Massey Research Online - Browsing by Author "Arif K"

Browsing by Author "Arif K"

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A Performance-Optimized Deep Learning-based Plant Disease Detection Approach for Horticultural Crops of New Zealand
(Institute of Electrical and Electronics Engineers, 23/08/2022) Saleem MH; Potgieter J; Arif K
Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After finding the most suitable DL model, the data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.
A weight optimization-based transfer learning approach for plant disease detection of New Zealand vegetables
(Frontiers Media, 25/10/2022) Saleem MH; Potgieter J; Arif K
Deep learning (DL) is an effective approach to identifying plant diseases. Among several DL-based techniques, transfer learning (TL) produces significant results in terms of improved accuracy. However, the usefulness of TL has not yet been explored using weights optimized from agricultural datasets. Furthermore, the detection of plant diseases in different organs of various vegetables has not yet been performed using a trained/optimized DL model. Moreover, the presence/detection of multiple diseases in vegetable organs has not yet been investigated. To address these research gaps, a new dataset named NZDLPlantDisease-v2 has been collected for New Zealand vegetables. The dataset includes 28 healthy and defective organs of beans, broccoli, cabbage, cauliflower, kumara, peas, potato, and tomato. This paper presents a transfer learning method that optimizes weights obtained through agricultural datasets for better outcomes in plant disease identification. First, several DL architectures are compared to obtain the best-suited model, and then, data augmentation techniques are applied. The Faster Region-based Convolutional Neural Network (RCNN) Inception ResNet-v2 attained the highest mean average precision (mAP) compared to the other DL models including different versions of Faster RCNN, Single-Shot Multibox Detector (SSD), Region-based Fully Convolutional Networks (RFCN), RetinaNet, and EfficientDet. Next, weight optimization is performed on datasets including PlantVillage, NZDLPlantDisease-v1, and DeepWeeds using image resizers, interpolators, initializers, batch normalization, and DL optimizers. Updated/optimized weights are then used to retrain the Faster RCNN Inception ResNet-v2 model on the proposed dataset. Finally, the results are compared with the model trained/optimized using a large dataset, such as Common Objects in Context (COCO). The final mAP improves by 9.25% and is found to be 91.33%. Moreover, the robustness of the methodology is demonstrated by testing the final model on an external dataset and using the stratified k-fold cross-validation method.
AI-Based Controls for Thermal Comfort in Adaptable Buildings: A Review
(MDPI AG, 2024-11-04) Ahsan M; Shahzad W; Arif K
Due to global weather changes and pandemics, people are more likely to spend most of their time in indoor environments. In this regard, indoor environment quality is a very important aspect of occupant well-being, which is often ignored in modern building designs. Based on our research, thermal comfort is one of the essential items in building environments that can improve the mental stability and productivity of the occupants if the building’s indoor environment is created in a way that meets the occupants’ comfort requirements. Buildings nowadays operate on adaptive or stationary models to attain thermal comfort, which is based on Fanger’s model of the Predicted Mean Vote (PMV). Based on the literature review, limited work has been carried out to enhance the quality of the inside environment, and most research work has been devoted to building energy management. Moreover, there have been no definite solutions so far that have the capability to detect the thermal comfort requirements of multiple occupants in real time. Modern buildings tend to operate on predefined set point parameters to control the indoor environment based on the measured room temperature, which can be different from the thermal comfort requirements of the occupants. This paper discusses the limitations and assumptions that are associated with the existing thermal comfort solutions and emphasises the importance of having a real-time solution to address the thermal requirements of occupants.
All-Dielectric Transreflective Angle-Insensitive Near-Infrared (NIR) Filter
(MDPI (Basel, Switzerland), 2022-08) Shaukat A; Umer R; Noble F; Arif K
This paper presents an all-dielectric, cascaded, multilayered, thin-film filter, allowing near-infrared filtration for spectral imaging applications. The proposed design is comprised of only eight layers of amorphous silicon (A-Si) and silicon nitride (Si₃N₄), successively deposited on a glass substrate. The finite difference time domain (FDTD) simulation results demonstrate a distinct peak in the near-infrared (NIR) region with transmission efficiency up to 70% and a full-width-at-half-maximum (FWHM) of 77 nm. The theoretical results are angle-insensitive up to 60⁰ and show polarization insensitivity in the transverse magnetic (TM) and transverse electric (TE) modes. The theoretical response, obtained with the help of spectroscopic ellipsometry (SE), is in good agreement with the experimental result. Likewise, the experimental results for polarization insensitivity and angle invariance of the thin films are in unison with the theoretical results, having an angle invariance up to 50⁰.
