Journal Papers by Dr. Redmond R . Shamshiri
International Journal of Intelligent Robotics and Applications , 2024
Navigation of agricultural mobile platforms in small-scale orchards poses challenges due to narro... more Navigation of agricultural mobile platforms in small-scale orchards poses challenges due to narrow row-end turning spaces and the need for precise path tracking in the presence of disturbances. The objective of this study is to improve path following and rapid turning maneuvers for a double-Ackermann steering robot by employing a simulation approach for PID-based waypoint following enhanced by learning-based H∞ robust adaptive control. With the zero-speed turning radius of the robot measured at 2.85 m, the primary question to address is determining the minimum achievable turning radius using the two controllers. For this purpose, a versatile framework for fine-tuning and analyzing of the controllers is presented in MATLAB Simulink blocks interfaced with the virtual replica of the robot in CoppeliaSim. A comparative study between the controllers is carried out involving three experiments: offline path following with a fixed number of predefined waypoints, online path following with continuously updated waypoints forming paths, and path tracking with disturbance rejection using the H∞ controller to reduce the radius of row-end turnings. Results indicate that while the PID controller achieves a minimum row-end turning radius of 3.0 m, the learning-based H∞ controller surpasses it with a minimum radius of 2.9 m. It is observed that a minimum of 4 waypoints is required for the PID controller to perform effective row-end turning in the offline experiment, with a higher number of waypoints enabling the robot to navigate through complex geometries and tight turns more effectively. Moreover, by incorporating an actor-critic structure, it has been demonstrated that the learning-based H∞ controller maintains stability even when facing wheel slippage disturbances, and outperforms the PID controller in online path tracking, particularly when maneuvering along a half-circle path. The framework proposed in this study contributes to improving autonomous navigation, particularly in determining the optimal number of waypoints and path configurations required for navigating agricultural robots with varying dimensions and steering mechanisms.
Discover Applied Sciences, 2024
In unstructured agricultural fields where autonomous navigation is challenging and demands additi... more In unstructured agricultural fields where autonomous navigation is challenging and demands additional safety, the operator’s experience and knowledge are essential for supervising operations and making decisions beyond the robot’s autonomous capabilities. Local networks with long-range wireless communication combined with digital twin concepts are promising solutions that can be used for robot teleoperation. The purpose of this study was to demonstrate the feasibility of supervising a mobile robot inside berry orchards using a digital shadow from a long-range distance (between 300 and 3000 m), with the primary objective of assisting the robot in navigating in complex situations such as row-end turning. This involved creating a virtual representation of the robot that mirrors its state and actions, allowing the remote operator to monitor and guide the robot effectively. The system comprised a GPS-based navigation controller with collision avoidance sensors, two sets of LoRa transmitters and repeaters, a simulation environment with a digital shadow of the robot, and a graphical user interface for the remote operator. Information about the digital shadow’s state, including location, orientation, and distances to obstacles, was received as a message by the LoRa gateway and was used to update the path for the actual robot that interfaced with the Robot Operating System (ROS). The main research hypothesis aimed to test the quality of the LoRa communication link between the robot and the operator, as well as the robustness of the robot’s control system, with an emphasis on the architecture, communication link, and situation awareness creation. Preliminary results showed that depending on the environment, the average packet loss was 12% at distances of approximately 2300 m. Our results highlight some of the core technical challenges that need to be addressed for an effective teleoperation system, including latency, stability, and the limited range of wireless communication. Future works involves evaluating the performance and reliability of the proposed method under different field conditions and scenarios, as well as considering the use of the 5G network for a significant improvement in data transmission speed, navigation efficiency, and visual feedback. Upon successful implementation, this study has the potential to enhance the efficiency and safety of robot navigation, providing a practical solution for remote supervision in challenging environments.
