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Spatial Distribution Patterns for Identifying Risk Areas Associated with False Smut Disease of Rice in Southern India
False smut disease (FSD) of rice incited by Ustilaginoidea virens is an emerging threat to paddy cultivation worldwide. We investigated the spatial distribution of FSD in different paddy ecosystems of South Indian states, viz., Andhra Pradesh, Karnataka, Tamil Nadu, and Telangana, by considering the exploratory data from 111 sampling sites. Point pattern and surface interpolation analyses were carried out to identify the spatial patterns of FSD across the studied areas. The spatial clusters of FSD were confirmed by employing spatial autocorrelation and Ripley’s K function. Further, ordinary kriging (OK), indicator kriging (IK), and inverse distance weighting (IDW) were used to create spatial maps by predicting the values at unvisited locations. The agglomerative hierarchical cluster analysis using the average linkage method identified four main clusters of FSD. From the Local Moran’s I statistic, most of the areas of Andhra Pradesh and Tamil Nadu were clustered together (at I > 0), except the coastal and interior districts of Karnataka (at I < 0). Spatial patterns of FSD severity were determined by semi-variogram experimental models, and the spherical model was the best fit. Results from the interpolation technique, the potential FSD hot spots/risk areas were majorly identified in Tamil Nadu and a few traditional rice-growing ecosystems of Northern Karnataka. This is the first intensive study that attempted to understand the spatial patterns of FSD using geostatistical approaches in India. The findings from this study would help in setting up ecosystem-specific management strategies to reduce the spread of FSD in India.
Investigating Applicability of Evaporative Cooling Systems for Thermal Comfort of Poultry Birds in Pakistan
In the 21st century, the poultry sector is a vital concern for the developing economies including Pakistan. The summer conditions of the city of Multan (Pakistan) are not comfortable for poultry birds. Conventionally, swamp coolers are used in the poultry sheds/houses of the city, which are not efficient enough, whereas compressor-based systems are not economical. Therefore, this study is aimed to explore a low-cost air-conditioning (AC) option from the viewpoint of heat stress in poultry birds. In this regard, the study investigates the applicability of three evaporative cooling (EC) options, i.e., direct EC (DEC), indirect EC (IEC), and Maisotsenko-cycle EC (MEC). Performance of the EC systems is investigated using wet-bulb effectiveness (WBE) for the climatic conditions of Multan. Heat stress is investigated as a function of poultry weight. Thermal comfort of the poultry birds is calculated in terms of temperature-humidity index (THI) corresponding to the ambient and output conditions. The heat production from the poultry birds is calculated using the Pederson model (available in the literature) at various temperatures. The results indicate a maximum temperature gradient of 10.2 °C (MEC system), 9 °C (DEC system), and 6.5 °C (IEC systems) is achieved. However, in the monsoon/rainfall season, the performance of the EC systems is significantly reduced due to higher relative humidity in ambient air.
Machine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran
Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.