Electrochemical and Optical Sensors for Real-Time Detection of Nitrate in Water
(MDPI, 2023-08-11) Lal K; Jaywant S; Arif K
The health and integrity of our water sources are vital for the existence of all forms of life. However, with the growth in population and anthropogenic activities, the quality of water is being impacted globally, particularly due to a widespread problem of nitrate contamination that poses numerous health risks. To address this issue, investigations into various detection methods for the development of in situ real-time monitoring devices have attracted the attention of many researchers. Among the most prominent detection methods are chromatography, colorimetry, electrochemistry, and spectroscopy. While all these methods have their pros and cons, electrochemical and optical methods have emerged as robust and efficient techniques that offer cost-effective, accurate, sensitive, and reliable measurements. This review provides an overview of techniques that are ideal for field-deployable nitrate sensing applications, with an emphasis on electrochemical and optical detection methods. It discusses the underlying principles, recent advances, and various measurement techniques. Additionally, the review explores the current developments in real-time nitrate sensors and discusses the challenges of real-time implementation.
Hybrid deposition additive manufacturing: novel volume distribution, thermo-mechanical characterization, and image analysis
(The Brazilian Society of Mechanical Sciences and Engineering, 25/08/2022) Harris M; Mohsin H; Potgieter J-G; Arif K; Anwar S; AlFaify A; Farooq MU
The structural integrity of additive manufacturing structures is a pronounced challenge considering the voids and weak layer-to-layer adhesion. One of the potential ways is hybrid deposition manufacturing (HDM) that includes fused filament fabrication (FFF) with the conventional filling process, also known as “HDM composites". HDM is a potential technique for improving structural stability by replacing the thermoplastic void structure with a voidless epoxy. However, the literature lacks investigation of FFF/epoxy HDM-based composites regarding optimal volume distribution, effects of brittle and ductile FFF materials, and fractographic analysis. This research presents the effects of range of volume distributions (10–90%) between FFF and epoxy system for tensile, flexure, and compressive characterization. Volume distribution in tensile and flexure samples is achieved using printable wall thickness, slot width, and maximum width. For compression, the printable wall thickness, slot diameter, and external diameter are considered. Polylactic acid and acrylonitrile butadiene styrene are used to analyze the brittle and ductile FFF structures. The research reports novel application of image analysis during mechanical characterization using high-quality camera and fractographic analysis using scanning electron microscopy (SEM). The results present surprising high tensile strain (0.038 mm/mm) and compressive strength (64.5 MPa) for lower FDM-percentages (10%, 20%) that are explained using in situ image analysis, SEM, stress–strain simulations, and dynamic mechanical analysis (DMA). In this regard, the proposed work holds novelty to apply DMA for HDM. The optimal volume distributions of 70% and 80% alongside fractographic mechanisms for lower percentages (10%, 20%) can potentially contribute to structural applications and future material-based innovations for HDM.
Laser ablation assisted micropattern screen printed transduction electrodes for sensing applications
(Nature Publishing Group, 28/04/2022) Rehmani MAA; Lal K; Shaukat A; Arif K
In this work we present a facile method for the fabrication of several capacitive transduction electrodes for sensing applications. To prepare the electrodes, line widths up to 300 μm were produced on polymethyl methacrylate (PMMA) substrate using a common workshop laser engraving machine. The geometries prepared with the laser ablation process were characterised by optical microscopy for consistency and accuracy. Later, the geometries were coated with functional polymer porous cellulose decorated sensing layer for humidity sensing. The resulting sensors were tested at various relative humidity (RH) levels. In general, good sensing response was produced by the sensors with sensitivities ranging from 0.13 to 2.37 pF/%RH. In ambient conditions the response time of 10 s was noticed for all the fabricated sensors. Moreover, experimental results show that the sensitivity of the fabricated sensors depends highly on the geometry and by changing the electrode geometry sensitivity increases up to 5 times can be achieved with the same sensing layer. The simplicity of the fabrication process and higher sensitivity resulting from the electrode designs is expected to enable the application of the proposed electrodes not only in air quality sensors but also in many other areas such as touch or tactile sensors.
Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network
(MDPI AG, 11/01/2023) Ali S; Alam F; Arif K; Potgieter J-G
The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
Low-cost IoT based system for lake water quality monitoring
(Public Library of Science (PLoS), 2024-03-28) Lal K; Menon S; Noble F; Arif K
Water quality monitoring is a critical process in maintaining the well-being of aquatic ecosystems and ensuring growth of the surrounding environment. Clean water supports and maintains the health, livelihoods, and ecological balance of the ecosystem as a whole. Regular assessment of water quality is essential to ensure clean and reliable water is available to everyone. This requires regular measurement of pollutants or contaminants in water that can be monitored in real-time. Hence, this research showcases a system that consists of low-cost sensors used to measure five basic parameters of water quality that are: turbidity, total dissolved solids, temperature, pH, and dissolved oxygen. The system incorporates electronics and IoT technology that are powered by a solar charged lead acid battery. The data gathered from the sensors was stored locally on a micro-SD card with live updates that could be viewed on a mobile device when in proximity to the system. Data was gathered from three different bodies of water over a span of three weeks, precisely during the seasonal transition from autumn to winter. We adopted a water sampling technique since our low-cost sensors were not designed for continuous submersion. The results show that the temperature drops gradually during this period and an inversely proportional relationship between pH and temperature could be observed. The concentration of total dissolved solids decreased during rainy periods with a variation in turbidity. The deployed system was robust and autonomous that effectively monitored the quality of water in real-time with scope of adding more sensors and employing Industry 4.0 paradigm to predict variations in water quality.
Low-Cost Sensor for Continuous Measurement of Brix in Liquids
(MDPI AG, 25/11/2022) Jaywant SA; Singh H; Arif K
This paper presents a Brix sensor based on the differential pressure measurement principle. Two piezoresistive silicon pressure sensors were applied to measure the specific gravity of the liquid, which was used to calculate the Brix level. The pressure sensors were mounted inside custom-built water-tight housings connected together by fixed length metallic tubes containing the power and signal cables. Two designs of the sensor were prepared; one for the basic laboratory testing and validation of the proposed system and the other for a fermentation experiment. For lab tests, a sugar solution with different Brix levels was used and readings from the proposed sensor were compared with a commercially available hydrometer called Tilt. During the fermentation experiments, fermentation was carried out in a 1000 L tank over 7 days and data was recorded and analysed. In the lab experiments, a good linear relationship between the sugar content and the corresponding Brix levels was observed. In the fermentation experiment, the sensor performed as expected but some problems such as residue build up were encountered. Overall, the proposed sensing solution carries a great potential for continuous monitoring of the Brix level in liquids. Due to the usage of low-cost pressure sensors and the interface electronics, the cost of the system is considered suitable for large scale deployment at wineries or juice processing industries.
Methamphetamine detection using nanoparticle-based biosensors: A comprehensive review
(Elsevier BV, 2022-12) Lal K; Noble F; Arif K
Drug abuse is a global issue, requiring diverse techniques for recognition of drug of interest. One such illicit drug that is abused worldwide is Methamphetamine (METH). It is an addictive and illicit substance that severely affects the central nervous system. Similar to many other illicit substances, recognition of METH in biological fluids and in more diverse matrices such as wastewater, is a topic of great interest to the government and law enforcement agencies. With the rise of nanotechnology that relies on exploiting the properties of certain materials at a scale down to their nanometer range in conjunction with aptamers, molecularly imprinted polymers as well as antibodies have gained much attention over the last decade. The scope and appositeness of nanomaterials have significant characteristics that are highly suitable for recognition of illicit chemical compounds such as METH. This comprehensive review focuses on the detection of METH using nanoparticles in real world samples such as biological fluids and wastewater, while discussing varieties of materials used as nanoparticles and that aid in its recognition. It also offers insights into future opportunities and challenges that come with the use of nanotechnology in sensing applications.
Partial Biodegradable Blend for Fused Filament Fabrication: In-Process Thermal and Post-Printing Moisture Resistance
(MDPI AG, 9/04/2022) Harris M; Mohsin H; Naveed R; Potgieter J-G; Ishfaq K; Ray S; Guen M-JL; Archer R; Arif K
Despite the extensive research, the moisture-based degradation of the 3D-printed polypropylene and polylactic acid blend is not yet reported. This research is a part of study reported on partial biodegradable blends proposed for large-scale additive manufacturing applications. However, the previous work does not provide information about the stability of the proposed blend system against moisture-based degradation. Therefore, this research presents a combination of excessive physical interlocking and minimum chemical grafting in a partial biodegradable blend to achieve stability against in-process thermal and moisture-based degradation. In this regard, a blend of polylactic acid and polypropylene compatibilized with polyethylene graft maleic anhydride is presented for fused filament fabrication. The research implements, for the first time, an ANOVA for combined thermal and moisture-based degradation. The results are explained using thermochemical and microscopic techniques. Scanning electron microscopy is used for analyzing the printed blend. Fourier transform infrared spectroscopy has allowed studying the intermolecular interactions due to the partial blending and degradation mechanism. Differential scanning calorimetry analyzes the blending (physical interlocking or chemical grafting) and thermochemical effects of the degradation mechanism. The thermogravimetric analysis further validates the physical interlocking and chemical grafting. The novel concept of partial blending with excessive interlocking reports high mechanical stability against moisture-based degradation.
