Advances in Deep Learning Algorithms for Agricultural Monitoring and Management

This study examines the transformative role of deep learning algorithms in agricultural monitoring and management. Deep learning has shown remarkable progress in predicting crop yields based on historical weather, soil, and crop data, thereby enabling optimized planting and harvesting strategies. In disease and pest detection, image recognition technologies such as Convolutional Neural Networks (CNNs) can analyze high-resolution images of crops to identify early signs of diseases or pest infestations, allowing for swift and effective interventions. In the context of precision agriculture, these advanced techniques offer resource efficiency by enabling targeted treatments within specific field areas, significantly reducing waste. The paper also sheds light on the application of deep learning in analyzing vast amounts of remote sensing and satellite imagery data, aiding in real-time monitoring of crop growth, soil moisture, and other critical environmental factors. In the face of climate change, advanced algorithms provide valuable insights into its potential impact on agriculture, thereby aiding the formulation of effective adaptation strategies. Automated harvesting and sorting, facilitated by robotics powered by deep learning, are also investigated, as they promise increased efficiency and reduced labor costs. Moreover, machine learning models have shown potential in optimizing the entire agricultural supply chain, ensuring minimal waste and optimum product quality. Lastly, the study highlights the power of deep learning in integrating multi-source data, from weather stations to satellites, to form comprehensive monitoring systems that allow real-time decision-making

Functionalized Carbon Nanotubes (CNTs) for Water and Wastewater Treatment: Preparation to Application

As the world human population and industrialization keep growing, the water availability issue has forced scientists, engineers, and legislators of water supply industries to better manage water resources. Pollutant removals from wastewaters are crucial to ensure qualities of available water resources (including natural water bodies or reclaimed waters). Diverse techniques have been developed to deal with water quality concerns. Carbon based nanomaterials, especially carbon nanotubes (CNTs) with their high specific surface area and associated adsorption sites, have drawn a special focus in environmental applications, especially water and wastewater treatment. This critical review summarizes recent developments and adsorption behaviors of CNTs used to remove organics or heavy metal ions from contaminated waters via adsorption and inactivation of biological species associated with CNTs. Foci include CNTs synthesis, purification, and surface modifications or functionalization, followed by their characterization methods and the effect of water chemistry on adsorption capacities and removal mechanisms. Functionalized CNTs have been proven to be promising nanomaterials for the decontamination of waters due to their high adsorption capacity. However, most of the functional CNT applications are limited to lab-scale experiments only. Feasibility of their large-scale/industrial applications with cost-effective ways of synthesis and assessments of their toxicity with better simulating adsorption mechanisms still need to be studied

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)

Potential Investigation of Membrane Energy Recovery Ventilators for the Management of Building Air-Conditioning Loads

The present study provides insights into the energy-saving potential of a membrane energy recovery ventilator (ERV) for the management of building air-conditioning loads. This study explores direct (DEC), Maisotsenko cycle (MEC) evaporative cooling, and vapor compression (VAC) systems with ERV. Therefore, this study aims to explore possible air-conditioning options in terms of temperature, relative humidity, human thermal comfort, wet bulb effectiveness, energy saving potential, and CO2 emissions. Eight different combinations of the above-mentioned systems are proposed in this study i.e., DEC, MEC, VAC, MEC-VAC, and their possible combinations with and without ERVs. A building was modeled in DesignBuilder and simulated in EnergyPlus. The MEC-VAC system with ERV achieved the highest temperature gradient, wet bulb effectiveness, energy-saving potential, optimum relative humidity, and relatively lower CO2 emissions i.e., 19.7 °C, 2.2, 49%, 48%, and 499.2 kgCO2/kWh, respectively. Thus, this study concludes the hybrid MEC-VAC system with ERV the optimum system for the management of building air-conditioning loads

Spatiotemporal Estimation of Reference Evapotranspiration for Agricultural Applications in Punjab, Pakistan

Estimation of reference evapotranspiration (ETo) is a key element in water resources management and crop water requirement which, in turn, affects irrigation scheduling. ETo is subject to the influence of various climatic parameters including minimum temperature (Tmin), maximum temperature (Tmax), relative humidity (RH), windspeed (WS), and sunshine hours (SH). Usually, the influence of the climatic parameters and a dominating climatic factor influencing ETo is estimated on yearly basis. However, in diverse climatic regions, ETo varies with the varying climate. Therefore, this study aims to estimate the spatiotemporal variation in the influence of the climatic parameters on ETo in Punjab, Pakistan, for the period 1950–2021, specifically focusing on decennial, annual, and monthly patterns. The study area was divided into five agroclimatic zones. The Penman–Monteith model was used to estimate ETo. The influence was assessed using geographic weighted regression (GWR) and multiscale geographic weighted regression (MGWR) as the primary methods. As per results from MGWR, ETo in Punjab was highly influenced by the Tmin, Tmax, and WS. Additionally, annual ETo exhibited a higher value in southern Punjab in comparison to northern Punjab, with a range of 2975 mm/year in the cotton–wheat zone to 1596 mm/year in the rain-fed zone. Over the course of the past seventy years, Punjab experienced an average increasing slope of 5.18 mm/year in ETo. Tmin was the highest monthly dominant factor throughout the year, whereas WS and SH were recorded to be the dominant factor in the winters, specifically. All in all, accurate estimation of ETo, which serves as an essential component for crop water requirement, could potentially help improve the irrigation scheduling of crops in the agroclimatic zones

Evaporative Cooling Options for Building Air-Conditioning: A Comprehensive Study for Climatic Conditions of Multan (Pakistan)

This study provides comprehensive details of evaporative cooling options for building air-conditioning (AC) in Multan (Pakistan). Standalone evaporative cooling and standalone vapor compression AC (VCAC) systems are commonly used in Pakistan. Therefore, seven AC system configurations comprising of direct evaporative cooling (DEC), indirect evaporative cooling (IEC), VCAC, and their possible combinations, are explored for the climatic conditions of Multan. The study aims to explore the optimum AC system configuration for the building AC from the viewpoints of cooling capacity, system performance, energy consumption, and CO2 emissions. A simulation model was designed in DesignBuilder and simulated using EnergyPlus in order to optimize the applicability of the proposed systems. The standalone VCAC and hybrid IEC-VCAC & IEC-DEC-VCAC system configurations could achieve the desired human thermal comfort. The standalone DEC resulted in a maximum COP of 4.5, whereas, it was 2.1 in case of the hybrid IEC-DEC-VCAC system. The hybrid IEC-DEC-VCAC system achieved maximum temperature gradient (21 °C) and relatively less CO2 emissions as compared to standalone VCAC. In addition, it provided maximum cooling capacity (184 kW for work input of 100 kW), which is 85% higher than the standalone DEC system. Furthermore, it achieved neutral to slightly cool human thermal comfort i.e., 0 to −1 predicted mean vote and 30% of predicted percentage dissatisfied. Thus, the study concludes the hybrid IEC-DEC-VCAC as an optimum configuration for building AC in Multan