Browsing by Author "Maina, Ciira"

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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.
Image Segmentation Deep Learning Model for Early Detection of Banana Diseases
(Taylor & Francis online, 2024-12-14) Elinisa, Christian; Maina, Ciira; Vodacek, Anthony; Mduma, Neema
Bananas are among the most widely produced perennial fruits and staple food crops that are highly affected by numerous diseases. When not managed early, Fusarium Wilt and Black Sigatoka are two of the most detrimental banana diseases in East Africa, resulting in production losses of 30% to 100%. Early detection of these banana diseases is necessary for designing proper management practices to avoid further yields and financial losses. The recent advances and successes of deep learning in detecting plant diseases have inspired this study. This study assessed a U-Net semantic segmentation deep learning model for the early detection and segmentation of Fusarium Wilt and Black Sigatoka banana diseases. This model was trained using 18,240 banana leaf and stalk images affected by these two banana diseases. The dataset was collected from the farms using mobile phone cameras with the guidance of agricultural experts and was annotated to label the images. The results showed that the U-Net model achieved a Dice Coefficient of 96.45% and an Intersection over Union (IoU) of 93.23%. The model accurately segmented areas where the banana leaves and stalks were damaged by Fusarium Wilt and Black Sigatoka diseases.