Partial Biodegradable Blend with High Stability against Biodegradation for Fused Deposition Modeling
(MDPI AG, 11/04/2022) Harris M; Mohsin H; Potgieter J; Ishfaq K; Archer R; Chen Q; De silva K; Guen M-JL; Wilson R; Arif K
This research presents a partial biodegradable polymeric blend aimed for large-scale fused deposition modeling (FDM). The literature reports partial biodegradable blends with high contents of fossil fuel-based polymers (>20%) that make them unfriendly to the ecosystem. Furthermore, the reported polymer systems neither present good mechanical strength nor have been investigated in vulnerable environments that results in biodegradation. This research, as a continuity of previous work, presents the stability against biodegradability of a partial biodegradable blend prepared with polylactic acid (PLA) and polypropylene (PP). The blend is designed with intended excess physical interlocking and sufficient chemical grafting, which has only been investigated for thermal and hydrolytic degradation before by the same authors. The research presents, for the first time, ANOVA analysis for the statistical evaluation of endurance against biodegradability. The statistical results are complemented with thermochemical and visual analysis. Fourier transform infrared spectroscopy (FTIR) determines the signs of intermolecular interactions that are further confirmed by differential scanning calorimetry (DSC). The thermochemical interactions observed in FTIR and DSC are validated with thermogravimetric analysis (TGA). Scanning electron microscopy (SEM) is also used as a visual technique to affirm the physical interlocking. It is concluded that the blend exhibits high stability against soil biodegradation in terms of high mechanical strength and high mass retention percentage.
Partial Polymer Blend for Fused Filament Fabrication with High Thermal Stability
(MDPI AG, 30/09/2021) Harris M; Potgieter J; Mohsin H; Ray S; Chen JQ; Arif K
The materials for large scale fused filament fabrication (FFF) are not yet designed to resist thermal degradation. This research presents a novel polymer blend of polylactic acid with polypropylene for FFF, purposefully designed with minimum feasible chemical grafting and overwhelming physical interlocking to sustain thermal degradation. Multi-level general full factorial ANOVA is performed for the analysis of thermal effects. The statistical results are further investigated and validated using different thermo-chemical and visual techniques. For example, Fourier transform infrared spectroscopy (FTIR) analyzes the effects of blending and degradation on intermolecular interactions. Differential scanning calorimetry (DSC) investigates the nature of blending (grafting or interlocking) and effects of degradation on thermal properties. Thermogravimetric analysis (TGA) validates the extent of chemical grafting and physical interlocking detected in FTIR and DSC. Scanning electron microscopy (SEM) is used to analyze the morphology and phase separation. The novel approach of overwhelmed physical interlocking and minimum chemical grafting for manufacturing 3D printing blends results in high structural stability (mechanical and intermolecular) against thermal degradation as compared to neat PLA.
Weed Identification by Single-Stage and Two-Stage Neural Networks: A Study on the Impact of Image Resizers and Weights Optimization Algorithms
(Frontiers Media, 25/04/2022) Saleem MH; Velayudhan KK; Potgieter J; Arif K
The accurate identification of weeds is an essential step for a site-specific weed management system. In recent years, deep learning (DL) has got rapid advancements to perform complex agricultural tasks. The previous studies emphasized the evaluation of advanced training techniques or modifying the well-known DL models to improve the overall accuracy. In contrast, this research attempted to improve the mean average precision (mAP) for the detection and classification of eight classes of weeds by proposing a novel DL-based methodology. First, a comprehensive analysis of single-stage and two-stage neural networks including Single-shot MultiBox Detector (SSD), You look only Once (YOLO-v4), EfficientDet, CenterNet, RetinaNet, Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Network (RFCN), has been performed. Next, the effects of image resizing techniques along with four image interpolation methods have been studied. It led to the final stage of the research through optimization of the weights of the best-acquired model by initialization techniques, batch normalization, and DL optimization algorithms. The effectiveness of the proposed work is proven due to a high mAP of 93.44% and validated by the stratified k-fold cross-validation technique. It was 5.8% improved as compared to the results obtained by the default settings of the best-suited DL architecture (Faster RCNN ResNet-101). The presented pipeline would be a baseline study for the research community to explore several tasks such as real-time detection and reducing the computation/training time. All the relevant data including the annotated dataset, configuration files, and inference graph of the final model are provided with this article. Furthermore, the selection of the DeepWeeds dataset shows the robustness/practicality of the study because it contains images collected in a real/complex agricultural environment. Therefore, this research would be a considerable step toward an efficient and automatic weed control system.

Browsing by Author "Arif K"

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