ICITR - 2023

Permanent URI for this collectionhttp://192.248.9.226/handle/123/22075

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item: Conference-Full-text
8th International Conference in Information Technology Research 2023 (Per Text)
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
item: Conference-Full-text
Accelerated adversarial attack generation and enhanced decision insight
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Kumarasiri, NKYS; Premaratne, SC; Wijesuriya, WMRM; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Adversarial Attack is a rapidly growing field that studies how intentionally crafted inputs can fool machine learning models. This can have severe implications for the security of machine learning systems, as it can allow attackers to bypass security measures and cause the system to malfunction. Finding solutions for these attacks involves creating specific attack scenarios using a particular dataset and training a model based on that dataset. Adversarial attacks on a trained model can significantly reduce accuracy by manipulating the decision boundary, causing instances initially classified correctly to be misclassified. This alteration results in a notable decline in the model's ability to classify instances after an attack accurately. The above process helps us develop strategies to defend against these attacks. However, a significant challenge arises because generating these attack scenarios for a specific dataset is time-consuming. Moreover, the disparity between the model's prediction outcomes before and after the attack tends to lack clear interpretability. In both above limitations, the common limiting factor is time. The time it takes to devise a solution is crucial because the longer it takes, the more opportunity an attacker has to cause harm in real-world situations. In this paper, we propose two approaches to address the above gaps: minimizing the time required for attack generation using data augmentation and understanding the effects of an attack on the model's decision-making process by generating more interpretable descriptions. We show that description can be used to gain insights into how an attack affects the model's decision-making process by identifying the most critical features for the model's prediction before and after the attack. Our work can potentially improve the security of machine learning systems by making it more difficult for attackers to generate effective attacks.
item: Conference-Full-text
Acoustic signature analysis for distinguishing human vs. synthetic voices in vishing attacks
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Gamage, P; Dissanayake, D; Kumarasinghe, N; Ganegoda, GU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Cybercrimes targeting mobile devices are on the rise, with vishing and smishing attacks being particularly prevalent. These attacks exploit social engineering techniques to manipulate individuals into divulging personal information or engaging in unintended actions. To counter this evolving threat landscape, this research proposes a pioneering methodology rooted in voice feature analysis. By distinguishing between human and robotic voices, this approach aims to discern legitimate calls from potential scams, thereby mitigating the associated financial losses and reputational damage. The research delves into the intricacies of voice feature analysis, leveraging natural language processing (NLP) and machine learning (ML) techniques to extract and analyze audio attributes such as pitch, volume, and temporal patterns. The ultimate objective is to create a binary classification model that accurately differentiates between human voice calls and robocalls, incorporating a comprehensive dataset comprising actual call recordings and synthesized scenarios. This research advances beyond conventional practices by championing a holistic analysis of both human and robocalls, contrary to the prevalent assumption of robocalls exclusively constituting scams. The application of various audio features, coupled with nuanced weightage allocation, enhances the model’s discernment capabilities. The resultant binary classifier is an exemplar of the innovative fusion of technology and human expertise. In conclusion, this research introduces a novel dimension to the combat against vishing and smishing attacks, with a robust voice feature analysis methodology capable of accurately identifying human and robotic voices. By effectively distinguishing legitimate calls from potential threats, this approach presents a promising avenue for safeguarding individuals and organizations against the far-reaching consequences of cybercrimes. The comprehensive analysis, validation, and insights presented in this paper contribute significantly to the field of cybersecurity and voicebased communication analysis.
item: Conference-Full-text
An agile project management supporting approach for estimating story points in user stories
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Wanigasooriya Arachchi, KJ; Amalraj, CRJ; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
While significant research has been conducted on software analytics for effort estimation in traditional software projects, limited attention has been given to estimation in agile projects, particularly in estimating the effort required for completing user stories. In our study, we present a novel prediction model for estimating story points, which serves as a common unit of measure for gauging the effort involved in completing a user story or resolving an issue. To achieve this, we propose a unique combination of two powerful deep learning architectures, namely LSTM and RHN. What sets our prediction system apart is its end-to-end training capability, allowing it to learn directly from raw input data without relying on manual feature engineering. To support our research, we have curated a comprehensive dataset specifically tailored for story points-based estimation. This dataset comprises 6801 issues extracted from 6 different open-source projects. Through an empirical evaluation, we demonstrate the superiority of our approach over three common baselines. In summary, our study addresses the gap in research regarding agile project estimation by introducing a prediction model that effectively estimates story points. By leveraging the combined power of LSTM and RHN architectures.
