Accurate and reliable predictions of biomass yield are important for decision-making in pasture management including fertilization, pest control, irrigation, grazing, and mowing. The possibilities for monitoring pasture growth and developing prediction models have greatly been expanded by advances in machine learning (ML) using optical sensing data. To facilitate the development of prediction models, an understanding of how ML techniques affect performance is needed. Therefore, this review examines the adoption of ML-based optical sensing for predicting the biomass yield of managed grasslands. We carried out a systematic search for English-language journal articles published between 2015-01-01 and 2022-10-26. Three coders screened 593 unique records of which 91 were forwarded to the full-text assessment. Forty-three studies were eligible for inclusion. We determined the adoption of techniques for collecting input data, preprocessing, and training prediction models, and evaluating their performance. The results show (1) a broad array of vegetation indices and spectral bands obtained from various optical sensors, (2) an emphasis focus on feature selection to cope with high-dimensional sensor data, (3) a low reporting rate of unitless performance metrics other than R², (4) higher variability of R² for models trained on sensor data of larger distance from the pasture sward, and (5) the need for greater comparability of study designs and results. We submit recommendations for future research and enhanced reporting that can help reduce barriers to the integration of evidence from studies.

准确可靠的生物量产量预测对于牧场管理（包括施肥、病虫害防治、灌溉、放牧和割草）的决策非常重要。使用光学传感数据的机器学习 (ML) 的进步极大地扩展了监测牧场生长和开发预测模型的可能性。为了促进预测模型的开发，需要了解机器学习技术如何影响性能。因此，本综述研究了采用基于机器学习的光学传感来预测管理草地的生物量产量。我们对 2015 年 1 月 1 日至 2022 年 10 月 26 日期间发表的英文期刊文章进行了系统检索。三位编码员筛选了 593 条独特记录，其中 91 条被转发至全文评估。四十三项研究有资格纳入。我们决定采用收集输入数据、预处理和训练预测模型并评估其性能的技术。结果显示（1）从各种光学传感器获得了广泛的植被指数和光谱带，（2）重点关注特征选择以应对高维传感器数据，（3）无单位性能指标的报告率较低以外R ² ，(4)对于距离牧场较远距离的传感器数据训练的模型，R ²的变异性较高，以及 (5) 研究设计和结果需要更大的可比性。我们为未来的研究和加强报告提出建议，这有助于减少整合研究证据的障碍。