## 5 Results and Discussion
### Dataset  
* CIFAR-10 dataset [44]: This dataset from the Canadian Institute for Advanced Research is a widely utilized dataset within computer vision (CV) and ML domains. Specifically tailored for image classification tasks, CIFAR-10 serves as a benchmark in the research and development of diverse image processing algorithms and models. Comprising 60,000 color images, each measuring \(32\times 32\) pixels, the dataset is distributed across 10 distinct classes, with 6000 images allocated per class. These classes encompass common objects such as airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks. Key characteristics include each image's RGB (red, green, and blue) color channels. The dataset is divided into a training set, consisting of 50,000 images, and a testing set, containing 10,000 images. CIFAR-10's primary purpose is to serve as a standardized dataset for evaluating image classification algorithms, offering a realistic and challenging environment. Despite the relatively small size of the images, CIFAR-10 remains instrumental for testing and comparing the efficacy of different models in addressing real-world image recognition challenges. Researchers commonly employ this dataset to develop and assess the performance of various ML models, especially convolutional neural networks (CNNs). The diverse set of classes in CIFAR-10 necessitates models to generalize effectively across different categories, contributing to its widespread use in the field. * MNIST dataset [45]: This dataset is widely acknowledged and frequently employed within the fields of ML and CV. MNIST, an acronym for Modified National Institute of Standards and Technology, is a curated collection of handwritten digits used extensively for training and assessing image processing algorithms and models. Essential characteristics of the MNIST dataset include 70,000 grayscale images featuring handwritten digits ranging from 0 to 9. Each image measures 28 \(\times\) 28 pixels. The dataset is divided into a training set comprising 60,000 images and a testing set containing 10,000 images. MNIST serves as a benchmark for evaluating ML models' performance, particularly in recognizing handwritten digits. It is widely adopted as a standard dataset for developing and assessing image classification algorithms. Challenges inherent in the MNIST dataset arise from the variability in writing styles, diverse penmanship, and potential noise in the images. Unlike datasets that include color images, MNIST images are grayscale, simplifying the input for models. MNIST is notable for its historical significance. It represents one of the pioneering datasets used for testing and benchmarking ML algorithms, especially in the context of deep learning and neural networks. Researchers commonly use MNIST as a foundational resource for experimenting with and validating image classification models. Despite its relative simplicity compared to more complex datasets, MNIST remains an essential tool for educational purposes, enabling practitioners to grasp and implement fundamental concepts in ML and computer vision. * Rice leaf disease dataset [2]: This dataset is a comprehensive compilation of images representing various rice leaf diseases. This dataset is meticulously curated to train and evaluate models in the context of rice leaf disease recognition. A preprocessing stage was applied to each image to ensure consistency in dimensions and data quality. During this process, all images were resized to a uniform dimension of \(224\times 224\) pixels, involving the standardization of dimensions without preserving the original aspect ratio. Additionally, normalization was performed to bring pixel values within the standardized range of \([0,1]\). The dataset is strategically divided into training, validation, and test sets, maintaining an 80%/10%/10% ratio, respectively. This division facilitates effective training and evaluation of ML models on distinct subsets of the data.