Browsing by Author "Shamshiri, Redmond R."
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- ItemArtificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems(Basel : MDPI, 2021) Asfahan, Hafiz M.; Sajjad, Uzair; Sultan, Muhammad; Hussain, Imtiyaz; Hamid, Khalid; Ali, Mubasher; Wang, Chi-Chuan; Shamshiri, Redmond R.; Khan, Muhammad UsmanThe 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.
- ItemCrop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany(Basel : MDPI, 2022) Li, Minhui; Shamshiri, Redmond R.; Weltzien, Cornelia; Schirrmann, MichaelMonitoring within-field crop variability at fine spatial and temporal resolution can assist farmers in making reliable decisions during their agricultural management; however, it traditionally involves a labor-intensive and time-consuming pointwise manual process. To the best of our knowledge, few studies conducted a comparison of Sentinel-2 with UAV data for crop monitoring in the context of precision agriculture. Therefore, prospects of crop monitoring for characterizing biophysical plant parameters and leaf nitrogen of wheat and barley crops were evaluated from a more practical viewpoint closer to agricultural routines. Multispectral UAV and Sentinel-2 imagery was collected over three dates in the season and compared with reference data collected at 20 sample points for plant leaf nitrogen (N), maximum plant height, mean plant height, leaf area index (LAI), and fresh biomass. Higher correlations of UAV data to the agronomic parameters were found on average than with Sentinel-2 data with a percentage increase of 6.3% for wheat and 22.2% for barley. In this regard, VIs calculated from spectral bands in the visible part performed worse for Sentinel-2 than for the UAV data. In addition, large-scale patterns, formed by the influence of an old riverbed on plant growth, were recognizable even in the Sentinel-2 imagery despite its much lower spatial resolution. Interestingly, also smaller features, such as the tramlines from controlled traffic farming (CTF), had an influence on the Sentinel-2 data and showed a systematic pattern that affected even semivariogram calculation. In conclusion, Sentinel-2 imagery is able to capture the same large-scale pattern as can be derived from the higher detailed UAV imagery; however, it is at the same time influenced by management-driven features such as tramlines, which cannot be accurately georeferenced. In consequence, agronomic parameters were better correlated with UAV than with Sentinel-2 data. Crop growers as well as data providers from remote sensing services may take advantage of this knowledge and we recommend the use of UAV data as it gives additional information about management-driven features. For future perspective, we would advise fusing UAV with Sentinel-2 imagery taken early in the season as it can integrate the effect of agricultural management in the subsequent absence of high spatial resolution data to help improve crop monitoring for the farmer and to reduce costs.
- ItemDynamic Evaluation of Desiccant Dehumidification Evaporative Cooling Options for Greenhouse Air-Conditioning Application in Multan (Pakistan)(Basel : MDPI, 2021) Ashraf, Hadeed; Sultan, Muhammad; Shamshiri, Redmond R.; Abbas, Farrukh; Farooq, Muhammad; Sajjad, Uzair; Md-Tahir, Hafiz; Mahmood, Muhammad H.; Ahmad, Fiaz; Taseer, Yousaf R.; Shahzad, Aamir; Niazi, Badar M.K.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).
- ItemEffect of 1-Methyl Cyclopropane and Modified Atmosphere Packaging on the Storage of Okra (Abelmoschus esculentus L.) : Theory and Experiments(Basel : MDPI, 2020) Kanwal, Rabia; Ashraf, Hadeed; Sultan, Muhammad; Babu, Irrum; Yasmin, Zarina; Nadeem, Muhammad; Asghar, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Ahmad, Nisar; Imran, Muhammad A.; Zhou, Yuguang; Ahmad, RiazOkra 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.
