Browsing by Author "Leo, Judith"

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Accessibility Features for Augmented Reality Indoor Navigation Systems
(Springer Link, 2024-06-29) Samson, Frank; Dida, Mussa; Leo, Judith; Shidende, Deogratias; Naman, Godfrey
Accessibility plays a pivotal role in developing technological tools that strive to promote inclusivity for users of all abilities. Regrettably, many technological advancements have traditionally disregarded accessibility, assuming homogeneous user abilities or treating it as an afterthought. This paper endeavors to provide a review of accessibility considerations on augmented reality indoor navigation systems, to enhance navigational experiences in indoor learning environments. To achieve this, interviews were conducted with visually impaired individuals, to investigate their existing methods of navigation, identify challenges they face, and uncover potential accessibility features that could enhance indoor navigation systems. Additionally, a literature review was undertaken to explore various accessibility features in the context of indoor navigation systems, including localization technologies and pathfinding algorithms employed in indoor navigation applications. Finally, the paper concludes by offering insights into accessibility features specifically tailored for individuals with visual impairments, to facilitate efficient indoor navigation.
AI for anglophone africa: unlocking its adoption for responsible solutions in academia-private sector
(Frontiers, 2023-04-11) Sinde, Ramadhani; Diwani, Salim; Leo, Judith; Kondo, Tabu; Elisa, Noe; Matogoro, Jabhera
In recent years, AI technologies have become indispensable in social and industrial development, yielding revolutionary results in improving labor efficiency, lowering labor costs, optimizing human resource structure, and creating new job demands. To reap the full benefits of responsible AI solutions in Africa, it is critical to investigate existing challenges and propose strategies, policies, and frameworks for overcoming and eliminating them. As a result, this study investigated the challenges of adopting responsible AI solutions in the Academia-Private sectors for Anglophone Africa through literature reviews, expert interviews, and then proposes solutions and framework for the sustainable and successful adoption of responsible AI.
Assessment of integrating Environmental Factors into Healthcare Models for Enhancing Timely Epidemics Analysis: A Case study of Cholera in Dar es Salaam –Tanzania
(IJASRE, 2021) Leo, Judith; Marwa, Janeth
The objective of this study is to assess the perspectives of userson the feasibility of using integrated environmental factors-based healthcare model to enchance timely cholera epidemics analysis in Tanzania.The study used a mixed-design approach of quantitative and qualitative methods with focus group discussion and interviewer-administered questionnaires. Participants or users included;medicalandepidemiological experts, environmental experts, Information and CommunicationTechnology (ICT) experts, and cholera patients from Ilala, Ubungo, Kigamboni, Temeke, and Kinondoni disctricts in Dar es Salaam, Tanzania.In the process,atotal of500interviews were conducted, consisting of 200medical experts, 50 environmental experts, 50 ICT experts, and 200 cholera patients, with an average age of 28 years old, and at 3:2 female to male ratio. Overall, our findings showed that Health and Environmental Integrated Modelled Systems (HEIMs) interventions are acceptable, feasible and capable in assisting timely analysis towards effective prediction and eradication of epidemics such as; cholera outbreaksat 79% acceptability, 90% aid-value and 69%awareness levels.Despite, the high acceptability level, participants also highlighted barriersof the model, such as;sustainabilityand operationcosts, which need to be addressed.These findings confirm that the program of HEIMS is of high potential towards enhancing timely cholera analysis, clear public health significance andrelevant to policy-makers, government, society, and related stakeholders at large. Therefore, the study, recommends that there is a need for all these organs to work together towards clearing all obstacles in order to achieve the intended goal of the HEIMs program
CFD Analysis of Flow Characteristics and Diagnostics of Leaks in Water Pipelines
(ETASR, 2024-09) Mushumbusi, Philbert; Leo, Judith; Chaudhari, Ashvinkumar; Masanja, Verdiana
This study utilizes Computational Fluid Dynamics (CFD) to generate pressure and flow rate values for the analysis of flow characteristics and the diagnosis of leaks in inclined pipelines. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) solver in OpenFOAM software was modified to incorporate the effect of pipe orientation angle. Subsequently, the SIMPLE solver was employed to simulate the flow of water through the pipe. Leakage rates were observed to vary in magnitude with respect to leak position and pipe orientation angle, except that leaks close to the flow inlet and pipes with a greater inclination were associated with higher leakage rates. A mathematical leak model is proposed based on non- dimensional flow variables and pipe orientation angle. To generate sufficient pressure values and leakage rates, the CFD simulation was performed 70 times. These values were then incorporated into the mathematical model for the leak location to be predicted. The proposed method is applicable to the detection of leakages of varying sizes in pipelines with different orientations. Therefore, knowing the pipe orientation angle and measurements of inlet flow rate, outlet flow rate, and pressure drop, the model can be used to precisely locate leaks in a pipeline.
