Browsing by Author "Akter, Bonna"

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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 Novel Automated Feature Selection Based Approach To Recognize Cauliflower Disease
(Institute of Electrical and Electronics Engineers Inc., 2023-04-17) Shakil, Rashiduzzaman; Akter, Bonna; Shamrat, F M Javed Mehedi; Noor, Sheak Rashed Haider
Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).
A Promising Prediction of Diabetes Using a Deep Learning Approach
(Daffodil International University, 2022-01-06) Shakil, Rashiduzzaman; Akter, Bonna; Faisal, Fahad; Chowdhury, Tahmid Rashik; Roy, Tonmoy; Khater, Ankit
Diabetes is a collection of metabolic illnesses caused by a persistently high blood sugar level. If a reliable estimation is achievable, diabetes risk factors and severity can be reduced. In diabetes datasets, consistent and effective diabetes prediction is challenging because of the limited amount of labeled data and the abundance of outliers (or missing values).Alongside, the incidence rates of diabetes are rising alarmingly every year. Consequently, an early diagnosis of diabetes would be the most crucial step for receiving proper treatment. Hence, a deep learning-based reorganization system has gained popularity regarding disease identification. In this work, we used an updated Convolution Neural Network (CNN) model, modifying different hyperparameters and layer topologies on the UCI 130 USA Hospitals diabetes dataset. Additionally, five different types of optimizer, namely adaptive moment estimation (ADAM), ADAMAX, A more sustainable deal has been made using the Root Mean Square Propagation algorithm (RMSprop), stochastic gradient descent (SGD), and Nesterov accelerated adaptive moment (NADAM). Furthermore, improved accuracy of 99.98% was received by the ADAMAX optimizer.
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.
Addressing Agricultural Challenges: An Identification of Best Feature Selection Technique for Dragon Fruit Disease Recognition
(Elsevier, 2023-11-02) Shakil, Rashiduzzaman; Islam, Shawn; Shohan, Yeasir Arafat; Mia, Anonto; Rajbongshi, Aditya; Rahman, Md Habibur; Akter, Bonna
Dragon fruit is a prominent substance in global agriculture. Despite this, it is gaining popularity and is a viable solution in resource-poor, environmentally degraded areas because of its many health benefits. Nevertheless, many dragon fruit plantations have been impacted by the disease, reducing their yield, and the detection system is still conventional. Farmers’ lack of disease identification and management expertise diminished crop quality and products. As a result, little research was carried out to assist those specific farmers requiring adequate agricultural support. This research has proposed an autonomous agro-based system to recognize dragon diseases using in-depth analysis of feature selection techniques. After the collection of real-time images of the dragon, the images are preprocessed using various image-processing techniques. The two important features are retrieved after segmentation. The analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) are used as feature selection techniques to assess the feature rank based on the mutual score. To analyze the effectiveness of the machine learning algorithms that were used, six distinct machine learning classifiers were applied to the top-ranked feature sets, and their performance was measured using seven distinct performance evaluation metrics. AdaBoost and Random Forest classifiers for the LASSO feature ranking approach got the maximum accuracy, which is 96.29%, based on a comparison of classifiers based on the ANOVA and LASSO feature set. Despite this, we have optimized the computational resources of each classifier for the LASSO feature set.
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.