- ItemEffect of In Vitro Digestion on the Antioxidant and Angiotensin-Converting Enzyme Inhibitory Potential of Buffalo Milk Processed Cheddar Cheese(Basel : MDPI AG, 2021) Shaukat, Amal; Nadeem, Muhammad; Qureshi, Tahir Mahmood; Kanwal, Rabia; Sultan, Muhammad; Kashongwe, Olivier Basole; Shamshiri, Redmond R.; Murtaza, Mian AnjumThe purpose of this study was to develop an in-vitro digestion protocol to evaluate the antioxidant potential of the peptides found in processed cheddar cheese using digestion enzymes. We first studied antioxidant and angiotensin-converting enzyme (ACE) inhibition and antioxidant activities of processed cheddar cheese with the addition of spices e.g., cumin, clove, and black pepper made from buffalo milk and ripened for 9 months. Then we conducted an in vitro digestion of processed cheddar cheese by gastric and duodenal enzymes. Freeze-dried water (WSE) and ethanol-soluble fractions (ESE) of processed cheddar cheese were also monitored for their ACE inhibition activity and antioxidant activities. In our preliminary experiments, different levels of spices (cumin, clove, and black pepper) were tested into a cheese matrix and only one level 0.2 g/100 g (0.2%) based on cheese weight was considered good after sensory evaluation. Findings of the present study revealed that ACE-inhibitory potential was the highest in processed cheese made from buffalo milk with the addition of 0.2% cumin, clove, and black pepper. A significant increase in ACE-inhibition (%) of processed cheddar cheese, as well as its WSE and ESE, was obtained. Lower IC50 values were found after duodenal phase digestion compared to oral phase digestion.
- ItemEffects of the COVID-19 Pandemic on Food Security and Agriculture in Iran: A Survey(Basel : MDPI AG, 2021) Rad, Abdullah Kaviani; Shamshiri, Redmond R.; Azarm, Hassan; Balasundram, Siva K.; Sultan, MuhammadThe consequences of COVID-19 on the economy and agriculture have raised many concerns about global food security, especially in developing countries. Given that food security is a critical component that is affected by global crises, beside the limited studies carried out on the macro-impacts of COVID-19 on food security in Iran, this paper is an attempt to address the dynamic impacts of COVID-19 on food security along with economic and environmental challenges in Iran. For this purpose, a survey was conducted with the hypothesis that COVID-19 has not affected food security in Iran. To address this fundamental hypothesis, we applied the systematic review method to obtain the evidence. Various evidences, including indices and statistics, were collected from national databases, scientific reports, field observations, and interviews. Preliminary results revealed that COVID-19 exerts its effects on the economy, agriculture, and food security of Iran through six major mechanisms, corresponding to a 30% decrease in the purchasing power parity in 2020 beside a significant increase in food prices compared to 2019. On the other hand, the expanding environmental constraints in Iran reduce the capacity of the agricultural sector to play a crucial role in the economy and ensure food security, and in this regard, COVID-19 forces the national programs and budget to combat rising ecological limitations. Accordingly, our study rejects the hypothesis that COVID-19 has not affected food security in Iran.
- ItemEnergy Systems and Applications in Agriculture(Basel : MDPI, 2022) Sultan, Muhammad; Mahmood, Muhammad Hamid; Ahamed, Md Shamim; Shamshiri, Redmond R.; Shahzad, Muhammad Wakil[No abstract available]