item: Conference-Full-text
Ai-driven user experience design: exploring innovations and challenges in delivering tailored user experiences
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Padmasiri, P; Kalutharage, P; Jayawardhane, N; Wickramarathne, J; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
In today’s digital landscape, providing user experiences is considered paramount in respective of user satisfaction and engagement. Artificial Intelligence (AI) has emerged as a transformative force in the User Experience (UX) design field, offering innovative solutions. Our research delves into key innovations and challenges enabled by AI in UX design particularly guided by Design Thinking (DT) process. The methodology involved administering a questionnaire to UX professionals in Sri Lanka using a snowball sampling method. The questionnaire, distributed through online platforms, explored participants’ familiarity with AI-driven UX design, contributions of AI in the DT process, and challenges faced, and the responses were analyzed using MS Excel and R Studio. The results demonstrate that AI technologies certainly empower UX professionals to design usercentric solutions adhering to DT process. A “Recommendation Guide” is provided, featuring a set of recommended tools for UX professionals to integrate AI technologies into the DT process.
item: Conference-Full-text
Alzheimer’s disease detection using blood gene expression data
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Yasodya, GDS; Ganegoda, GU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Alzheimer's disease is the most prevalent form of dementia with no established cure. Extensive research aims to comprehend its underlying mechanisms. Genetic insights are sought through gene expression data analysis, leveraging computational and statistical techniques to identify risk-associated genes. This study focuses on accurate AD detection using blood gene expression data. Four feature classification methods—TFrelated genes, Hub genes, CFG, and VAE are employed to identify crucial AD-related genes. Five classification approaches—RF, SVM, LR, L1-LR, and DNN—are used, evaluated by AUC. The VAE + LR model yields the highest AUC (0.76). The study identifies 100 influential AD-associated genes where data is sourced from Alzheimer's Disease Neuroimaging Initiative (ADNI). Findings hold promise for advancing early diagnosis and treatment, enhancing AD patients' quality of life.
item: Conference-Full-text
Alzheimer’s disease prediction using clinical data approach
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Perera, LRD; Ganegoda, GU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Alzheimer's Disease (AD) is a progressive neurodegenerative condition that profoundly affects cognition and memory. Due to the absence of curative treatments, early detection and prediction are crucial for effective intervention. This study employs machine learning and clinical data from Alzheimer's Disease Neuroimaging Initiative (ADNI) to predict AD onset. Data preprocessing ensures quality through variable selection and feature extraction. Diverse machine learning algorithms, including Naive Bayes, logistic regression, SVM-Linear, random forest, Gradient Boosting, and Decision Trees, are evaluated for prediction accuracy. The model resulted with random forest classifier together with filter method yields the highest AUC. The study highlights important analysis using Random Forest and Decision Trees, revealing significant variables including cognitive tests, clinical scales, demographics, brain-related metrics, and key biomarkers. By enhancing predictive capabilities, this research contributes to advancing Alzheimer's disease diagnosis and intervention strategies.
item: Conference-Full-text
The application of convolutional neural network in the context of Tamil handwritten character recognition
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Thuvarakan, P; Kowreesan, P; Jeyarooban, S; Janotheepan, M; Ekanayake, EMUWJB; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
This research paper presents an in-depth investigation into the application of Convolutional Neural Networks (CNNs) for Tamil handwritten character recognition. We explore existing research, methodologies, and cutting-edge techniques, showcasing CNNs' effectiveness in achieving a remarkable 95% accuracy. Our dataset comprises 247 Tamil characters and 18 North Indian characters, accommodating diverse writing styles. We tailor CNN architectures for Tamil characters, implement advanced preprocessing, data augmentation, and training methods to enhance model performance. Our paper tackles challenges posed by accessible datasets, offering remedies for data scarcity, class imbalance, and writing style variations. Our distinct contribution lies in achieving 95% accuracy across 247 Tamil characters and 18 North Indian characters, demonstrating CNNs' potential for document processing, language preservation, and automation in Tamil-speaking regions. This work advances the field by introducing novel techniques, a comprehensive dataset, and strategic insights, serving as a significant step forward in Tamil character recognition.
