice. An apparently looking simple pus may yield a diagnostic diversity ranging from simple suppurative inflammation to a carcinomatous lesion. Proper processing followed by patient examination may be lifesaving for a patient. Very few cytological studies on PLAs have been conducted in Eastern India. Materials and Methods: One hundred and four PLAs from 104 patients (53 males, 51 females) received during 6 months (January 2022–June 2022) were included in this study. Results: The adult age group (19–60 years) and involvement of the head-and-neck area were most common in this study. The sex ratio (male: female) was almost equal. Simple pyogenic abscess is the most common diagnosis, followed by granulomatous and tuberculous abscess. Some special diagnoses were revealed, such as fungal infection, lymphoma, and carcinoma. Conclusion: This study emphasizes the importance of careful examination of PLAs as it may unmask some special diagnoses, even malignancy too. As a simple and quick outdoor-based study, prompt diagnosis and timely intervention may be lifesaving for some patients.

The baseline approaches on satellite image segmentation problem suffer from issues like sensitivity towards initialization, local optima solutions, a predefined number of output clusters, single-objective optimization, etc. To solve these challenges, this study proposes a unique pixel-level Multi-Spectral (MS) very high resolution (VHR) image segmentation algorithm based on variable-length multi-objective genetic clustering. We propose a new approach to update solutions by retaining variable length property throughout the optimization process. The resulting clustering algorithm contains a set of near-Pareto-optimal solutions. A map that has a scale of less than 1/10000 is called a large-scale map. We propose a large-scale change detection technique as an application of the proposed image segmentation algorithm. Solving Land-use/Land-Cover (LULC) change detection problems in a congested area is a complex task. This study considers the dataset from Pleiades-HR 1B, and Landsat 5 TM sensors in the experimental study. An extensive quantitative and qualitative analysis is performed to validate the superior performance of the proposed method with different state-of-the-art techniques.

Sustaining crop yield under abiotic stresses with optimized resource use is a prerequisite for sustainable agriculture, especially in arid and semi-arid areas. Water and heat stress are major abiotic stresses impacting crop growth and yield by influencing complex physiological and biochemical processes during the life cycle of crops. In a 2-year (2015–2017) research, spring wheat cv. HD-2967 was grown under deficit irrigation and delayed sowing conditions to impose water and terminal heat stresses, respectively. The data were analyzed for seasonal crop water use, radiation interception, water productivity (WP), and radiation productivity (RP) under combined water deficit and terminal heat stresses. Seasonal crop water use was significantly affected by stresses in the order of water + terminal heat > water > terminal heat. Water stress showed minimal effect on the light extinction coefficient and consequently on seasonal intercepted photosynthetically active radiation (IPAR). However, seasonal IPAR was primarily affected by combined water + terminal heat and terminal heat stress alone. The slope of crop water use and IPAR, i.e., canopy conductance, an indicator of canopy stomatal conductance, was more influenced by water stress than by terminal heat stress. Results showed that linear proportionality between WP and RP is no longer valid under stress conditions, as it follows a curvilinear relation. This is further supported by the fact that independent productivity (either water or radiation) lacked the ability to explain variability in the final economic yield or biomass of wheat. However, the ratio of RP to WP explained the variability in wheat yield/biomass under individual or combined stresses. This suggests a clue for improving higher wheat yield under stress by managing WP and RP. The highest biomass or yield is realized when the ratio of RP to WP approaches unity. Screening of genotypes for traits leading to a higher ratio of RP to WP provides an opportunity for improving wheat productivity under stressed environments.

Weather forecasts are important for the planning of agricultural operations, especially during times of heightened climatic variability. This study analyzed and verified the medium-range weather forecast issued by the India Meteorological Department (IMD) for different weather parameters over four locations in the northeastern hill (NEH) region of India considering five years of daily datasets. Results revealed good overall accuracy of the forecast over the NEH region. The accuracy of relative humidity (>80%), rainfall (>79%), and wind speed (>70%) were good, and the accuracy of temperature was average, with the usability values for maximum temperatures (44.7–62.7%) comparatively better than for minimum temperatures (38.5–58.6%). The correlation coefficient between the observed and forecasted values was positive (0.24–0.70) and statistically significant for most of the cases, indicating that the forecast could capture variations. Field experiments for maize crops showed that a near-real-time weather forecast-based agro-advisory could manage the uncertainties related to the in-season weather and thereby help in its day-to-day management, which is depicted by the statistically significant (p < 0.05) improvements in the yield of maize. The accuracy of the minimum temperature was poor during winter and post-monsoon seasons, when it plays a crucial role in the determination of optimal growing conditions. Usability of the maximum temperature needs improvement during the pre-monsoon season, as crop cultivation over the region starts from this season due to the high probability of assured rainfall. Therefore, the forecasts were found to be useful but in need of improvement for minimum temperature, which is very crucial for the region.

Understanding the behavior of crops under interactive effect of different abiotic stresses is the need of hour. Present study aimed to examine the interactive effect of late planting and deficit irrigation on wheat (Triticum aestivum L.) growth and development in semi-arid environment. The field experimentation with two planting dates and three levels of irrigation was conducted during rabi 2015-16 and 2016-17. The results showed that response of water stress on wheat growth and development behaved in similar manner as of that in late planting. The reduction in LAI, biomass, yield and yield attributes was observed under independent effect of water stress as well as late planting with varied magnitude. Noticeably, late sowing had large influence on rate of LAI change in the order of 20% and 47% in greening and senescence phase, respectively than deficit irrigation. The slowdown of LAI change in greening phase is also associated with faster senescence in late phase of crop growth. However, the additive effect was observed under combined water deficit and late planting conditions. It may be concluded that the co-existence of limited water availability and late planting is most detrimental to wheat growth and development ultimately on crop yields in semiarid environment. Therefore, suitable agronomic and breeding strategies must be devised to mitigate the hampering effect of combined reduced water availability and late planting conditions.