- ItemEvaluating Evaporative Cooling Assisted Solid Desiccant Dehumidification System for Agricultural Storage Application(Basel : MDPI, 2022) Hussain, Ghulam; Aleem, Muhammad; Sultan, Muhammad; Sajjad, Uzair; Ibrahim, Sobhy M.; Shamshiri, Redmond R.; Farooq, Muhammad; Usman Khan, Muhammad; Bilal, MuhammadThe study aims to investigate Maisotsenko cycle evaporative cooling assisted solid desiccant air‐conditioning (M‐DAC) system for agricultural storage application. Conventional air‐conditioning (AC) systems used for this application are refrigeration‐based which are expensive as they consume excessive amount of primary‐energy. In this regard, the study developed a lab‐scale solid silica gel‐based desiccant AC (DAC) system. Thermodynamic performance of the developed system was investigated using various adsorption/dehumidification and desorption/regeneration cycles. The system possesses maximum adsorption potential i.e., 4.88 g/kg‐DA at higher regeneration temperature of 72.6 °C and long cycle time i.e., 60 min: 60 min. Moreover, the system’s energy consumption performance was investigated from viewpoints of maximum latent, sensible, and total heat as well as latent heat ratio (LHR), which were found to be 0.64 kW, 1.16 kW, and 1.80 kW, respectively with maximum LHR of 0.49. Additionally, the study compared standalone DAC (S‐ DAC), and M‐DAC system thermodynamically to investigate the feasibility of these systems from the viewpoints of temperature and relative humidity ranges, cooling potential (Qp), and coefficient of performance (COP). The S‐DAC system showed temperature and relative humidity ranging from 39 °C to 48 °C, and 35% to 66%, respectively, with Qp and COP of 17.55 kJ/kg, and 0.37, respectively. Conversely, the M‐DAC system showed temperature and relative humidity ranging from 17 °C to 25 °C, and 76% to 98%, respectively, with Qp and COP of 41.80 kJ/kg, and 0.87, respectively. Additionally, the study investigated respiratory heat generation rate (Qres), and heat transfer rate (Qrate) by agricultural products at different temperature gradient (∆T) and air velocity. The Qres and Qrate by the products were increased with ∆T and air velocity, respectively, thereby generating heat loads in the storage house. Therefore, the study suggests that the M‐DAC system could be a potential AC option for agricultural storage application.
- ItemFree Discharge of Subsurface Drainage Effluent: An Alternate Design of the Surface Drain System in Pakistan(Basel : MDPI AG, 2021) Imran, Muhammad Ali; Xu, Jinlan; Sultan, Muhammad; Shamshiri, Redmond R.; Ahmed, Naveed; Javed, Qaiser; Asfahan, Hafiz Muhammad; Latif, Yasir; Usman, Muhammad; Ahmad, RiazIn Pakistan, many subsurface (SS) drainage projects were launched by the Salinity Control and Reclamation Project (SCARP) to deal with twin problems (waterlogging and salinity). In some cases, sump pumps were installed for the disposal of SS effluent into surface drainage channels. Presently, sump pumps have become dysfunctional due to social and financial constraints. This study evaluates the alternate design of the Paharang drainage system that could permit the discharge of the SS drainage system in the response of gravity. The proposed design was completed after many successive trials in terms of lowering the bed level and decreasing the channel bed slope. Interconnected MS-Excel worksheets were developed to design the L-section and X-section. Design continuity of the drainage system was achieved by ensuring the bed and water levels of the receiving drain were lower than the outfalling drain. The drain cross-section was set within the present row with a few changes on the service roadside. The channel side slope was taken as 1:1.5 and the spoil bank inner and outer slopes were kept as 1:2 for the entire design. The earthwork was calculated in terms of excavation for lowering the bed level and increasing the drain section to place the excavated materials in a specific manner. The study showed that modification in the design of the Paharang drainage system is technically admissible and allows for the continuous discharge of SS drainage effluent from the area.
- ItemImpact of Camera Viewing Angle for Estimating Leaf Parameters of Wheat Plants from 3D Point Clouds(Basel : MDPI, 2021) Li, Minhui; Shamshiri, Redmond R.; Schirrmann, Michael; Weltzien, CorneliaEstimation 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.
- ItemInvestigating Applicability of Evaporative Cooling Systems for Thermal Comfort of Poultry Birds in Pakistan(Basel : MDPI, 2020) Raza, Hafiz M.U.; Ashraf, Hadeed; Shahzad, Khawar; Sultan, Muhammad; Miyazaki, Takahiko; Usman, Muhammad; Shamshiri, Redmond R.; Zhou, Yuguang; Ahmad, RiazIn 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.