Climatology-aware health management information system to enhance cholera epidemic analysis and prediction in Tanzania
(International Journal of Advanced Technology and Engineering Exploration (IJATEE), 2019) Leo, Judith; Luhanga, Edith T.; Michael, Kisangiri
The cholera epidemic remains a public health threat in many developing countries including Tanzania. It affects vulnerable populations living with an unreliable water supply and sub-standard sanitary conditions. Various studies have found that the occurrence of cholera has strong linkage with environmental factors such as climatology aspects and geographical location. In addition, climatology has been strongly linked to the creation of weather patterns that favor the transmission and growth of Vibrio cholerae, which causes the disease. There are several studies that have been conducted to integrate environmental factors into the existing health management information systems (HMISs) in order to enhance the analysis of cholera epidemics in Tanzania. This work explored how well climatology factors have been integrated into these existing HMISs and the potential of the systems in enhancing cholera epidemics analysis. We found that most of the existing HMISs have not explicitly integrated environmental and climatology features for effective analysis of diseases. We thus proposed the design and development of an effective Climatology-aware HMIS. Then, evaluate it with clinical and environmental data such as; geographical location, weather, conditions of the day, and date on set, of 22 medical students staying in the Mweka district in Tanzania. The results of system evaluation showed that 87% provided positive feedback on the capacity of the developed system, towards enhancing the cholera epidemic analysis and prediction linked with environmental factors particularly the climate change variables. The study recommends the review of systems and policies in the health sectors in order to adapt climatology factors.
A Comparative Study of Assistive Technologies for Physically challenged peoples for usability of Powered Wheelchair Mobility Aid
(International Journal of Advances in Scientific Research and Engineering (ijasre), 2022-01) Rwegoshora, Florian; Leo, Judith; Kaijage, Shubi
Physical disabilities have always been a big issue in our communities. Ageing, sickness, and other variables have all had a role in the creation of these issues. That is why Powered wheelchairs were designed to aid people with physical disabilities. Wheelchair users have been exposed to a variety of assistive technologies designed to improve their mobility. As a result, different assistive technologies have recently played a significant role in assisting wheelchair users with movement, this is because technology changes so quickly. The recent trendy of assistive technologies include the joystick, brain-computer interface, voice recognition, tongue drive system, eye tracer, and sip and puff. However, some of the most beneficial assistive technologies become difficult to utilize due to technological gaps among individuals in particular nations. The objective of this research to study and review the comparative study of these assistive technologies for Physical Disabilities. In the study, tongue drive system, eye tracer, voice recognition, and sip and puff technologies are compared to joystick assistive technology. The comparison is made based on selected parameters including usability commands, fatigue, response time, information transfer rate, effects, and costs. Based on review, the researchers propose the design of appropriate wheelchairs with assistive technology for developing countrie
Complexity of Epidemics Models: A Case-Study of Cholera in Tanzania
(Springer Nature Switzerland AG., 2022-12-02) Leo, Judith
Timely prediction of Cholera epidemics is essential for preventing and controlling the size of an outbreak. Over the past years, there have been great initiatives in the development of Cholera epidemic models using mathematical techniques, which are believed to be the most powerful tools in developing mechanistic understanding of epidemics. Despite the existence of these initiatives, the timely prediction of Cholera is still a great challenge. Recently, the World Health Organization reported that “the global burdens of waterborne epidemics from environmental factors are expected to increase over-time with an increase of epidemic size.” Due to these challenges, this paper reviewed existing Cholera mathematical models and observe that they have limitations/complexities, especially when working with many variables. The use of how machine learning (ML) can be used to overcome the limitations/complexities, such as lack of effective integration of environmental factors, such as weather are investigated. Hence, the study developed an ML reference model and its development procedures, which can be used to overcome the existing complexities. The results indicate at an average of 87% that the developed measures can integrate a large number of datasets, including environmental factors for the timely prediction of Cholera epidemics in Tanzania.
Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania
(Elsevier, 2023-06-16) Mduma, Neema; Leo, Judith
Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis, formerly known as Mycosphaerella fijiensis. Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa.
Dataset: Optimizing LoRaWAN Throughput in Maritime Environments Through Adaptive Coding and Modulation in Rayleigh Fading Channels
(Zenodo, 2025) Lyimo, Martine; Mgawe, Bonny; Leo, Judith; Dida, Mussa; Michael, Kisangiri
This dataset supports the article “Optimizing LoRaWAN Throughput in Maritime Environments through Adaptive Coding and Modulation under Rayleigh Fading”. It includes simulation outputs and MATLAB source code for reproducing all figures and results in the study. Files include throughput, PER, energy efficiency, spectral efficiency, and an ACM algorithm function.
A Deep Learning Model for Classifying Black Sigatoka Disease in Banana Leaves Based on Infection Stages
(IJST, 2024-10-04) Nyambo, Devotha; Kambo, Edwin; Leo, Judith; Ally, Mussa
Objective: This research study aims to develop an efficient deep-learning model to detect and classify stages of Black Sigatoka disease in banana plants. Methods: In this study, deep learning techniques, specifically the basic Convolutional Neural Network (CNN) and VGG16 models, were used to address the challenge of identifying Black Sigatoka disease in banana leaves early on. The tests were conducted on a dataset containing labelled images of banana leaves, assessing their effectiveness based on criteria such as accuracy, precision, recall, and F1-score after adjusting hyperparameters for optimal outcomes. Findings: The results of the trials revealed that the basic CNN model attained a training accuracy of 96% and a validation accuracy of 89%, surpassing the performance of the VGG16 model. The VGG16 model, on the other hand, had a training accuracy of 92% and a validation accuracy of 89%. Across precision, recall, and F1 score measurements, the basic CNN model consistently outperformed the VGG16 model, with scores averaging 0.90 for all three metrics compared to VGG16’s precision of 0.80, recall of 0.75, and F1 score of 0.75. The CNN model demonstrated its efficiency by stopping training at 26 epochs, whereas VGG16 completed training in 21 epochs. This demonstrates its effectiveness in detecting Black Sigatoka while utilising minimal resources. Novelty: A significant component of this study is its emphasis on identifying the stages of Black Sigatoka disease, which is commonly overlooked in research. By studying disease progression, this study provides insights for early intervention and disease management, aiding efforts to lessen the impact of Black Sigatoka on banana farming.
Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data
(Elsevier, 2024-12-08) Mambile, Cesilia; Kaijage, Shubi; Leo, Judith
Forest fires (FFs) are a growing threat to ecosystems and human settlements, particularly in vulnerable regions such as Mount Kilimanjaro, Tanzania. Accurate and timely fire prediction is essential to mitigate these risks and improve fire management strategies. This study develops and evaluates advanced Deep Learning (DL) models for FF prediction by integrating spatiotemporal vegetation indices, environmental data, and human activity in- dicators. Specifically, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Convolu- tional Long Short-Term Memory (ConvLSTM) models were employed to analyze Sentinel-2 satellite imagery and weather data, along with anthropogenic factors such as beekeeping, tourism, agriculture, and deforestation rates. Leveraging this diverse, high-dimensional dataset, the ConvLSTM model engineered to capture intricate spatial and temporal relationships delivered superior performance, achieving an AUROC of 0.9785 and Accuracy 98.08%, surpassing the LSTM and CNN models. Integrating human-induced activities with environmental data, these models provide accurate and actionable predictions for fire management in high-risk areas. This study demonstrates the potential of ConvLSTM in developing operational tools for early fire detection, streamlining data-driven decision-making, improving resource allocation, and guiding preventive strategies in fire-prone re- gions such as Mount Kilimanjaro.