Impact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds
Estimation of plant canopy using low-altitude imagery can help monitor the normal growth status of crops and is highly beneficial for various digital farming applications such as precision crop protection. However, extracting 3D canopy information from raw images requires studying the effect of sensor viewing angle by taking into accounts the limitations of the mobile platform routes inside the field. The main objective of this research was to estimate wheat (Triticum aestivum L.) leaf parameters, including leaf length and width, from the 3D model representation of the plants. For this purpose, experiments with different camera viewing angles were conducted to find the optimum setup of a mono-camera system that would result in the best 3D point clouds. The angle-control analytical study was conducted on a four-row wheat plot with a row spacing of 0.17 m and with two seeding densities and growth stages as factors. Nadir and six oblique view image datasets were acquired from the plot with 88% overlapping and were then reconstructed to point clouds using Structure from Motion (SfM) and Multi-View Stereo (MVS) methods. Point clouds were first categorized into three classes as wheat canopy, soil background, and experimental plot. The wheat canopy class was then used to extract leaf parameters, which were then compared with those values from manual measurements. The comparison between results showed that (i) multiple-view dataset provided the best estimation for leaf length and leaf width, (ii) among the single-view dataset, canopy, and leaf parameters were best modeled with angles vertically at -45⸰_ and horizontally at 0⸰_ (VA -45, HA 0), while (iii) in nadir view, fewer underlying 3D points were obtained with a missing leaf rate of 70%. It was concluded that oblique imagery is a promising approach to effectively estimate wheat canopy 3D representation with SfM-MVS using a single camera platform for crop monitoring. This study contributes to the improvement of the proximal sensing platform for crop health assessment. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Investigation of Energy Consumption and Associated CO2 Emissions for Wheat–Rice Crop Rotation Farming
This study investigates the input–output energy-flow patterns and CO2 emissions from the wheat–rice crop rotation system. In this regard, an arid region of Punjab, Pakistan was selected as the study area, comprising 4150 km2. Farmers were interviewed to collect data and information on input/output sources during the 2020 work season. The total energy from these sources was calculated using appropriate energy equivalents. Three energy indices, including energy use efficiency (ηe), energy productivity (ηp), and net energy (ρ), were defined and calculated to investigate overall energy efficiency. Moreover, the data envelopment analysis (DEA) technique was used to optimize the input energy in wheat and rice production. Finally, CO2 emissions was calculated using emissions equivalents from peer-reviewed published literature. Results showed that the average total energy consumption in rice production was twice the energy consumed in wheat production. However, the values of ηe, ηp, and ρ were higher in wheat production and calculated as 5.68, 202.3 kg/GJ, and 100.12 GJ/ha, respectively. The DEA showed the highest reduction potential in machinery energy for both crops, calculated as −42.97% in rice production and −17.48% in wheat production. The highest CO2 emissions were found in rice production and calculated as 1762.5 kg-CO2/ha. Our conclusion indicates that energy consumption and CO2 emissions from wheat–rice cropping systems can be minimized using optimized energy inputs.
Dynamic Evaluation of Desiccant Dehumidification Evaporative Cooling Options for Greenhouse Air-Conditioning Application in Multan (Pakistan)
This study provides insights into the feasibility of a desiccant dehumidification-based Maisotsenko cycle evaporative cooling (M-DAC) system for greenhouse air-conditioning application. Conventional cooling techniques include direct evaporative cooling, refrigeration systems, and passive/active ventilation. which are commonly used in Pakistan; however, they are either not feasible due to their energy cost, or they cannot efficiently provide an optimum microclimate depending on the regions, the growing seasons, and the crop being cultivated. The M-DAC system was therefore proposed and evaluated as an alternative solution for air conditioning to achieve optimum levels of vapor pressure deficit (VPD) for greenhouse crop production. The objective of this study was to investigate the thermodynamic performance of the proposed system from the viewpoints of the temperature gradient, relative humidity level, VPD, and dehumidification gradient. Results showed that the standalone desiccant air-conditioning (DAC) system created maximum dehumidification gradient (i.e., 16.8 g/kg) and maximum temperature gradient (i.e., 8.4 °C) at 24.3 g/kg and 38.6 °C ambient air conditions, respectively. The DAC coupled with a heat exchanger (DAC+HX) created a temperature gradient nearly equal to ambient air conditions, which is not in the optimal range for greenhouse growing conditions. Analysis of the M-DAC system showed that a maximum air temperature gradient, i.e., 21.9 °C at 39.2 °C ambient air condition, can be achieved, and is considered optimal for most greenhouse crops. Results were validated with two microclimate models (OptDeg and Cft) by taking into account the optimality of VPD at different growth stages of tomato plants. This study suggests that the M-DAC system is a feasible method to be considered as an efficient solution for greenhouse air-conditioning under the climate conditions of Multan (Pakistan).
Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems
The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the prediction efficiency. The deep learning algorithm was then optimized to increase the learning rate and predictive accuracy with respect to experimental data by tuning the hyperparameters, such as by manipulating the activation functions, the number of hidden layers, and the neurons in each layer by incorporating optimizers, including Adam and RMsprop. The results confirmed the applicability of the method with an overall value of R2 = 0.987 between the input data and ground-truth data, showing that the most competent model could predict the designated output features (Tdbout, wout, and Eairout). The suggested method is straightforward and was found to be practical in the evaluation of the thermal performance of deployed air conditioning systems under different conditions. The results supported the hypothesis that the proposed deep learning AI algorithm has the potential to explore the feasibility of the three evaporative cooling systems in dynamic ambient conditions for various agricultural and livestock applications.
Pollution Characteristics of Particulate Matter (PM2.5 and PM10) and Constituent Carbonaceous Aerosols in a South Asian Future Megacity
The future megacity of Faisalabad is of prime interest when considering environmental health because of its bulky population and abundant industrial and anthropogenic sources of coarse particles (PM10) and fine airborne particulate matter (PM2.5). The current study was aimed to investigate the concentration level of PM2.5 and PM10, also the characterization of carbonaceous aerosols including organic carbon (OC), elemental carbon (EC) and total carbon (TC) in PM2.5 and PM10 samples collected from five different sectors (residential, health, commercial, industrial, and vehicular zone). The data presented here are the first of their kind in this sprawling city having industries and agricultural activities side by side. Results of the study revealed that the mass concentration of PM2.5 and PM10 is at an elevated level throughout Faisalabad, with ambient PM2.5 and PM10 points that constantly exceeded the 24-h standards of US-EPA, and National Environment Quality Standards (NEQS) which poses harmful effects on the quality of air and health. The total carbon concentration varied between 21.33 and 206.84 μg/m3, and 26.08 and 211.15 μg/m3 with an average of 119.16 ± 64.91 μg/m3 and 124.71 ± 64.38 μg/m3 for PM2.5 in summer and winter seasons, respectively. For PM10, the concentration of TC varied from 34.52 to 289.21 μg/m3 with an average of 181.50 ± 87.38 μg/m3 (for summer season) and it ranged between 44.04 and 300.02 μg/m3 with an average of 191.04 ± 87.98 μg/m3 (winter season), respectively. No significant difference between particulate concentration and weather parameters was observed. Similarly, results of air quality index (AQI) and pollution index (PI) stated that the air quality of Faisalabad ranges from poor to severely pollute. In terms of AQI, moderate pollution was recorded on sampling sites in the following order; Ittehad Welfare Dispensary > Saleemi Chowk > Kashmir Road > Pepsi Factory, while at Nazria Pakistan Square and Allied Hospital, higher AQI values were recorded. The analysis and results presented in this study can be used by policy-makers to apply rigorous strategies that decrease air pollution and the associated health effects in Faisalabad.
Effect of 1-Methyl Cyclopropane and Modified Atmosphere Packaging on the Storage of Okra (Abelmoschus esculentus L.) : Theory and Experiments
Okra possesses a short shelf-life which limits its marketability, thereby, the present study investigates the individual and combined effect of 1-methylcyclopropene (1-MCP) and modified atmosphere packaging (MAP) on the postharvest storage life of okra. The treated/ untreated okra samples were stored at ambient (i.e., 27 °C) and low (i.e., 7 °C) temperatures for eight and 20 days, respectively. Results revealed that the 1-MCP and/or MAP treatment successfully inhibited fruit softening, reduction in mucilage viscosity, and color degradation (hue angle, ∆E, and BI) in the product resulting in a longer period of shelf-life. However, MAP with or without 1-MCP was more effective to reduce weight loss in okra stored at both ambient and cold storage conditions. Additionally, ascorbic acid and total antioxidants were also retained in 1-MCP with MAP during cold storage. The 1-MCP in combination with MAP effectively suppressed respiration rate and ethylene production for four days and eight days at 27 °C and 7 °C temperature conditions, respectively. According to the results, relatively less chilling injury stress also resulted when 1-MCP combined with MAP. The combined treatment of okra pods with 1-MCP and MAP maintained the visual quality of the product in terms of overall acceptability for four days at 20 °C and 20 days at 7 °C.