Pinpointing Plant Breeds And Their Correlated Ailments From Leaf Depictions Employing Deep Learning And Convolutional Neural Networks

Abstract

The early and precise identification of plant diseases is essential for ensuring agricultural sustainability, minimizing crop loss, and improving food security. Plant diseases often manifest as visual symptoms on leaves, making leaf image analysis a vital tool in modern plant pathology. Traditional image processing techniques have been used extensively for this purpose; however, these methods are often limited by their reliance on handcrafted features and lack of adaptability to complex visual variations. With the emergence of deep learning, especially Convolutional Neural Networks (CNNs), significant improvements have been made in the field of image-based classification. CNNs automatically learn hierarchical feature representations from raw images, outperforming conventional techniques in both accuracy and robustness. This paper addresses the challenge of automatic disease detection by leveraging the power of deep learning to analyze leaf images and classify plant diseases efficiently. In this study, we propose an effective deep learning-based approach for plant species identification and disease classification using the GoogLeNet architecture, a sophisticated CNN model known for its depth and computational efficiency. To enhance performance and reduce training time, transfer learning is employed by fine-tuning a pre-trained GoogLeNet model on a dataset containing images of healthy and diseased plant leaves. The proposed system achieves a classification accuracy of 85.04% across four distinct disease categories, demonstrating its capability in recognizing complex patterns in leaf textures and colors.