Aprecise machine learning model: Detecting cervical cancer using feature selection and explainable AI
(Scopus, 2024-12-30) Shakil, Rashiduzzaman; Islam, Sadia; Akter, Bonna
Cervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies, we presented a precise approach utilizing six dif ferent machine learning models (decision tree, logistic regression, naïve bayes, random forest, k nearest neighbors, support vector machine), which can predict the early stage of cervical cancer by an alysing 36 risk factor attributes of 858 individuals. In addition, two data balancing techniques—Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling—were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to eval uate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the
Fruitseg30_segmentation Dataset & Mask Annotations: A Novel Dataset for Diverse Fruit Segmentation and Classification
(Elsevier, 2024-08-10) Shamrat, F.M. Javed Mehedi; Shakil, Rashiduzzaman; Idris, Mohd Yamani Idna; Akter, Bonna; Zhou, Xujuan
Fruits are mature ovaries of flowering plants that are integral to human diets, providing essential nutrients such as vitamins, minerals, fiber and antioxidants that are crucial for health and disease prevention. Accurate classification and segmentation of fruits are crucial in the agricultural sector for enhancing the efficiency of sorting and quality control processes, which significantly benefit automated systems by reducing labor costs and improving product consistency. This paper introduces the “FruitSeg30_Segmentation Dataset & Mask Annotations”, a novel dataset designed to advance the capability of deep learning models in fruit segmentation and classification. Comprising 1969 high-quality images across 30 distinct fruit classes, this dataset provides diverse visuals essential for a robust model. Utilizing a U-Net architecture, the model trained on this dataset achieved training accuracy of 94.72 %, validation accuracy of 92.57 %, precision of 94 %, recall of 91 %, f1-score of 92.5 %, IoU score of 86 %, and maximum dice score of 0.9472, demonstrating superior performance in segmentation tasks. The FruitSeg30 dataset fills a critical gap and sets new standards in dataset quality and diversity, enhancing agricultural technology and food industry applications.
RoseNet:
(Daffodil International University, 22-08-02) Sazzada, Sadia; Rajbongshi, Aditya; Shakil, Rashiduzzaman; Akter, Bonna; Kaiser, M. Shamim
For the welfare of self-development and the country’s economic evolution, people invest their youth and money in different cultivation and sustainable production business sectors. The crops or fruits get all the attention for this purpose, but currently, the commercial cultivation of flowers is becoming a numerous beneficial investment. As a consequence, the rose(Genus Rosa) is one of the most beautiful and commercially demanding flowers among different flowers. However, insecticide resistance is considered one of the lion’s share issues facing agricultural production of roses by decreasing plants’ growth and the quality as well as the quantity of healthy-looking flowers. Apart from this, due to different natural and environmental issues, rose’s quality and production level are losing their fame. Additionally, the cultivators of this sector are not educated enough to identify the initial affection of different diseases of leaves with beard eyes. Besides, the lack of communication skills to consult with an agriculturist timely turns the situation worst more than the estimation of the production. With this concern, early detection of diseases that affected different parts of roses, such as leaves, is crucial. Recently, image processing techniques and machine learning classifiers have been primarily applied to recognize multiple diseases. This article presents an extensive dataset of rose leaves images, both diseases affected and diseases free are classified into three classes (Blackspot, Downy Mildew, and Fresh Leaf). The dataset is composed of the collected images which were captured during the seasonal time of diseases affection with the consultation of a domain expert and the dataset is accessible at https://data.mendeley.com/datasets/7z67nyc57w/2
Sunflower Diseases Recognition Using Computer Vision-Based Approach
(Scopus, 2021) Rajbongshi, Aditya; Biswas, Al Amin; Biswas, Jahanur; Shakil, Rashiduzzaman; Akter, Bonna; Barman, Mala Rani
Sunflower (Helianthus annuus) is a plant categorized as a low to medium drought-sensitive crop. It adds a significant value to the agricultural-based economy. But nowadays worldwide sunflower production is in crisis due to its many diseases. But if proper action is not adopted earlier, many serious diseases will have affect plants. Consequently, it will reduce the productivity, quantity, and quality of sunflower. Manual identification of disease is a very tedious task or perhaps impossible at times. Nowadays, computer vision-based technique has gained its popularity in the field of object recognition. In this paper, we proposed an approach for sunflower disease recognition. A total of 650 images were used to accomplish this work. The image data processing techniques such as resizing, contrast, and color enhancement have also been used. We have used k-means clustering for segmenting the diseases affected region and then extracted features from the segmented images. The classification has performed using five classifiers. We calculated the seven performance evaluation metrics for the performance measurement of each classifier. The highest average accuracy of 90.68% has been obtained for the Random Forest classifier that outperformed others.
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.