Browsing by Author "ZAKIA SULTANA"

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    SUNFLOWER DISEASE PREDICTION USING ARTIFICIAL INTELLIGENCE IN PERCEPT OF BANGLADESH
    (2022-11-30) ZAKIA SULTANA
    Plant disease prediction and recognition in the early stage is one of the most essential needs to increase agriculture, which plays an important role in our country's economy and helps to feed a large population. Sunflower is a plant categorized as a low to medium drought-sensitive crop. But now a days sunflower production faced a sever crisis due to its many diseases. Farmers also lost their interest to the production of sunflower. But if proper action is received earlier, many serious disease will have no effect on the plants. It also improve the productively, quantity and quality of sunflower plants. Manual identification of sunflower disease is an exhausting task at time. Now Artificial intelligent is a great field and gained its popularity for the prediction of plant disease as early as possible. In this research paper different machine learning techniques such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGBoosting) are applied in the image features that are extracted using digital image processing technique. And deep learning techniques such as Residual Network 50 (ResNet 50), Visual Geometry Group 19 (VGG 19) and Mobile Net Version 2 (MobileNet V2) are used in the direct image for predicting the disease of sunflower. A total of 602 images which are collected from Bangladesh Agricultural Institute, gazipur is used in this research work. Machine learning techniques GB shows 93.38 % accuracy on the other hand XGBoost Shows 92.56% accuracy but highest sensitivity 0.92. Deep learning model ResNet 50 Shows highest accuracy and sensitivity 95.09 % and 0.95. Among Machine learning and Deep learning models ResNet 50 shows highest accuracy and sensitivity, therefore ResNet 50 is the best model for predicting sunflower disease for this dataset.
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    SUNFLOWER DISEASE PREDICTION USING ARTIFICIAL INTELLIGENCE IN PERCEPT OF BANGLADESH
    (2022-11-30) ZAKIA SULTANA
    Plant disease prediction and recognition in the early stage is one of the most essential needs to increase agriculture, which plays an important role in our country's economy and helps to feed a large population. Sunflower is a plant categorized as a low to medium drought-sensitive crop. But now a days sunflower production faced a sever crisis due to its many diseases. Farmers also lost their interest to the production of sunflower. But if proper action is received earlier, many serious disease will have no effect on the plants. It also improve the productively, quantity and quality of sunflower plants. Manual identification of sunflower disease is an exhausting task at time. Now Artificial intelligent is a great field and gained its popularity for the prediction of plant disease as early as possible. In this research paper different machine learning techniques such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Extreme Gradient Boosting (XGBoosting) are applied in the image features that are extracted using digital image processing technique. And deep learning convolution neural network (CNN) techniques such as Residual Network 50 (ResNet 50), Visual Geometry Group 19 (VGG 19) and Mobile Net Version 2 (MobileNet V2) are used in the direct image for predicting the disease of sunflower. A total of 602 images which are collected from Bangladesh Agricultural Institute, gazipur, is used in this research work. Machine learning techniques GB shows 93.38 % accuracy on the other hand XGBoost Shows 92.56% accuracy but highest sensitivity 0.92. Deep learning model ResNet 50 Shows highest accuracy and sensitivity 96.19 % and 0.96. Among Machine learning and Deep learning models ResNet 50 shows highest accuracy and sensitivity, therefore ResNet 50 is the best model for predicting sunflower disease for this dataset.