用卫星影像和时间序列分析，自动识别番茄田里的恶性寄生杂草。
Branched Broomrape Detection in Tomato Farms Using Satellite Imagery and Time-Series Analysis
- 融合多时相卫星数据与神经网络，提取植物生理指标进行检测。
- 模型在测试集上达到87%准确率，召回率达92%，可有效定位受侵地块。
- 适合农业监测、精准种植及病虫害预警系统开发者参考。
分枝列当（Phelipanche ramosa (L.) Pomel）是一种缺乏叶绿素的寄生植物，通过吸取番茄植株养分导致产量损失最高达80%。其大部分生命周期潜伏于地下，单株可产生超过20万颗种子，且存活期长达20年，因此早期发现至关重要。本研究提出端到端流程，利用哨兵-2遥感影像与时间序列分析，在加州番茄种植区识别受列当侵染的农田。基于农户报告划定感兴趣区域，并筛选云量低于10%的影像。处理12个光谱波段及太阳-传感器几何参数，计算20种植被指数（如NDVI、NDMI），并借助神经网络结合实地与合成数据校准，推导出叶面积指数、叶绿素含量、冠层吸收光合有效辐射比例（FAPAR）及植被覆盖度等5项植物性状。冠层叶绿素含量的变化趋势用于划分移栽至收获期，结合积温日数对齐物候。将植被像素分割后，使用长短期记忆网络（LSTM）在48个积温日时间点上的18,874个像素上进行训练。模型在训练集上准确率达88%，测试集为87%，精度0.86，召回率0.92，F1值0.89。特征重要性分析表明NDMI、冠层叶绿素含量、FAPAR和叶绿素红边指数贡献最大，符合侵染生理机制。结果表明，卫星驱动的时间序列建模在规模化检测番茄寄生胁迫方面具有巨大潜力。
原文摘要 · Abstract (English)
Branched broomrape (Phelipanche ramosa (L.) Pomel) is a chlorophyll-deficient parasitic plant that threatens tomato production by extracting nutrients from the host, with reported yield losses up to 80 percent. Its mostly subterranean life cycle and prolific seed production (more than 200,000 seeds per plant, viable for up to 20 years) make early detection essential. We present an end-to-end pipeline that uses Sentinel-2 imagery and time-series analysis to identify broomrape-infested tomato fields in California. Regions of interest were defined from farmer-reported infestations, and images with less than 10 percent cloud cover were retained. We processed 12 spectral bands and sun-sensor geometry, computed 20 vegetation indices (e.g., NDVI, NDMI), and derived five plant traits (Leaf Area Index, Leaf Chlorophyll Content, Canopy Chlorophyll Content, Fraction of Absorbed Photosynthetically Active Radiation, and Fractional Vegetation Cover) using a neural network calibrated with ground-truth and synthetic data. Trends in Canopy Chlorophyll Content delineated transplanting-to-harvest periods, and phenology was aligned using growing degree days. Vegetation pixels were segmented and used to train a Long Short-Term Memory (LSTM) network on 18,874 pixels across 48 growing-degree-day time points. The model achieved 88 percent training accuracy and 87 percent test accuracy, with precision 0.86, recall 0.92, and F1 0.89. Permutation feature importance ranked NDMI, Canopy Chlorophyll Content, FAPAR, and a chlorophyll red-edge index as most informative, consistent with the physiological effects of infestation. Results show the promise of satellite-driven time-series modeling for scalable detection of parasitic stress in tomato farms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。