Front. Environ. Sci., 2024
The digitalization of agriculture is rapidly changing the way farmers do business. With the integ... more The digitalization of agriculture is rapidly changing the way farmers do business. With the integration of advanced technology, farmers are now able to increase efficiency, productivity, and precision in their operations. Digitalization allows for real-time monitoring and management of crops, leading to improved yields and reduced waste. This paper presents a review of some of the use cases that digitalization has made an impact in the automation of open-field and closed-field cultivations by means of collecting data about soils, crop growth, and microclimate, or by contributing to more accurate decisions about water usage and fertilizer application. The objective was to address some of the most recent technological advances that are leading to increased efficiency and sustainability of crop production, reduction in the use of inputs and environmental impacts, and releasing manual workforces from repetitive field tasks. The short discussions included at the end of each case study attempt to highlight the limitations and technological challenges toward successful implementations, as well as to introduce alternative solutions and methods that are rapidly evolving to offer a vast array of benefits for farmers by influencing cost-saving measures. This review concludes that despite the many benefits of digitalization, there are still a number of challenges that need to be overcome, including high costs, reliability, and scalability. Most of the available setups that are currently used for this purpose have been custom designed for specific tasks and are still too expensive to be implemented on commercial scales, while others are still in their early stages of development, making them not reliable or scalable for widespread acceptance and adoption by farmers. By providing a comprehensive understanding of the current state of digitalization in agriculture and its impact on sustainable crop production and food security, this review provides insights for policy-makers, industry stakeholders, and researchers working in this field.
Computers and Electronics in Agriculture, 2024
Autonomous navigation of mobile robots inside unstructured agricultural fields proposes serious c... more Autonomous navigation of mobile robots inside unstructured agricultural fields proposes serious challenges due to the extreme variations in high-density bushes, the presence of random obstacles, and the inaccuracies in the GPS and IMU measurements. Advanced perception solutions are therefore required to assist the existing GPS-based navigation and to improve the reliability of the operation. This paper reports on the development and evaluation of a modular and scalable sensing system to assist the autonomous navigation of an agricultural mobile robot by providing it with collision avoidance capabilities. The robot benefited from a four-wheel steering mechanism that could be driven remotely via a 2.4 GHz wireless transmitter and could be programmed using the Robot Operating System (ROS) to follow waypoints. Multiple arrays of Time-of-Flight and infrared sensors with independent processing units were installed on the left, right, and front of the robot to enable a distributed control system. Communication between the sensor modules was realized via a CAN network. The collision avoidance system then exchanged messages with the robot computer over Ethernet using ROS on multiple machines scheme. A virtual model of the robot with an exact sensing setup was replicated in a robotic simulator to accelerate experimenting with different control algorithms and to optimize the sensors’ functionality. The simulation scenes and dynamic models were then improved by manually driving the robot in a real berry field for collecting sensor and steering data. Results from the simulation showed that the robot was able to autonomously navigate in different tracks and stabilize itself in the presence of random obstacles using a fuzzy knowledge-based algorithm. Preliminary field tests suggested that the Exponential filter was necessary to be implemented on each sensor for removing noise and outliers. The proposed approach created a flexible framework for exchanging data between each of the sensor ECUs and preventing the robot from colliding with random obstacles in front, left, and right. The study confirmed the functionality of the affordable sensing system and control architecture and can be suggested as an alternative solution for the high-end 3D LiDARs and the complex simultaneous localization and mapping methods.
Frontiers in Robotics and AI, 2024
Agriculture 4.0 presents several challenges for the automation of various operations, including t... more Agriculture 4.0 presents several challenges for the automation of various operations, including the fundamental task of harvesting. One of the crucial aspects in the automatic harvesting of high value crops is the grip and detachment of delicate fruits without spoiling them or interfering with the environment. Soft robotic systems, particularly soft grippers, offer a promising solution for this problem, as they can operate in unstructured environments, manipulate objects delicately, and interact safely with humans. In this context, this article presents a soft gripper design for harvesting as well as for pick-and-place operations of small and medium-sized fruits. The gripper is fabricated using 3D printing technology with a flexible thermoplastic elastomer filament. This approach enables the production of an economical, compact, easily replicable, and interchangeable gripper by utilizing soft robotics principles, such as flexible structures and pneumatic actuation.
