Browsing by Author "Sathi, Taslima Akter"

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    SunNet: A Deep Learning Approach to Detect Sunflower Disease
    (IEEE, 2023-05-24) Sathi, Taslima Akter; Hasan, Md Abid; Alam, Mohammad Jahangir
    Helianthus annuus, often known as sunflower, is a crop that is only mildly affected by drought. The agricultural sector of the economy benefits greatly from this. However, various illnesses have imposed a halt on sunflower cultivation over the world. However, many severe diseases will affect plants if corrective measures are not taken sooner. Therefore, it will have a negative impact on sunflower yield, quantity, and quality. Diagnosing a disease by hand can be a time-consuming and difficult process. Object recognition methods that use deep learning are becoming increasingly commonplace today. This study has developed a strategy for identifying diseases in sunflowers. A total of 1428 photos were utilized to complete this task. Images have also been processed using methods like resizing, adjusting contrast, and boosting color. Here, the area of the photos afflicted by the disease is segmented by using k-means clustering, and then retrieved characteristics from those regions. Four deep-learning classifiers were used to complete the classification. For the purpose of comparing classifier quality, four performance evaluation measures are computed. The best-performing classifier overall was a ResNet50 classifier, which had an average accuracy of 97.88% and the lowest accuracy is obtained from Inception V3.
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    Sunnet: A Deep Learning Approach To Detect Sunflower Disease
    (Daffodil International University, 23-02-18) Sathi, Taslima Akter; Hasan, MD. Abid
    Helianthus annuus, often known as sunflower, is a crop that is only mildly affected by drought. The agricultural sector of the economy benefits greatly from this. However, various illnesses have imposed a halt on sunflower cultivation over the world. However, many severe diseases will have affected plants if corrective measures are not taken sooner. Therefore, it will have a negative impact on sunflower yield, quantity, and quality. Diagnosing a disease by hand can be a time-consuming and difficult process. Object recognition methods that use deep learning are becoming increasingly commonplace today. In this study, we put out a strategy for identifying diseases in sunflowers. A total of 1428 photos were utilized to complete this task. Images have also been processed using methods like resizing, adjusting contrast, and boosting color. We have segmented the area of the photos afflicted by the disease using k-means clustering, and then retrieved characteristics from those regions. Five deep learning classifiers were used to complete the classification. For the purpose of comparing classifier quality, we computed four performance evaluation measures. The best performing classifier overall was a ResNet50 classifier, which had an average accuracy of 97.88%