Deep learning-based mobile application for the enhancement of pneumonia medical imaging analysis: A case-study of West-Meru Hospital
(Elsevier, 2024-09) Kimeu, Japheth; Kisangiri, Michael; Mbelwa, Hope; Leo, Judith
Pneumonia remains a significant global health challenge, demanding innovative solutions. This study presents a novel approach to pneumonia diagnosis and medical imaging analysis, leveraging advanced technologies. The study used a Literature Review Methodology to study various scientific articles and involved healthcare staff, including Doctors, Nurses, Radiologists and the community, in sharing their requirements for the study. The findings led to the proposal for the integration of Deep Learning techniques, including Convolutional Neural Network (CNN), as well as tools like YOLOv8, Roboflow, and Ultralytics, to revolutionize pneumonia detection and classification. The EfficientDet-Lite2 model architecture was subsequently used to generate a TensorFlow Lite Model, deployable in both Android and iOS mobile applications. The study’s outcomes reveal a substantial improvement in the precision and recall metrics. These results signify a promising step forward in empowering healthcare professionals with timely and reliable results for optimal patient management.
Design of an Interactive Geo-Location Mobile Application for Civil Societies in East Africa
(Scientific Research Publishing Inc, 2021-10-13) Patrick, Emil; Leo, Judith; Kaijage, Shubi
This paper reports the developed mobile application through the use of ad- vanced technologies such as mobile computing, cloud computing and Global Positioning System (GPS) in order to solve the challenges. The purpose of this study is to find out how the recent advances and mass adoption of ICT by the public can best be leveraged to enhance the performance of Civil Society Organizations (CSOs) in East Africa. The data collection techniques used are through questionnaires, observations and conducting interviews with differ- ent stakeholders in the civil society arena i.e., the donors, the CSOs and the people in society that are given a voice by civil societies. In the system devel- opment phase, agile development methodology was used. Analysis of the data collected showed that there is a gap in the availability of a single or a centra- lized platform which can be easily accessed, user-friendly and reliable where different actors can readily get reliable and up-to-date information about the available CSOs they are interested in. To address this problem, an interactive online directory of CSOs has been developed. The platform is mobile based and enables CSOs to register and fill up their current details, thus ensuring that there is always correct and updated information. The platform is equipped with, among other features, a geo-mapping facility which enables users of the system to correctly geo-locate their civil societies of interest on a map view. The results of system evaluation showed that 88.125% of users were satisfied with the system basing on the evaluation criteria.
Detectron2-enhanced mask R-CNN for precise instance segmentation of rice blast disease in Tanzania: Supporting timely intervention and data-driven severity assessment
(Elservier, 2025-08-09) Alfred, Reuben; Leo, Judith; Kaijage, Shubi
Rice blast, caused by Magnaporthe oryzae, poses a significant threat to rice production in Tanzania and across Africa, affecting food security and farmers’ livelihoods. Traditional inspection methods are slow and often overlook early symptoms, leading to delayed responses. Although progress has been made with deep learning diagnostics, many approaches still depend on whole-image classification or broad bounding boxes, lacking them pixel-level detail needed to assess infection severity. This study introduces a Mask R-CNN instance segmentation model developed within the Detectron2 framework to accurately detect and segment blast lesions (BL), blast- infected leaves (BIL), and healthy leaves (HL) at the pixel level. In addition to detection, the model quantifies the lesion severity by computing the proportion of infected leaf area, supporting informed evaluation and improved disease management decisions. Built on a ResNet-50 backbone with a Feature Pyramid Network (FPN), it achieved a mean average precision (mAP) of 89.4 %, with an AP50 of 94.6 % and an AP75 of 90.5 %. The model exhibited consistent performance across object scales, achieving an AP of 81.31 % for small objects and 86.06 % for large objects. Furthermore, testing on unseen images (images not used in the training process) demonstrated strong generalization, with detection confidence above 99 % and accurate masks that provide reliable severity scores. By enabling pixel-level severity assessment without expensive sensors or UAVs, this study offers a practical and affordable solution for disease monitoring in resource constrained farming communities. It equips Tanzanian smallholder farmers with timely, accessible tools for effective blast detection and data-driven decision making.