Precision Agriculture, 2023
Spatio-temporal variability of soil fertility and cocoa pod borer (CPB) infestation rate provides... more Spatio-temporal variability of soil fertility and cocoa pod borer (CPB) infestation rate provides strategic information about the soil nutrients and CPB population densities at different harvest intervals. This enables the transitioning of cocoa fields (cooca-gliricidia and cocoa-coconut) from conventional to modern precision management. Geostatistical methods were applied to interpolate the data collected from a systematic grid based on a cluster of six cocoa tree stands for both fields and produce maps representing the spatial variability of all soil variables and CPB attack. Cocoa fresh bean weight and CPB infestation data were collected at two week-intervals from cocoa-gliricidia and cocoa-coconut. All field data points were geo-referenced by a differential global positioning system. Data were processed for possible outliers, and analysed by variography and interpolation techniques for quantification of spatial variability. Results showed that both plots exhibited definable spatial structures and were described by exponential models. Precision cocoa management recorded an increase in crop yield by 52.8 and 37.5% at cocoa-gliricidia and cocoa-coconut, respectively. Site-specific nutrient management and integrated pest control in the critical zones showed improvement in cocoa yields, especially during the peak harvest season.
MDPI Sustainability , 2023
Digital agriculture involving different tools and management practices has advanced considerably ... more Digital agriculture involving different tools and management practices has advanced considerably in recent years, intending to overcome climate risk and reduce food insecurity. Climate change and its impacts on agricultural production and food security are significant sources of public concern worldwide. The objective of this study was to provide an overview of the potential impact of digital agriculture technologies and practices that can reduce greenhouse gas emissions and enhance productivity while ensuring food security. Based on a comprehensive survey of the previously published works, it was found that due to global warming, altered precipitation patterns, and an increase in the frequency of extreme events, climate change has negatively impacted food security by reducing agricultural yields, slowing animal growth rates, and decreasing livestock productivity. The reviewed works also suggest that using digital technology in agriculture is necessary to mitigate the effect of climate change and food insecurity. In addition, issues regarding creating sustainable agricultural food systems, minimizing environmental pollution, increasing yields, providing fair and equitable food distribution, and reducing malnutrition leading to food security were discussed in detail. It was shown that while digital agriculture has a crucial role in mitigating climate change and ensuring food security, it requires a concerted effort from policymakers, researchers, and farmers to ensure that the benefits of digitalization are realized in a sustainable and equitable manner.
Remote Sensing, 2022
Monitoring within-field crop variability at fine spatial and temporal resolution can assist farme... more Monitoring 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.
MDPI Sustainability, 2022
Air pollution, as one of the most significant environmental challenges, has adversely affected th... more 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 (R 2), 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 (R 2 PM2.5 = 0.36, R 2 PM10 = 0.27, R 2 NO2 = 0.46, R 2 SO2 = 0.41, R 2 O3 = 0.52, and R 2 CO = 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.
Smart Agricultural Technology, 2022
Hydroponic fodder production in controlled environment (CE) settings have gained more focus in re... more Hydroponic fodder production in controlled environment (CE) settings have gained more focus in recent years due to the shortage of agricultural land for food production and the adverse effect of climate changes. However, the operation costs and dry matter issues are the major concerns for the sustainability of fodder production in the CE. This study provides a comprehensive literature review on techniques and control strategies for indoor environments and watering that are currently used and could be adopted in the future to achieve the economic and environmental sustainability of controlled environment fodder production (CEFP). The literature indicates fodder production in the modular system is becoming popular in developed countries, and low-tech systems like greenhouse are more prevalent in developing countries. The optimum temperature and RH range between 16-27°C and 70-80% to get efficient biomass yield; however, minimal research has been conducted to optimize the indoor temperature and relative humidity (RH) for efficient and higher efficiency fodder production. Besides, the water-saving techniques and optimal lighting spectrum need to be studied extensively. Automating and monitoring in CEFP system could reduce operating costs and improve quality and yield. Overall, this industry might have great potential for livestock production. Still, more strong research needs to be conducted to answer nutritional concerns and reduce the capital and operating costs for CEFP.
