International Journal of Science, Engineering and TechnologyAuthors: Karmbir, Kunal Rawat, Akshit Pandey, Cherry
Abstract: The precise and timely identification of drought stress in plants is vital for optimizing crop yield and water management. Although preliminary results on deep learning models have indicated promising results in processing thermal images for stress identification, previous studies have faced significant challenges due to small dataset sizes, single-crop evaluation, and highly controlled and manipulated experimental conditions. This paper proposes a robust and scalable deep learning framework for multi-class drought stress identification based on thermal image analysis. To bridge the gap between current research works, we propose a large- scale dataset for thermal image analysis under natural environmental conditions. We compare our proposed framework with state-of-the-art models and propose a comprehensive framework for real-time deployment on edge platforms such as UAVs.
DOI: http://doi.org/10.5281/zenodo.21786618