item: Conference-Full-text
Blockchain-based software subscription and licenses management system
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) De Alwis, H; Wijayasiri, A; De Silva, S; De Silva, K; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Current software licensing models exhibit shortcomings in transparency, security, and adaptability. Addressing these challenges, this study presents a novel blockchain-based licensing system using the Ethereum platform. By employing smart contracts and the ERC721 and ERC20 token standards, the system ensures automated, transparent, and secure license agreement enforcement and facilitates license token operations. Influenced by the rise of subscription licenses and the implications of the UsedSoft court decision, the research designs a blockchain-driven subscription license model, analyses the UsedSoft case’s impact on license transfers, and formulates specialized smart contracts for varied licensing models. The approach signifies a marked advancement in contemporary software licensing practices.
item: Conference-Full-text
Classification of fungi images using different convolutional neural networks
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Nawarathne, UMMPK; Kumari, HMNS; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Fungi offer vital solutions to humanity through roles in medicine, agriculture, and ecological balance while presenting potential threats. They have yielded antibiotics, food fermentation, and nutrient recycling however, fungal infections, crop diseases, and spoilage highlight their dark side. Therefore, it is important to identify fungi to harness their potential benefits and mitigate threats. Offering quick and accurate identification through image classification improves the aforementioned features. Therefore, this study classified images of five types of fungi using convolutional neural networks (CNN). Initially, dataset distribution was observed, and it was identified that there was a class imbalance in the dataset. To address this issue, data augmentation technique was used. Several preprocessing techniques were also applied to understand the model training behavior with their application. Then the images were rescaled into six different resolution combinations such as original images, low-resolution images, high-resolution images, a mix of original and low-resolution images, a mix of original and high-resolution images, and a mix of low and high-resolution images. Then these data were trained using 13 pre-trained CNN models such as Xception, VGG16, VGG19, InceptionResNetV2, ResNet152, EfficientNetB6, EfficientNetB7, ConvNeXtTiny, ConvNeXtSmall, ConvNeXtBase, ConvNeXtLarge, ConvNeXtXLarge, BigTransfer (BiT). To evaluate these models, accuracy, macro average precision, macro average recall, macro average f1- score, and loss learning curve assessment were used. According to the results, the BiT model preprocessed with normalization, which used a mix of original and high-resolution images, performed the best, producing a model accuracy of 87.32% with optimal precision, recall, and f1-score. The loss learning curve of the BiT model also depicted a low overfitting aspect proving the model’s optimal behavior. Therefore, it was concluded that the BiT model with the mix of original and high-resolution data can be used to detect fungi efficiently.
item: Conference-Full-text
Cloud-based weather condition monitoring system using esp8266 and amazon web services
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Mohamed, A; Gunasegaran, G; Herath, D; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
In the recent years, there has been a lot of interest in global climate change. People want to be aware of the most recent weather conditions in their vicinity and immediate surroundings. This study presents a prototype of an Internet of Things-based system that uses sensors to monitor weather conditions. Three separate sensors, including an ultrasonic sensor, a raindrop sensor, and a pressure sensor, were used. The microcontroller board served as the system's brain. The sensors continuously collect weather data and transmit it through Wi-Fi to a remote server. The weather data is moved to a cloud platform, which gives real-time weather informatics reporting on a website after that. The platform employed in this study was Amazon Web Services (AWS) IoT, and Amazon DynamoDB was used to store sensor data. The IoT presents a novel perspective on future environmental monitoring. Consequently, the role of the Internet of Things in the developing field of environmental informatics is also covered in this study.
item: Conference-Full-text
Cross-vit: cross-attention vision transformer for image duplicate detection
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Chandrasiri, MDN; Talagala, PD; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Duplicate detection in image databases has immense significance across diverse domains. Its utility transcends specific applications, adapting seamlessly to a range of use cases, either as a standalone process or an integrated component within broader workflows. This study explores cutting-edge vision transformer architecture to revolutionize feature extraction in the context of duplicate image identification. Our proposed framework combines the conventional transformer architecture with a groundbreaking cross-attention layer developed specifically for this study. This unique cross-attention transformer processes pairs of images as input, enabling intricate cross-attention operations that delve into the interconnections and relationships between the distinct features in the two images. Through meticulous iterations of Cross-ViT, we assess the ranking capabilities of each version, highlighting the vital role played by the integrated cross-attention layer between transformer blocks. Our research culminates in recommending a final optimal model that capitalizes on the synergies between higher-dimensional hidden embeddings and mid-size ViT variations, thereby optimizing image pair ranking. In conclusion, this study unveils the immense potential of the vision transformer and its novel cross-attention layer in the domain of duplicate image detection. The performance of the proposed framework was assessed through a comprehensive comparative evaluation against baseline CNN models using various benchmark datasets. This evaluation further underscores the transformative power of our approach. Notably, our innovation in this study lies not in the introduction of new feature extraction methods but in the introduction of a novel cross-attention layer between transformer blocks grounded in the scaled dot-product attention mechanism.