MDPI Remote Sensing, 2022
Leaf area index (LAI) and height are two critical measures of maize crops that are used in ecophy... more Leaf area index (LAI) and height are two critical measures of maize crops that are used in ecophysiological and morphological studies for growth evaluation, health assessment, and yield prediction. However, mapping spatial and temporal variability of LAI in fields using handheld tools and traditional techniques is a tedious and costly pointwise operation that provides information only within limited areas. The objective of this study was to evaluate the reliability of mapping LAI and height of maize canopy from 3D point clouds generated from UAV oblique imagery with the adaptive micro-terrain model. The experiment was carried out in a field planted with three cultivars having different canopy shapes and four replicates covering a total area of 48 × 36 m. RGB images in nadir and oblique view were acquired from the maize field at six different time slots during the growing season. Images were processed by Agisoft Metashape to generate 3D point clouds using the structure from motion method and were later processed by MATLAB to obtain clean canopy structure, including height and density. The LAI was estimated by a multivariate linear regression model using crop canopy descriptors derived from the 3D point cloud, which account for height and leaf density distribution along the canopy height. A simulation analysis based on the Sine function effectively demonstrated the micro-terrain model from point clouds. For the ground truth data, a randomized block design with 24 sample areas was used to manually measure LAI, height, N-pen data, and yield during the growing season. It was found that canopy height data from the 3D point clouds has a relatively strong correlation (R 2 = 0.89, 0.86, 0.78) with the manual measurement for three cultivars with CH90. The proposed methodology allows a cost-effective high-resolution mapping of in-field LAI index extraction through UAV 3D data to be used as an alternative to the conventional LAI assessments even in inaccessible regions.
MDPI Agriculture, 2021
The demand for smart irrigation and water-saving practices in agriculture has triggered the devel... more The demand for smart irrigation and water-saving practices in agriculture has triggered the development of different soil moisture sensing techniques that can operate under harsh field conditions. In this study, a soil moisture sensing technology appropriate for the field applications was comprehensively evaluated. From a qualitative and quantitative perspective, the Delphi and analytic hierarchy process methods were used to construct an index system involving technological advantage, economic benefit, risk analysis, policy support, four second-level indicators, and 23 fourth-level indicators. The results showed that economic benefits account for the largest weight. The practical evaluation resulted in 12 farms that showed that the selected soil water sensing methods performed reasonably and exhibited obvious water-saving irrigation benefits, which are usually used for scheduling irrigation. The overall score of M4 in different soil types was 0.2% lower than that of M5. Farms with reasonable economic conditions and a high awareness scored 5.3% higher on technology than those with modest economic conditions, which clearly affects the evaluation scores of the two technologies. The evaluation results help farmers and government decision-making bodies in technology selection, production decision-making, and risk control.
Sustainability
The consequences of COVID-19 on the economy and agriculture have raised many concerns about globa... more The 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, corresp...
International Journal of Agricultural and Biological Engineering, 2021
Soil moisture is directly related to the amount of irrigation in agriculture and influences the y... more Soil moisture is directly related to the amount of irrigation in agriculture and influences the yield of crops. Accordingly, a soil moisture sensor is an important tool for measuring soil moisture content. In this study, the previous research conducted in recent 2-3 decades on soil moisture sensors was reviewed and the principles of commonly used soil moisture sensor and their various applications were summarized. Furthermore, the advantages, disadvantages, and influencing factors of various measurement methods employed were compared and analyzed. The improvements were presented by several scholars have established the major applications and performance levels of soil moisture sensors, thereby setting the course for future development. These studies indicated that soil moisture sensors in the future should be developed to achieve high-precision, low-cost, non-destructive, automated, and highly integrated systems. Also, it was indicated that future studies should involve the development of specialized sensors for different applications and scenarios. This review research aimed to provide a certain reference for application departments and scientific researchers in the process of selecting soil moisture sensor products and measuring soil moisture.
