Deep Learning-Based Plant Species Identification using VGG16 and Transfer Learning: Enhancing Accuracy in Biodiversity and Agricultural Applications

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. Machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model's ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks.