Crop Breeding
Crop breeding aims to improve agricultural productivity and sustainability by developing superior crop varieties. Current research emphasizes using machine learning, particularly deep learning models like U-Nets and Transformers, along with computer vision techniques, to optimize breeding programs, improve precision agriculture practices (e.g., weed detection and crop monitoring via satellite and drone imagery), and enhance yield prediction through multi-modal data fusion. These advancements offer significant potential for increasing crop yields, reducing resource use, and improving the efficiency and resilience of agricultural systems globally.
Papers
Can SAM recognize crops? Quantifying the zero-shot performance of a semantic segmentation foundation model on generating crop-type maps using satellite imagery for precision agriculture
Rutuja Gurav, Het Patel, Zhuocheng Shang, Ahmed Eldawy, Jia Chen, Elia Scudiero, Evangelos Papalexakis
Precision Agriculture: Crop Mapping using Machine Learning and Sentinel-2 Satellite Imagery
Kui Zhao, Siyang Wu, Chang Liu, Yue Wu, Natalia Efremova
Localise to segment: crop to improve organ at risk segmentation accuracy
Abraham George Smith, Denis Kutnár, Ivan Richter Vogelius, Sune Darkner, Jens Petersen
Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection
Shivam K Panda, Yongkyu Lee, M. Khalid Jawed