Deep Learning Approaches for Disease Detection in Groundnut Crops using CNN Models

Abstract

A major oilseed crop grown in tropical and subtropical parts of the world, groundnuts are a major crop in India. In the sixteenth century, groundnuts were likely transported from Brazil to West Africa, later making their way to India and the African east coast. According to earlier research, various strategies are employed to prevent diseases of groundnut leaves. The main methods include artificial intelligence (AI), machine learning (ML), convolutional neural networks (CNN), and more. Several CNN techniques for leaf disease identification and methodology will be employed in this study. Different CNN models, such as MobileNet, VGG-16, and EfficientNet, are compared to determine which model is most frequently used to identify leaf disease. The accuracy and precision will be computed and presented as a result of utilizing the dataset.