Plant Species Identification Using Transfer Learning

Abstract

Plants are essential for the survival of human life and maintaining ecological balance by providing oxygen, food, medicine, raw materials, and habitat support. They contribute significantly to climate regulation, air and water purification, and the preservation of biodiversity. Accurate plant species identification is therefore essential for biodiversity conservation, agricultural development, environmental protection, and the discovery of new medicinal resources. However, traditional plant identification methods rely heavily on expert knowledge and manual observation, making the process inefficient, require extensive manual effort, and vulnerable to subjective errors. Recent developments in deep learning have and computer vision, automated plant species identification has emerged as an effective alternative. This study explores the use of transfer learning frameworks for building an accurateand efficient plant species identification system. A pre-trained VGG16 convolutional neural network is utilized to extract meaningful features from plant images and classify species with high accuracy. The deep architecture of VGG16, consisting of convolutional and pooling layers, enables the model to capture complex visual patterns while reducing dimensionality. The use of transfer learning significantly reduces computational efficiency while maintaining strong classification performance. Experimental results demonstrate that deep learning combined with transfer learning provides a scalable, reliable, and accurate solution for plant species identification, highlighting its suitability for real-world applications in agriculture, botany, and environmental monitoring.