Context: Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons.

Aims: This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons.

Methods: Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images.

Key results: (1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R² = 0.79) during the pre-flowering stage; NGRDI (R² = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages.

Conclusion: UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean.

Implication: These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.

背景：配备红绿蓝 (RGB) 摄像头的无人机 (UAV) 越来越多地用作农业系统中的监控工具。这是首次使用无人机和跨多个季节的图像分析对绿豆（ Vigna radiata (L.) Wilzcek）进行实地研究。

目的：本研究旨在验证使用无人机图像来评估绿豆多个季节的生长参数（生物量、叶面积、光截获分数和辐射利用效率）。

方法：在2018/19年夏季和2019/20年春夏季进行了三个播种期的田间试验。每两周收集一次生长参数，以匹配整个作物生长过程中的无人机飞行。植被覆盖分数 (FVC) 和计算植被指数：植被提取颜色指数 (CIVE)、绿叶指数 (GLI)、过量绿色指数 (ExG)、归一化绿红差指数 (NGRDI) 和可见光抗大气指数 (VARI) ）是从无人机正射马赛克图像生成的。

主要成果：（1）利用无人机获取的RGB图像可以准确估算开花前阶段的绿豆生物量；(2) 与绿豆花后阶段相比，在花前阶段观察到基于无人机的 RGB 图像与地面数据之间的关系更准确；(3) 花前期FVC与生物量强相关( R ^{2 = 0.79)；}NGRDI ( R ²  = 0.86) 在开花前阶段的三个实验中表现出更好的直接预测生物量的能力。

结论：基于无人机的 RGB 图像是一种很有前途的技术，可以取代手动光拦截测量并预测生物量，特别是在绿豆的早期生长阶段。

意义：这些发现可以帮助研究人员评估农艺策略并考虑针对不同季节条件的必要管理实践。