A Comprehensive Review of Remote Sensing Technology for Mass-Flowering Crops Extraction

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 18; pp. 14382 - 14405
• Main Authors: Meng, Qingji, Zang, Shuying, Zhu, Bingxue, Song, Kaishan, Li, Miao, Sun, Li
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Asia; Classification; Crop identification; Crop planting; Crops; Data integration; Data mining; Data sources; Deep learning; Earth; Extraction; Flowering; Flowering plants; Information retrieval; Landsat; Laser radar; Machine learning; mass-flowering crops; Monitoring; Normalized difference vegetative index; Radar data; Rapeseed; Remote sensing; remote sensing (RS); Soft sensors; Spatiotemporal data; Statistical analysis; Statistical methods; sunflower; Vegetation

Precise and reliable crop evaluations hold significant value in ensuring agricultural security and fostering agricultural progress. Using the flowering characteristics of crops during their growth period to accurately identify crops is a hot research direction in the field of agricultural remote sen...