关键词: 物候相机, 绿度指数, 物候指标, 产量估测, 归一化植被指数

Abstract: Aims With the development of precision agriculture, real-time monitoring and accurate prediction of crop growth status and yield have become crucial. However, traditional monitoring methods are often limited by factors such as time, manpower, and cost, making it difficult to meet the needs of modern agricultural management. The aim of this study was to use PhenoCam to monitor the growth of artificial forage and extract phenological metrics to estimate the yield. Methods GCC, NDVI and LCI were extracted from phenological photos and UAV images of silage maize and oat under different fertility treatments. The phenological metrics were calculated by fitting and combining the vegetation growth curves of GCC. The relationship between phenological metrics and plant height and yield of two kinds of artificial forage was studied by linear fitting, and the best fitting model of phenological metrics to harvest characteristic was constructed. Important findings (1) Nitrogen application rate drove the phenological metrics and harvest characteristic of artificial forage. The growing period length of maize silage under high fertilizer treatment and oat under medium fertilizer treatment was the longest (68?5 days and 59?1 days), and the corresponding yield was the highest (1903.22?284.62 kg/ mu and 345.38?129.27 kg/ mu, respectively). (2) GCC and LCI had the best correlation with the plant height of artificial forage, especially before GCC reached the peak POP (pre-POP R2 was 0.86 and 0.49), and GCC had the smallest deviation in dynamic capture of plant height of silage maize. (3) The phenological metrics of artificial herbage can effectively predict the measured maximum plant height and final yield (R2 of silage maize were 0.328 and 0.829, and R2 of oat were 0.995 and 0.935). This study confirmed the effectiveness of phenological metrics based on Phenocam and UAV images in monitoring the growth status and yield of artificial forage grass.

Key words: Phenocam, GCC, phenological metrics, yield estimation, normalized difference vegetation index (NDVI)