Browsing by Author "Elinisa, Christian"

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Banana Leaves Imagery Dataset
(Springer Nature, 2025-03-21) Mduma, Neema; Elinisa, Christian
In this work, we present a dataset of banana leaf imagery, both with and without diseases. The dataset consists of 11,767 images, categorized as follows: 3,339 healthy images, 3,496 images of leaves affected by Black Sigatoka and 4,932 images of leaves affected by Fusarium Wilt Race 1. This data was collected to support machine learning diagnostics for disease detection. The data collection process involved farmers, researchers, agricultural experts and plant pathologists from the northern and southern highland regions of Tanzania. To ensure unbiased representation, farms were randomly selected from the Rungwe, Mbeya, Arumeru, and Arusha districts, based on the presence of banana crops and the targeted diseases. The dataset offers a comprehensive collection of images captured from November 2022 to January 2023, using a high-resolution smartphone camera across a wide geographical area. Researchers and developers can use this dataset to build machine learning solutions that automatically detect diseases in images, potentially enabling agricultural stakeholders, including farmers, to diagnose Fusarium Wilt Race 1 and Black Sigatoka early and take timely action.
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.
Mask R-CNN Model for Banana Diseases Segmentation
(Springer Link, 2024) Elinisa, Christian; Mduma, Neema
Early detection of banana diseases is necessary for developing an effective control plan and minimizing quality and financial losses. Fusarium Wilt Race 1 and Black Sigatoka diseases are among the most harmful banana diseases globally. In this study, we propose a model based on the Mask R-CNN architecture to effectively segment the damage of these two banana diseases. We also include a CNN model for classifying these diseases. We used an image dataset of 6000 banana leaves and stalks collected in the field. In our experiment, Mask R-CNN achieved a mean average precision of 0.04529, while the CNN model achieved an accuracy of 96.75%. The Mask R-CNN model was able to accurately segment areas where the banana leaves and stalk were affected by Black Sigatoka and Fusarium Wilt Race 1 diseases in the image dataset. This model can assist farmers to take the required measures for early control and minimize the harmful effects of these diseases and rescue their yields.
Mobile-Based convolutional neural network model for the early identification of banana diseases
(Elsevier, 2024-02-29) Elinisa, Christian; Mduma, Neema
This study aimed to deploy a deep learning model in a mobile application for the early identification of Fusarium Wilt and Black Sigatoka in bananas. In this paper, a Convolutional Neural Network (CNN) model for the clas- sification of Black Sigatoka banana disease and Fusarium Wilt disease is assessed. A dataset of 27,360 images of diseased and healthy banana leaves and stalks that were collected from the farms using a mobile phone camera served as the training data for this model. An extra class of 407 images that are not of the banana plant downloaded from the internet was used to help the model detect other images not of the banana plant. The CNN model achieved an accuracy of 91.17 % and was deployed in a mobile application for the classification of the diseases. This study shows that deep learning can be implemented and assist in the early identification of banana diseases. The application could detect images of healthy and diseased banana leaves and stalks and images not of the banana plant with a confidence score of more than 90 % in less than five seconds per image and provide research-based mitigation recommendations.
Mobile-Based convolutional neural network model for the early identification of banana diseases
(Elsevier, 2024-02-29) Elinisa, Christian; Mduma, Neema
This study aimed to deploy a deep learning model in a mobile application for the early identification of Fusarium Wilt and Black Sigatoka in bananas. In this paper, a Convolutional Neural Network (CNN) model for the clas- sification of Black Sigatoka banana disease and Fusarium Wilt disease is assessed. A dataset of 27,360 images of diseased and healthy banana leaves and stalks that were collected from the farms using a mobile phone camera served as the training data for this model. An extra class of 407 images that are not of the banana plant downloaded from the internet was used to help the model detect other images not of the banana plant. The CNN model achieved an accuracy of 91.17 % and was deployed in a mobile application for the classification of the diseases. This study shows that deep learning can be implemented and assist in the early identification of banana diseases. The application could detect images of healthy and diseased banana leaves and stalks and images not of the banana plant with a confidence score of more than 90 % in less than five seconds per image and provide research-based mitigation recommendations