Development of IoT based Security System for Fire Monitoring and Prevention in Tanzanian Industries
(International Journal of Advances in Scientific Research and Engineering (ijasre), 2021-12) Irakomeye, Jesus; Nshimiye, Abel; Ng’ondya, Kisakyake; Sumari, Elibariki; Leo, Judith; Sam, Anael
Over the past years, there have been great advances in ICTs which have led to the development of fire monitoring and prevention systems. Despite the existence of these solutions, fire incidents especially in Tanzanian have been a major problem in industries which leads to the destruction of materials and loss of lives. With the help of wireless sensor networks (WSN) and the internet of things (IoT), the monitoring and prevention of fire outbreaks become easy and reliable. The present work explores the existing fire monitoring and prevention systems and then, designs and develops an innovative security system for early fire monitoring andprevention in Tanzanian industries through the use of emerging technologies such as WSN and IoT. The results of the system evaluation shows that 88% of users were satisfied with its functionalities. This is because the system works in real-time and through the use of different sensors such as DHT sensor, MQ2 sensor, and Flame sensor to monitor humidity, temperature, smoke, flammable gasses, and fire outbreak in the different work areas. The use of the mobile application and cloud subsystem, the developed system can timely alert the users via their mobile phones on the status of the fire and different parameter.
Development of RFID based Automatic Warehouse Management System: A Case Study of ROK industries Limited Kenya
(International Journal of Advances in Scientific Research and Engineering (ijasre), 2021-08) Ngaboyimbere, Fabien; Leo, Judith; Michael, Kisangiri; Muteteshe, Dinah
In the supply chain and logistics industry, the precision of the data assets inventory plays a crucial role in warehouse activities. The operations like storage locations arrangement, inventory management, and maintaining the flow of incoming and out coming goods lead to the success of the warehouse. Nowadays, most people are preferring online shopping all over the world because it is faster than local trade. Thus, there's massive information in the supply chain and logistics sector to explore in order to improve operations of the warehouse such as receiving, ordering, shipping, storage assignment to facilitate the automation in the warehouse. This study is conducted to improve warehouse activities by automating storage and inventory management. A software program was developed with sets of rules, and an algorithm to optimize the inventory operations with the help of RFID technology. The predefined rules help in giving priorities to some of the selected products to store and retrieve in indicated location. The UHF RFID reader is attached to the entrance of the gate of the warehouse to facilitate the reading of incoming goods. Once the goods arrive in the warehouse, the storage location function will assign to each product the storage location and update status in the database. A handheld reader facilitates inventory management and communicates with the application via a wireless network and finally store data in the database for future use. After developing this system, a test was conducted for testing the feasibility and applicability of the system. The output showed that the inventory management operation was made strides, and the correctness of inventory location increased from 72.8% to 99%. The cycle time moreover decreases from 50 minutes to 18 minutes which is down to 28.79%.
Early-Warning Dropout Visualization Tool for Secondary Schools: Using Machine Learning, QR Code, GIS and Mobile Application Techniques
(International Journal of Advanced Computer Science and Applications, 2022) Leo, Judith
: Investment in education through the provision of secondary school to the community is geared to develop human capital in Tanzania. However, these investments have been hampered by unacceptable higher rates of school dropouts, which seriously affect female students, since most schools do not have effective mechanisms for quality data management for immediate and effective decision making. Therefore, this study aims to solve the problem of data management from the school level in order to assist higher levels to receive appropriate and effective data on time through the use of emerging technologies such as machine learning, QR codes, and mobile application. To implement this solution, the study has explored the predictors of school dropout using a mixed approach with questionnaires and interview discussion. 600 participants participated in problem identification in the Arusha region. Through the use of design science research methodology, Unified Modeling Language, MYSQL, QR codes and mobile application techniques were integrated with Support Vector Machine to develop the proposed solution. Finally, the evaluation process considered 100 participants, and the results showed that an average of 89% of participants provided positive feedback on the functionalities of the developed tool to prevent dropouts in secondary schools in Africa at large.
