Convolutional Neural Network for Identifying Common Ragweed From Digital Images
Year: 2018
Type: other
Abstract: As an invasive and allergic plant species, common ragweed, Ambrosia artemisiifolia L., poses threats to the environment and human health in Europe and Asia. A monitoring system for ragweed would be highly recommended to understand its propagation and combat the plant more effectively. Here, we propose a model for identifying ragweed plants out of images with cluttered field scenes based on new developments in the data science (deep-learning). We show that the use of a pre-existing convolutional neural network (AlexNet) can be used to train successfully an image classifier for ragweed identification. Convolutional neural network have a more suitable and scalable network architecture for handling images than simple feed-forward neural networks. The purpose of the paper is to describe the model and show first classification results based on an image set collected in a ragweed infested field after harvesting.
Authors: S. Michael, U. Anton, G. Antje, Dammer Karl-Heinz
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4406589645