Timely and precise detection is essential for effective disease management. The proposed model offers a solution to enhance detection accuracy while simplifying the process, utilizing a dataset of rice leaf images obtained from Kaggle.com. The dataset, though valuable, presented challenges due to image...
The experimental data for this study are mainly collected from the Internet, public dataset Rice Leaf Disease Image Samples [12], and relevant rice leaf disease images and labels provided by Kaggle (https://www.kaggle.com). The dataset contains a ...
plants. The images can be categorized into four different classes namely Brown-Spot, Rice Hispa, Leaf-Blast and Healthy. The dataset consists of 2092 different images with each class containing 523 images. Each image consists of a single healthy or diseased leaf placed against a white background...
types of rice leaf disease. About Dataset Context Shayan Riyaz found this dataset when it was not uploaded on Kaggle and was scattered across the internet. This dataset is a collection of multiple data sets that can be found online. The size of this dataset is ~ 7 GB due to the high ...
This combination results in a robust and efficient disease prediction system. We evaluate our model's performance using a comprehensive dataset of rice leaf images from various disease types and growth stages, sourced from the freely available Kaggle online resource. Our hybrid MCSVM-DNN predictor ...
The first scenario of the experiment has been carried out using Plant Village dataset. The second scenario of experiment uses the rice plant disease dataset obtained from Kaggle with three classes. The second dataset used which is known as the Mendeley dataset which contains five dif...
The plant disease datasets are collected from two sources, such as Rice Disease Image Dataset (Kaggle) and Rice Leaf Diseases Dataset (UCI Machine Learning Repository). The proposed M-Net model exhibits classy results, attained 71% accuracy, outperformed the benchmarked state-of-the-art deep ...
The dataset for training, testing and validation collected and constructed from Kaggle and Google dataset includes approximately 400 images for each type of rice leaf diseases. From the experiment to assess the performance of the proposed learning method, the accuracy can reach approximately 97%. ...
The experiments are performed on Kaggle based rice plant disease detection dataset and the performance in terms of precision, recall, f1-score and accuracy has been measured. The experimental evaluation highlights two major points (1) the CNN does not require additional features ...
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