Species Classification of Plant Seedlings Using Deep Convolutional Neural Networks

Abstract

Species classification of plant seedlings plays a vital role in modern agriculture, biodiversity conservation, and precision farming. This project leverages the power of deep convolutional neural networks (CNNs) to accurately classify plant seedlings into their respective species based on visual features. By training the CNN model on a curated dataset of seedling images, the system learns to identify subtle variations in shape, texture, and color that differentiate one species from another. Furthermore, to address the critical issue of plant health, the project integrates the InceptionV3 architecture for the detection and classification of leaf-based plant diseases. This dual approach enables early diagnosis of diseases through leaf image analysis, facilitating timely intervention. The proposed system not only enhances the efficiency of species classification but also provides a scalable solution for real-time plant monitoring in agricultural settings. This paper discusses the design, implementation, and performance of the deep learning models, while also exploring challenges such as dataset variability and model generalization. Future advancements may integrate this technology with IoT devices and smart farming platforms to further improve agricultural productivity and sustainability.