item: Conference-Full-text
Detecting tabnabbing attacks via an rl-based agent
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Fonseka, A; Pashenna, P; Ariyadasa, SN; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Tabnabbing attacks exploit user behavior in web browsers, deceiving users by altering content in inactive tabs to appear legitimate, leading to data disclosure or unintended actions. This research evaluates the effectiveness of Reinforcement Learning (RL) in detecting Tabnabbing attacks at the web browser level, presenting a proactive defense mechanism against this cyber threat. The study began with a literature review to find the top 5 critical features of Tabnabbing attacks and were extracted using a publicly available dataset from "Phishpedia". Data preprocessing is conducted to handle missing and incorrect data, resulting in a refined dataset. The RL agent is designed using the Deep QNetwork (DQN) algorithm, which effectively handles highdimensional state spaces. The evaluation of the RL agent demonstrates promising results. However, there is room for improvement requiring further research and model tuning.
item: Conference-Full-text
Dominant color palette extraction in resumes using the new color pixel quantifier algorithm
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Perera, NN; Warusawithana, SP; Weerasinghe, RL; Hindakaraldeniya, TM; Ganegoda, GU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
In the realm of resume analysis and enhancement, the extraction of dominant color palettes plays a pivotal role in assessing the visual impact of resumes. Existing methods designed for images with extensive color ranges have proven to be suboptimal when applied to the distinct context of resumes, which inherently possess a limited color palette. This paper introduces a novel approach that addresses this challenge effectively and efficiently. By minimizing the time required for palette extraction without compromising accuracy, the proposed method offers a practical solution for resume feedback systems. It is important to clarify that this research neither rejects nor supports existing methods; instead, it presents an alternative, tailor-made solution for resume analysis. In summary, this paper sets a promising precedent for more streamlined and functional dominant color palette extraction methods in the context of resumes, promising advancements in resume analysis and improvement.
item: Conference-Full-text
Early identification of deforestation using anomaly detection
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Wijesinghe, N; Perera, R; Sellahewa, N; Talagala, PD; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Research involving anomaly detection in image streams has seen growth through the years, given the proliferation of high-quality image data in various applications. One such application that is in urgent need of attention is deforestation. Detecting anomalies in this context, however, remains challenging due to the irregular and low-probability nature of deforestation events. This study introduces two anomaly detection frameworks utilizing machine learning and deep learning for the early detection of deforestation activities in image streams. Furthermore, Explainable AI was used to explain the black box models of the deep learning-based anomaly detection framework. The class imbalance problem, the inter-dependency between the images with time, the lack of available labelled images, a datadriven anomalous threshold, and the trade-off of accuracy while increasing interpretability in the black box optimization methods are some key aspects considered in the model-building process. Our novel framework for anomaly detection in image streams underwent rigorous evaluation using a range of datasets that included synthetic and real-world data, notably datasets related to Amazon’s forest coverage. The objective of this evaluation was to detect occurrences of deforestation in the Amazon. Several metrics were used to evaluate the performance of the proposed framework.
item: Conference-Full-text
Enhanced timetable scheduling: a high-performance computational approach
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Sovis, A; Patikirige, C; Pandigama, Y; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Timetable scheduling is a complicated, expensive and resource-intensive Optimization Problem. This project aims to suggest a solution to this problem using multiple strategies. The core strategy is to use Artificial Intelligence and Machine Learning to optimize a timetable. The result is optimized further by reapplying this optimization mechanism iteratively without aiming to build a perfect result in a single iteration. The project uses the concepts of High-Performance Computing and Cluster Computing to provide flexibility and efficiency on a hardware level. These form the basis of Project Almanac: a robust and flexible timetable optimization architecture. Project Almanac aims to generate a ‘good enough’ timetable by adjusting the expenses according to the end-user requirements. Alternatively, the solution also intends to offer a faster, cheaper and more flexible hardware-software architecture to generate optimized timetables for diverse applications.