- ItemInvestigating Solid and Liquid Desiccant Dehumidification Options for Room Air-Conditioning and Drying Applications(Basel : MDPI, 2020) Naik, B. Kiran; Joshi, Mullapudi; Muthukumar, Palanisamy; Sultan, Muhammad; Miyazaki, Takahiko; Shamshiri, Redmond R.; Ashraf, HadeedThis study reports on the investigation of the performance of single and two-stage liquid and solid desiccant dehumidification systems and two-stage combined liquid and solid desiccant dehumidification systems with reference to humid climates. The research focus is on a dehumidification system capacity of 25 kW designed for room air conditioning application using the thermal models reported in the literature. RD-type silica gel and LiCl are used as solid and liquid desiccant materials, respectively. In this study, the application of proposed system for deep drying application is also explored. Condensation rate and moisture removal efficiency are chosen as performance parameters for room air conditioning application, whereas air outlet temperature is chosen as performance parameter for deep drying application. Further, for a given range of operating parameters, influences of air inlet humidity ratio, flow rate, and inlet temperature on performance parameters of the systems are investigated. In humid climatic conditions, it has been observed that a two-stage liquid desiccant dehumidification system is more effective for room air conditioning application, and two-stage solid desiccant dehumidification system is more suitable for deep drying application in the temperature range of 50 to 70 °C, while single-stage solid desiccant and two-stage combined liquid and solid desiccant dehumidification systems are more effective for low temperature, i.e., 30 to 50 °C deep drying application.
- ItemInvestigation of Energy Consumption and Associated CO2 Emissions for Wheat–Rice Crop Rotation Farming(Basel : MDPI, 2021) Ashraf, Muhammad N.; Mahmood, Muhammad H.; Sultan, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.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.
- ItemIoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato(Basel : MDPI, 2020) Rezvani, Sayed Moin-eddin; Abyaneh, Hamid Zare; Shamshiri, Redmond R.; Balasundram, Siva K.; Dworak, Volker; Goodarzi, Mohsen; Sultan, Muhammad; Mahns, BenjaminOptimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.
- ItemMachine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran(Basel : MDPI, 2022) Rad, Abdullah Kaviani; Shamshiri, Redmond R.; Naghipour, Armin; Razmi, Seraj-Odeen; Shariati, Mohsen; Golkar, Foroogh; Balasundram, Siva K.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.
- ItemModification of colorimetric method based digital soil test kit for determination of macronutrients in oil palm plantation(Beijing : [IJABE Editing and Publishing Office], 2020) Yamin, Muhammad; Ishak bin Wan Ismail, Wan; Saufi bin Mohd Kassim, Muhamad; Abd Aziz, Samsuzana Binti; Akbar, Farah Naz; Shamshiri, Redmond R.; Ibrahim, Muhammad; Mahns, BenjaminIt is the need of time that oil palm farmers must perform the spatially planned soil analysis to know about the fertilizer sufficient and deficient zones of land. Colorimetric method is a suitable and fast solution of soil analysis for NPK determination using the digital soil test kit. NPK determination procedure with a digital soil test kit was undefined for oil palm. Furthermore, the digital soil test kit determines the passage of light through an opaque medium of soil solution with a specified reagent. Therefore, environmental light may interfere leading to wrong results of NPK measurement. Likewise, this equipment was non-incorporable with the controller of any VRT fertilizer applicator. In this research, these issues were addressed and the NPK measurement procedure was defined for oil palm plantation by modifying the ‘soil to water’ ratio in sample soil solution with an optimum environmental light range of 18-23 W/m2. ‘Soil to water’ ratios were found for nitrogen, phosphorus and potassium as 0.31 to 5.00, 1.00 to 5.00 and 4.50 to 5.00, respectively to fit the requirement of NPK for oil palm in the prescribed range of the equipment. Validation study of modified digital soil test kit showed that 91.7% N, 89.6% P and 93.8% K results of modified digital soil test kit were matched with analytical laboratory method. Thus, the reliability of NPK results using digital soil test kit was enhanced, making the kit incorporable with the controller of variable rate fertilizer applicator through remote monitoring based data acquisition system. The outcome of this research can be used in the development of an IoT network data fusion for dynamic assessment of the NPK variation in the soil and nutrient management in oil palm plantations.
- ItemPollution Characteristics of Particulate Matter (PM2.5 and PM10) and Constituent Carbonaceous Aerosols in a South Asian Future Megacity(Basel : MDPI, 2020) Aslam, Afifa; Ibrahim, Muhammad; Shahid, Imran; Mahmood, Abid; Irshad, Muhammad Kashif; Yamin, Muhammad; Ghazala; Tariq, Muhammad; Shamshiri, Redmond R.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.
