Deep Learning-Based Classification of Rice Varieties Using Image Analysis: A Comparative Study of Neural Network Architectures

Rice is quite rich in genetic variation that has led to thousands of varieties each with unique patterns, shapes, and colors. The work of the research uses image analysis together with machine learning approaches to classify five types of rice: Arborio, Basmati, Ipsala, Yasemin, and Karacadag. The paper contains a data set of 75,000 images and an advanced architecture for a neural network that will improve quality grading of rice and help push agricultural application computer vision technology forward. The authors describe a deep learning framework capable of classifying various types of rice by using agricultural data from different collections of rice images. The model is demonstrated through experiments on activities related to image classification. The evaluation performance measures used were accuracy, precision, and recall. The experiments gave a peak accuracy of 97.59% on the dataset in question. These results underscore the fact that deep learning models can augment both the accuracy and efficiency of skin cancer diagnosis while emphasizing the necessity to utilize data from an extensive and diverse population for the training of the models. Furthermore, the higher effectiveness of AlexNet architecture compared with LeNet architecture suggests that more complex and deeper convolutional neural network architectures would perhaps be appropriate for cancer prediction.