item: Conference-Full-text
Enhancing ddos attack detection via blending ensemble learning
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Amalraj, CRJ; Madhusankha, PGG; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
This research focuses on identifying DDoS attacks using an ensemble learning approach that incorporates blending techniques. We developed an innovative methodology by selecting the 21 most significant features from the CIC-DDoS2019 dataset. To improve classification accuracy, we used a two-layer blending ensemble technique. In the first layer, we combined Decision Tree, Logistic Regression, and KNN classifiers, while the second layer used a Random Forest classifier. The model achieved exceptional results, with a 99.94% accuracy score and a 97.35% F1 score for detecting DDoS attacks accurately. We also created a user-friendly web portal to make the model accessible for individuals in network security, regardless of their technical expertise. This approach advances DDoS attack detection and enhances usability for users in the field of network security.
item: Conference-Full-text
Explainable ai techniques for deep convolutional neural network based plant disease identification
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Kiriella, S; Fernando, S; Sumathipala, S; Udayakumara, EPN; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Deep learning-based computer vision has shown improved performance in image classification tasks. Due to the complexities of these models, they have been referred as opaque models. As a result, users need justifications for predictions to enhance trust. Thus, Explainable Artificial Intelligence (XAI) provides various techniques to explain predictions. Explanations play a vital role in practical application, to apply the exact treatment for a plant disease. However, application of XAI techniques in plant disease identification is not popular. This paper discusses the key concerns and taxonomies available in XAI and summarizes the recent developments. Also, it develops a tomato disease classification model and uses different XAI techniques to validate model predictions. It includes a comparative analysis of XAI techniques and discusses the limitations and usefulness of the techniques in plant disease symptom localization.
item: Conference-Full-text
Game-based analytical skills testing for graduate software engineering recruitment
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Dasanayake, DWMNC; Sandanayake, TC; Premasiri, SMU; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Game-based recruitment is an emerging trend adopted by organizations globally, given its proven results in boosting candidate perceptions of the company and providing an improved recruitment experience. This paper explores the use of game-based analytical skill testing in the recruitment process of entry-level graduate software engineers in Sri Lanka. The Test of Logical Thinking by Tobin and Capie has been used as a reference, and a game-based version has been developed using the MDA framework, relying on mechanics, dynamics, and aesthetics. The testing phase has been carried out using a focus group of eight fresh graduate software engineering recruits, and the results have depicted a significantly high level of accuracy between the results produced through the paper-based and gamebased versions. Candidate perceptions of the recruitment process and the employer have been recorded to be positively influenced by the introduction of game-based testing in the recruitment process
item: Conference-Full-text
Green insight: a novel approach to detecting and classifying macro nutrient deficiencies in paddy leaves.
(Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Rathnayake, DMGD; Kumarasinghe, KMSJ; Rajapaksha, RMIK; Katuwawala, NKAC; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
Macro nutrient deficiency in paddy leaves is a critical concern in agriculture that impacts crop yield, food security, and sustainable farming. Addressing nutrient deficiencies in paddy plants is vital for ensuring these concerns. This research focuses on automating the detection and classification of common macro-nutrient deficiencies, specifically Nitrogen (N), Phosphorus (P), and Potassium (K). Utilizing image processing techniques, the study identifies distinct color patterns associated with each deficiency, providing a non-invasive and efficient approach. The analysis involves pixel ratio calculations within defined HSV color ranges and threshold values. A modular workflow encompasses preprocessing, horizontal partitioning, pixel ratio computation, and deficiency classification. The innovative methodology we introduced demonstrates promising outcomes, achieving a 96% accuracy rate in identifying nitrogen deficiency, along with 90% accuracy for phosphorus deficiency and 92% accuracy for potassium deficiency detection. While the methodology showcases promise, certain limitations, such as the requirement for leaf symmetry and single-deficiency identification, are recognized. These findings lay the groundwork for more accurate and automated nutrient deficiency detection, and the future work aims to address the identified limitations and generalize the solution for broader applications in real-world agricultural settings.

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