MDPI Energies, 2021
This study investigates the input-output energy-flow patterns and CO2 emissions from the wheat-ri... more 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 km 2. 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.
MDPI Agriculture, 2021
Estimation of plant canopy using low-altitude imagery can help monitor the normal growth status o... more 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.
MDPI Energies, 2021
The present study reports the development of a deep learning artificial intelligence (AI) model f... more 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 (, , and ). 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.
Applied Sciences
In the 21st century, the poultry sector is a vital concern for the developing economies including... more 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 condi...
Next-Generation Greenhouses for Food Security
Temperature and humidity control are crucial in next generation greenhouses. Plants require optim... more Temperature and humidity control are crucial in next generation greenhouses. Plants require optimum temperature/humidity and vapor pressure deficit conditions inside the greenhouse for optimum yield. In this regard, an air-conditioning system could provide the required conditions in harsh climatic regions. In this study, the authors have summarized their published work on different desiccant and evaporative cooling options for greenhouse air-conditioning. The direct, indirect, and Maisotsenko cycle evaporative cooling systems, and multi-stage evaporative cooling systems have been summarized in this study. Different desiccant materials i.e., silica-gels, activated carbons (powder and fiber), polymer sorbents, and metal organic frameworks have also been summarized in this study along with different desiccant air-conditioning options. However, different high-performance zeolites and molecular sieves are extensively studied in literature. The authors conclude that solar operated desicca...
Sustainability
As the world human population and industrialization keep growing, the water availability issue ha... more 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...
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Journal Papers by Dr. Redmond R . Shamshiri
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1. To design and develop and test a modified shaking mechanism for the available Blueberry harvesting machine.
2. To develop and install a yield monitoring system for the modified machine and collect yield data.
3. To determine machine performance parameters (i.e., Field efficiency, field machine index, etc ) by collecting and processing yield data.
Space Exploration Asia Sdn Bhd (Malaysia) has partnered with Adaptive AgroTech Research Group International, and in collaboration with the Massey University of New Zealand and the New Zealand Center for Precision Agriculture, to develop an exciting and intensive eight-day Study Tour to New Zealand from the 23rd October to 31st October 2017 (limited to 20 participants). The Study Tour is supported and endorsed by the Precision Agricultural association of New Zealand, as well as the Smart Farming Technology Research Center of the University Putra Malaysia.
The study tour is designed to demonstrate recent advances in precision agriculture, digital farming, and Instrumentation, with the objectives to give participants a hands-on experience and to gain insights on technology transfer through a dynamic tour of farms and facilities located in both in the North and South Island of New Zealand. The study tour offers field visits and technology demonstrations by leading farm companies and cooperatives, including visiting the world wheat yield record holder and the PrecisionAg Farmer of the Year for 2016 to showcase how technologies can be applied to increase yields and how these technologies can be customized to Malaysian agriculture productions, including oil-palm, cocoa, rubber and paddy. It also includes a study on the integration of ICT, instrumentation and ag-robotics, and how digital agriculture can dramatically reduce farm inputs and contribute to higher yield and efficiency.
The Study Tour will be facilitated by the New Zealand Centre for Precision Agriculture under the Directorship of Professor Ian Yul, who is a world-recognized expert in this field and is presently the President-Elect of the International Society of Precision Agriculture and will take over as President in June 2018. Field visits will be guided by Dr. Redmond Ramin Shamshiri, international robotic and plantation expert (on oil palm), who will also support the participants during the tour on how these technologies and solutions are used and customized for Malaysian plantations and agriculture.