End-to-End Deep Learning Models for Gap Identification in Maize Fields

Rana Waqar, Željana Grbović, Maryam Khan, Nina Pajević, Dimitrije Stefanović, Vladan Filipović, Marko Panić, Nemanja Djuric; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5403-5412

We propose an approach to jointly count plants and detect gaps in maize fields using end-to-end deep-learning models. Unlike previous efforts that focused solely on plant detection our methodology also integrates the task of gap identification offering a holistic view of the state of the agricultural field. Moreover we consider different data sources in our experiments and explore the benefits of using multispectral over RGB images which are commonly used in the industry. The findings suggest that multi-task learning on multispectral images significantly outperforms other model configurations demonstrating the potential of the proposed approach for precision agriculture.