Browsing by Author "Sara, Umme"

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A Comprehensive Guava Leaves and Fruits Dataset for Guava Disease Recognition
(Daffodil International University, 2022-04-12) Rajbongshi, Aditya; Sazzad, Sadia; Shakil, Rashiduzzaman; Akter, Bonna; Sara, Umme
Guava (Psidium guajava) is a delicious fruit native to Mexico, Central or South America, and the Caribbean region. It's high in vitamin C, Calcium, Pectins and is a good source of fiber. Due to concerns with natural and environmental resources, technical issues, and other impediments, the production level decreases day-to-day. However, we'll concentrate on the most critical challenges, such as infections that affect guava plants, fruits, and disease outbreak prevention through early identification. Besides, the early recognition of guava disease using the expert system will lead to higher yields that will eventually help guava farmers reduce their economic losses. In the recent era, image processing and computer vision have been broadly applied to recognize multiple diseases that are not identified with the naked eyes. This article presents a dataset of guava images containing both leaves and fruit images (diseases affected and disease-free) are classified into six classes: for guava fruits-Phytophthora, Scab, Styler end Rot, and Disease-free fruit, and for guava leaves-Red Rust, and diseases-free leave. All images are basically captured from the guava garden located at Bangladesh Agricultural University in July when the guava fruits are almost ripened, and the infections are found in guava plants. This dataset is mainly for those researchers who work with computer vision, machine learning, and deep learning to develop a system that recognizes the guava disease to assist guava farmers in their cultivation.
A Machine Learning Approach to Detect the Brain Stroke Disease
(Daffodil International University, 2022-03-15) Akter, Bonna; Rajbongshi, Aditya; Sazzad, Sadia; Shakil, Rashiduzzaman; Biswas, Jahanur; Sara, Umme
The brain, which comprises the cerebrum, cere-bellum, and brainstem and is covered by the skull, is a very complex and intriguing organ in the human body. Stroke is the world's second-leading cause of mortality; as a result, it requires prompt treatment to avoid brain damage. Early detection of a brain stroke can help to prevent or lessen the severity of the stroke, which can lower death rates. Using machine learning algorithms to identify risk variables is a promising method. This paper proposed a model that included a methodology to achieve an accurate brain stroke forecast. The efficient data collection, data pre-processing, and data transformation methods have been applied to provide reliable information for our proposed model to be successful. A “brain stroke dataset” was employed to build up the model. The standardization technique is used to standardize data. In the training and testing procedure, Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT) classifiers are applied. The performance of each classifier has been estimated by adopting performance evaluation metrics such as accuracy, sensitivity (SEN), error rate, false-positive rate (FPR), false-negative rate (FNR), root mean square error, and log loss. Based on the outcome while using the RF classifier, we can determine that our proposed model provided the maximum accuracy, which was 95.30%.
A Transfer Learning Approach to the Development of an Automation System for Recognizing Guava Disease Using CNN Models for Feasible Fruit Production
(IEEE, 2023-05-25) Shakil, Rashiduzzaman; Akter, Bonna; Rajbongshi, Aditya; Sara, Umme; Barman, Mala Rani; Dhali, Aditi
Guava (Psidium guava) is one of the most popular fruits which plays a vital role in the world economy. To increase guava production and sustain economic development, early detection and diagnosis of guava disease is important. As traditional recognition systems are time-consuming, expensive, and sometimes their predictions are also inaccurate, farmers are facing a lot of losses because of not getting the proper diagnosis and appropriate cure in time. In this study, an automatic system based on Convolution Neural Networks (CNN) models for recognizing guava disease has been proposed. To make the dataset more efficient, image processing techniques have been employed to boost the dataset which is collected from the local Guava Garden. For training and testing the applied models named InceptionResNetV2, ResNet50, and Xception with transfer learning technique, a total of 2,580 images in five categories such as Phytophthora, Red Rust, Scab, Stylar end rot, and Fresh leaf are utilized. To estimate the performance of each applied classifier, the six-performance evaluation metrics have been calculated where the Xception model conducted the highest accuracy of 98.88% which is good enough compared to other recent relevant works.
An Extensive Sunflower Dataset Representation for Successful Identification and Classification of Sunflower Diseases
(Daffodil International University, 2022-05-13) Sara, Umme; Rajbongshi, Aditya; Shakil, Rashiduzzaman; Akter, Bonna; Sazzad, Sadia; Uddin, Mohammad Shorif
Sunflowers are agricultural seed crops that can be used for essential edible oils and ornamental purposes. This cash crop is primarily cultivated in North and South America. Sunflower crops are prone to various diseases, insects, and nematodes, resulting in a wide range of production losses. Digital image processing and computer vision approaches have been widely utilized to categorize and detect plant diseases including leaves, fruits, and flowers over the last few decades. Early diagnosis of infections in sunflowers helps to prevent them from spreading throughout the farm and reducing financial losses to the farmers. This article offers a resourceful dataset of sunflower leaves and flowers that will help the researchers in developing effective algorithms for the detection of diseases. The dataset contains healthy and affected sunflower leaves and flowers with downy mildew, gray mold, and leaf scars. The images were captured manually between 25th to 29th November 2021 from the demonstration farm of Bangladesh Agricultural Research Institute (BARI) at Gazipur in cooperation with its one domain expert when the sunflower plants were about to bloom and the maximum diseases can be found. The dataset is hosted by the Department of Computer Science and Engineering, National Institute of Textile Engineering and Research (NITER), Bangladesh and freely available at https://data.mendeley.com/datasets/b83hmrzth8/1.
VegNet
(Daffodil International University, 2022-06-26) Sara, Umme; Rajbongshi, Aditya; Shakil, Rashiduzzaman; Akter, Bonna; Uddin, Mohammad Shorif
Cauliflower, a winter seasoned vegetable that originated in the Mediterranean region and arrived in Europe at the end of the 15th century, takes the lead in production among all vegetables. It's high in fiber and can keep us hydrated, and have medicinal properties like the chemical glucosinolates, which may help prevent cancer. If proper care is not given to the plants, several significant diseases can affect the plants, reducing production, quantity, and quality. Plant disease monitoring by hand is extremely tough because it demands a great deal of effort and time. Early detection of the diseases allows the agriculture sector to grow cauliflower more efficiently. In this scenario, an insightful and scientific dataset can be a lifesaver for researchers looking to analyze and observe different diseases in cauliflower development patterns. So, in this work, we present a well-organized and technically valuable dataset “VegNet’ to effectively recognize conditions in cauliflower plants and fruits. Healthy and disease-affected cauliflower head and leaves by black rot, downy mildew, and bacterial spot rot are included in our suggested dataset. The images were taken manually from December 20th to January 15th, when the flowers were fully blown, and most of the diseases were observed clearly. It is a well-organized dataset to develop and validate machine learning-based automated cauliflower disease detection algorithms.