Extreme Rainfall Event Classification Using Machine Learning for Kikuletwa River Floods
(MDPI, 2023-03-08) Mdegela, Lawrence; Municio, Esteban; Bock, Yorick; Luhanga, Edith; Leo, Judith; Mannens, Erik
Advancements in machine learning techniques, availability of more data sets, and increased computing power have enabled a significant growth in a number of research areas. Predicting, detecting, and classifying complex events in earth systems which by nature are difficult to model is one such area. In this work, we investigate the application of different machine learning techniques for detecting and classifying extreme rainfall events in a sub-catchment within the Pangani River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify a rain condition in the selected sub-catchment, we use data from five weather stations that have been labeled for the whole sub-catchment. In order to assess which machine learning technique is better suited for rainfall classification, we apply five different algorithms in a historical dataset for the period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, reporting random forest and XGBoost as having the best overall performances. However, because the class distribution is imbalanced, a generic multi-layer perceptron performs best when identifying heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the Pangani River Basin
Extreme Rainfall Events Classification Using Machine Learning for Kikuletwa River Floods
(Preprints, 2023-02-20) Mdegela, Lawrence; Municio, Esteban; Bock, Yorick; Mannens, Erik; Luhanga, Edith; Leo, Judith
Advancements in Machine Learning techniques, availability of more data-sets, and 1 increased computing power have enabled a significant growth in a number research areas. Predicting, 2 detecting and classifying complex events in earth systems which by nature are difficult to model 3 is one of such areas. In this work, we investigate the application of different machine learning 4 techniques for detecting and classifying extreme rainfall events in a sub-catchment within Pangani 5 River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event 6is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify 7 a rain condition in the selected sub-catchment, we use data from five weather stations which have 8 been labeled for the whole sub-catchment. In order to assess which Machine Learning technique 9 suits better for rainfall classification, we apply five different algorithms in a historical dataset for the 10 period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, 11 reporting Random Forest and XGBoost as the ones with best overall performance. However, since the 12 class distribution is imbalanced, the generic Multi-layer Perceptron performs best when identifying 13 the heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the 14 Pangani River Basin.
Identification of Resistant Common Bean Genotypes to Foliar Diseases using Hyperspectral Data
(IEEE, 2023-11-15) Kaijage, Shubi; Leo, Judith; Mamo, Teshale; Mbelwa, Hope; Guerena, David; Irakiza, Josiane
Common bean is one of many legumes (family Fabaceae) widely cultivated for their edible seeds, seedpods, and leaves. Despite its benefits and life dependency especially in sub-Saharan Africa, production of improved common bean seeds is still a problem. Among the major problems that the bean breeders are facing is manual phenotyping which led to slowing the field processes.According to the literature, imaging technologies have been introduced to help in different processes for crop management and production. However, there is a lack of automated mechanisms for phenotyping processes. Therefore, this study has reviewed existing mechanisms among breeders in Tanzania in order to propose suitable mechanisms for effective phenotyping processes. In the process of data collection, the study interviewed 60 participants among which were common bean breeders and field technicians. Questionnaires and group discussion methods were deployed to understand the insight of the participants on the current. It was observed that manual phenotyping causes biases of data due to human judgments and leads to late decision-making. Additionally, the study reviewed existing literature and it was noted that the use of technologies such as spectrometry and remote sensing technologies were found to be suitable for early and continuous plant health monitoring. However, these technologies are of high cost and produce complex outputs for the breeders to understand. However, there is a way in which these techniques can be innovatively integrated with artificial intelligence (AI) techniques in order to produce required outputs that are easy to understand and cost-effective.Based on the findings, this study proposes the development of a Hyperspectral-based system that will be used for automating phenotyping, data collection, and analysis. The system will be developed using AI that uses spectral bands captured using a cheap handheld spectrometer for the early detection of foliar diseases in bean varietie...