Present experiment was conducted (2015-16 and 2016-17) comprising different levels of water stress and environmental conditions for the model parametrization and validation of Info Crop-wheat v2.1 model. The model was calibrated and validated for popular wheat variety HD-2967 under semi-arid environmental conditions. The important genetic coefficients were generated and model fine-tuned for simulating phenology, leaf area index (LAI), dry matter and yield of wheat crop. The result showed that the model simulated the phenology in terms of days to 50% anthesis and days to physiological maturity very accurately with root mean squared error of 2.26 and 1.36 days, respectively. The model could also able to simulate temporal course of LAI and biomass very satisfactorily. The errors in simulating final biomass and yield were also within the acceptable limit of 10%. We conclude that InfoCrop model is reasonably suitable to simulate the developmental stages, leaf area expansion, dry matter and yield of wheat crop in semi-arid environment hence it can be utilized for undertaking various crop management applications.

Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions. In this study, Hölder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture. A threshold is derived to segment the transformed image into textured and non-textured regions. Subsequently, the original image is extracted into textured and non-textured regions using this segmented image mask. Afterward, extracted textured region is classified using ISODATA classification algorithm considering HE, VAR, and intensity values of individual pixel of textured region. And extracted non-textured region of the image is classified using ISODATA classification algorithm. In case of non-textured region, HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes. Consequently, the classified outputs of non-textured and textured regions that are generated independently are merged together to get the final classified image. IKONOS 1 m PAN images are classified using the proposed algorithm, and the classification accuracy is more than 88%.

Evaluation of crop simulation model for growth and development of crops at farmers' eld is uncommon, as large variability exists in management practices at farmers' elds. Present study was attempted to evaluate the suitability of InfoCrop v2.1 model for prediction of growth, development and yield of wheat crop at farmers' elds using two years data i.e. 2015-16 & 2016-17. A total of 42 farmers' elds were selected in Pataudi block of Haryana state. The large variability in wheat sowing date (1-Nov to 25-Dec), seed rate (87 to 150 kgha-1), N application rate (70 to 195 kgha-1) and number of irrigations (5-8) were observed in farmers' eld. InfoCrop model could able to predict well days to anthesis and physiological maturity within an acceptable error of 5% (RMSE~3 days). Measured leaf area index (LAI) matched well with simulated LAI (RMSE ~ 0.5). The agreement between observed and model simulated wheat grain yield was found to be satisfactory (nRMSE ~ 6-8%). We conclude that InfoCrop-wheat model satisfactorily simulate the growth, development and yield of wheat crop under varied management practices at farmers' elds, and hence can be applied for agricultural applications for farmers

Green parks and open spaces are indispensable parts of an urban environment as they play important roles in promoting public health, protecting natural ecosystems and improving socioeconomic conditions of the city dwellers. Hence, the information of green and open spaces in a city needs to be provided to its governing authority and people for better management and utilisation. WebGIS technology is capable of maintaining and disseminating green park information among its users over the web. The availability of open source geospatial software and tools made this technology more cost effective and acceptable to the user community. In present study an interactive and user friendly WebGIS enabled solution is developed for green parks of Kolkata city that facilitates city dwellers and decision makers to search and visualise green parks both on map and satellite image along with park information and ground photo. It is an easy deployable solution that helps in urban planning for creating new as well as preserving the existing green spaces.

This study describes the retrieval of wheat biophysical variables of leaf chlorophyll (Cab), leaf area index (LAI), canopy chlorophyll (CCC), and leaf wetness (Cw) from broadband reflectance data corresponding to IRS LISS-3 (Linear Imaging Self Scanner) sensor by inversion of PROSAIL5B canopy radiative transfer model. Reflectance data of wheat crop, grown under different treatments, were measured by hand-held spectroradiometer and later integrated to LISS-3 reflectance using its band-wise relative spectral response function. Three inversion techniques were used and their performance was compared using different statistical parameters and target diagram. The inversion techniques tried were: a look up table with best solution (LUT-I), a look up table with mean of best 10% solutions (LUT-II) and an artificial neural network (ANN). All the techniques could estimate the biophysical variables by capturing variability in their observed values, though accuracy of estimation varied among the three techniques. Target diagram clearly depicted the superiority of LUT-II over the other two approaches indicating that a mean of best 10% solutions is a better strategy while ANN was worst performer showing highest bias for all the parameters. In all the three inversion techniques, the general order of retrieval accuracy was LAI > Cab > CCC > Cw. The range of Cw was very narrow and none of the techniques could estimate variations in it. In most of the cases, the parameters were underestimated by model inversion. The best identified LUT-II technique was then applied to retrieve wheat LAI from IRS LISS-3 satellite image of 5-Feb-2012 in Sheopur district. The comparison with ground observations showed that the RMSE of LAI retrieval was about 0.56, similar to that observed in ground experimentation. The findings of this study may help in refining the protocol for generating operational crop biophysical products from IRS LISS-3 or similar sensors.