- ItemScientific Irrigation Scheduling for Sustainable Production in Olive Groves(Basel : MDPI AG, 2022) Aziz, Marjan; Khan, Madeeha; Anjum, Naveeda; Sultan, Muhammad; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Balasundram, Siva K.; Aleem, MuhammadThe present study aimed at investigating scientific irrigation scheduling (SIS) for the sustainable production of olive groves. The SIS allows farmers to schedule water rotation in their fields to abate crop water stress and maximize yields, which could be achieved through the precise monitoring of soil moisture. For this purpose, the study used three kinds of soil moisture sensors, including tensiometer sensors, irrometer sensors, and gypsum blocks for precise measurement of the soil moisture. These soil moisture sensors were calibrated by performing experiments in the field and laboratory at Barani Agricultural Research Institute, Chakwal in 2018 and 2019. The calibration curves were obtained by performing gravimetric analysis at 0.3 and 0.6 m depths, thereby equations were developed using regression analysis. The coefficient of determination (R2 ) at 0.3 and 0.6 m depth for tensiometer, irrometer, and gypsum blocks was found to be equal to 0.98, 0.98; 0.75, 0.89; and 0.82, and 0.95, respectively. After that, a drip irrigation system was installed with the calibrated soil moisture sensors at 0.3 and 0.6 m depth to schedule irrigation for production of olive groves as compared to conventional farmer practice, thereby soil moisture profiles of these sensors were obtained to investigate the SIS. The results showed that the irrometer sensor performed as expected and contributed to the irrigation water savings between 17% and 25% in 2018 and 2019, respectively, by reducing the number of irrigations as compared toother soil moisture sensors and farmer practices. Additionally, olive yield efficiencies of 8% and 9%were observed by the tensiometer in 2018 and 2019, respectively. The outcome of the study suggests that an effective method in providing sustainable production of olive groves and enhancing yield efficiency.
- ItemSimulating Cotton Growth and Productivity Using AquaCrop Model under Deficit Irrigation in a Semi-Arid Climate(Basel : MDPI AG, 2022) Aziz, Marjan; Rizvi, Sultan Ahmad; Sultan, Muhammad; Bazmi, Muhammad Sultan Ali; Shamshiri, Redmond R.; Ibrahim, Sobhy M.; Imran, Muhammad A.AquaCrop is a water-driven model that simulates the effect of environment and management on crop production under deficit irrigation. The model was calibrated and validated using three databases and four irrigation treatments (i.e., 100%ET, 80%ET, 70%ET, and 50%ET). Model performance was evaluated by simulating canopy cover (CC), biomass accumulation, and water productivity (WP). Statistics of root mean square error (RMSE) and Willmott’s index of agreement (d) showed that model predictions are suitable for non-stressed and moderate stressed conditions. The results showed that the simulated biomass and yield were consistent with the measured values with a coefficient of determination (R2) of 0.976 and 0.950, respectively. RMSE and d-index values for canopy cover (CC) were 2.67% to 4.47% and 0.991% to 0.998% and for biomass were 0.088 to 0.666 ton/ha and 0.991 to 0.999 ton/ha, respectively. Prediction of simulated and measured biomass and final yield was acceptable with deviation ˂10%. The overall value of R2 for WP in terms of yield was 0.943. Treatment with 80% ET consumed 20% less water than the treatment with 100%ET and resulted in high WP in terms of yield (0.6 kg/m3) and biomass (1.74 kg/m3), respectively. The deviations were in the range of −2% to 11% in yield and −2% to 4% in biomass. It was concluded that AquaCrop is a useful tool in predicting the productivity of cotton under different irrigation scenarios.
- ItemSoil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review(Basel : MDPI, 2022) Rasheed, Muhammad Waseem; Tang, Jialiang; Sarwar, Abid; Shah, Suraj; Saddique, Naeem; Khan, Muhammad Usman; Imran Khan, Muhammad; Nawaz, Shah; Shamshiri, Redmond R.; Aziz, Marjan; Sultan, MuhammadThe amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.