Browsing by Author "Mayo, Flavia"

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Convolutional Neural Network Deep Learning Model for Early Detection of Streak Virus and Lethal Necrosis in Maize: A Case of Northern-Highlands, Tanzania
(Springer Link, 2024-06-30) Mayo, Flavia; Mduma, Neema
In the Tanzanian context, maize is the dominant food crop that serves as a significant common and traditional food being grown in about 45% of the country’s farmland. However, its productivity is hindered by diseases that diminutions its quality and quantity. Maize streak virus (MSV) and maize lethal necrosis (MLN) are the two diseases that have been reported by farmers to dominate for ages. These diseases are likely to be cured if early detected. Nevertheless, sophisticated tools for detecting these diseases are still lagging behind the fast pace of technology in developing countries like Tanzania. That being the case, this study aims to fill the gap by investigating the need and development of a deep learning model for early detection of these two diseases. In doing so, a deep learning solution based on Convolution Neural Networks (CNN) has been developed to predict the early occurrence of these diseases in maize leaves. A CNN model was developed from scratch with a total of 1500 datasets belonging to three classes namely; healthy, MLN, and MSV. The developed model attained a validation accuracy of 98.44%. Since the validation accuracy is more than 70% then, this model is reliable and have potential of being adopted in early prediction of MLN and MSV diseases. However, the vision transformer (ViT) model will be developed, and its efficiency be compared with CNN. The model with best results will be deployed in a mobile device, ready for use by farmers in real-life environments.
A deep learning model for early detection of maize diseases in Tanzania
(NM-AIST, 2024-08) Mayo, Flavia
Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of residents depending on it for their livelihood. Maize is the main and dominating food crop in the country, accounting for 45% of all farmland produce. Inevitably, its production is affected by diseases that, if detected early, could be easily treated. Maize Streak Virus (MSV) and Maize Lethal Necrosis (MLN) are commonly reported diseases that are often detected too late by farmers. This raised the need for a sophisticated method for early detection of these diseases to enable timely treatment. This study investigated the potential of developing a deep-learning model for the early detection of maize diseases in Tanzania. Imagery datasets were used for model training, they were collected through physical observation with the help of a plant pathologist from three regions of the country; Arusha, Kilimanjaro, and Manyara. Two models, a Convolutional Neural Network (CNN) and a Vision Transformer (ViT), were developed from scratch and classified into four classes: Healthy, MLN, MSV, and WRONG. The results revealed that the ViT model outperformed the CNN model, achieving validation accuracies of 0.931 and 0.9096 respectively. Despite the superior performance of the ViT model, the CNN model was selected for deployment in a mobile-based application due to its smaller size, lower memory, and small computational requirements. A quantitative research method using a survey questionnaire was employed to gather requirements for system development and validation. Validation of the model's performance through questionnaires administered to farmers and agricultural experts, yielding highly positive feedback. This enthusiastic reception highlights the potential impact of the developed application on improving early disease detection and enhancing maize productivity in Tanzania. Empowering stakeholders of agriculture to identify more effective methods of managing them before serious harm is done. Consequently, custodians of agriculture, such as the Ministry of Agriculture, organizations, and other companies, will have the potential to detect maize diseases early, henceforth adding up to the increase in the Gross Domestic Product (GDP) and guaranteeing the country’s food security.
Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania
(International Journal of Innovative Research & Development, 2024-08-16) Mduma, Neema; Mgala, Mvurya; Maina, Ciira; Mayo, Flavia
Agriculture is considered the backbone of Tanzania’s economy, with more than 60% of the residents depending on it for survival. Maize is the country’s dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep- learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.
Updating “machine learning imagery dataset for maize crop: A case of Tanzania” with expanded data to cover the new farming season
(Elsevier Inc., 2024-03-23) Mduma, Neema; Mayo, Flavia
Maize Lethal Necrosis (MLN) and Maize Streak Virus (MSV) are among maize diseases which affect productivity in Tan- zania and Africa at large. These diseases can be detected early for timely interventions and minimal losses. Machine learning (ML) has emerged as a powerful tool for automated diseases detection, offering several advantages over tradi- tional methods. This article presents the updated dataset of 9356 imagery maize leaves to assist researchers in develop- ing technological solutions for addressing crop diseases. The high-resolution imagery data presented in this dataset were captured using smartphone cameras in farm fields which were not selected in the previously published dataset. Also, data collection was taken in the range of three months from November 2022 to January 2023 to incorporate farming sea- son not covered in the previously published dataset. The pre- sented dataset can be used by researchers in the field of Arti- ficial Intelligence (AI) to develop ML solutions and eliminate the need of manual inspection and reduce human bias. De- veloping ML solutions require large amount of data therefore, the updated and previously published datasets can be com- bined to accommodate diverse and wider applicability.