面向追踪湿地松养分胁迫响应的高通量表型系统研究

High-throughput Phenotyping Systems for Tracking Nutrient Stress Response of Pinus Elliottii

摘要:

植物表型分析是制约农林现代化发展的关键瓶颈之一。传统表型分析方法存在效率低下、操作复杂等局限，难以实现大规模、动态监测植物在环境胁迫下的生理响应。随着高通量表型技术的快速发展，多源传感器数据融合已成为研究植物健康与胁迫适应的重要手段。然而，现有系统难以应对植株高度变化不一、不同生长阶段表型特征变异大的现象，导致采集设备适应性差、作业效率有限，制约动态生理响应的精准捕捉。为此，本研究以湿地松为研究对象开展梯度养分胁迫试验（正常、轻度、重度），设计并构建了一种自走式高通量表型监测系统，该系统集成可见光、多光谱等多源成像传感器，可根据株高动态变化自动调节传感器空间位置，实现对360株样本的植物表型信息高效采集。在算法层面，系统引入了一种基于遗传算法的递归特征消除交叉验证方法（Genetic algorithm-recursive feature elimination with cross-validation，GA-RFECV），用于筛选与养分胁迫高度相关的敏感特征，并结合机器学习模型构建湿地松养分胁迫响应的分类框架。试验结果表明GA-RFECV方法提高了模型监测精度，其中随机森林（Random forest，RF）模型在验证集上的准确率、精确率、召回率和F1分数分别达到0.694、0.695、0.694、0.685。在进一步结合超参数优化后，差分进化算法（Differential evolution，DE）优化的极端梯度提升（Extreme gradient boosting，XGBoost）模型在验证集上的综合性能最优，相比于其他模型表现较好，准确率、精确率、召回率和F1分数分别提升至0.759、0.770、0.759、0.756，验证了混合特征选择与超参数优化策略在植物养分胁迫分类中的有效性。本研究提出并构建的自走式高通量表型监测系统在植物养分胁迫的精准高效追踪方面展现出较大优势，为精准施肥、抗逆品种选育、林木养分大规模监测提供了可靠的技术支撑与研究方法。

Abstract:

Plant phenotyping is one of the key bottlenecks restricting the modernization of agriculture and forestry. Traditional phenotyping methods suffer from limitations such as low efficiency and complex operation, making it difficult to achieve large-scale, dynamic monitoring of plant physiological responses under environmental stress. With the rapid development of high-throughput phenotyping technology, multi-source sensor data fusion has become an important means of studying plant health and stress adaptation. However, existing systems are unable to cope with the phenomenon of varying plant height and large phenotypic variations at different growth stages, resulting in poor adaptability of data acquisition equipment and limited operational efficiency, which restricted the accurate capture of dynamic physiological responses. To address this, a gradient nutrient stress experiment (normal, mild, and severe) on slash pine was conducted, designing and constructing a self-propelled high-throughput phenotyping monitoring system. This system integrated multi-source imaging sensors such as visible light and multispectral sensors, and can automatically adjust the spatial position of the sensors according to dynamic changes in plant height, achieving efficient collection of plant phenotypic information from 360 samples. At the algorithmic level, the system introduced a genetic algorithm-recursive feature elimination with cross-validation (GA-RFECV) method to screen sensitive features highly correlated with nutrient stress, and combined this with a machine learning model to construct a classification framework for the nutrient stress response of slash pine. Experimental results showed that the GA-RFECV method improved the model’s monitoring accuracy, with the random forest (RF) model achieving accuracy, precision, recall, and F1 score of 0.694, 0.695, 0.694, and 0.685 on the validation set, respectively. After further hyperparameter optimization, the extreme gradient boosting (XGBoost) model optimized by differential evolution (DE) achieved the best overall performance on the validation set, outperforming other models. Accuracy, precision, recall, and F1 score improved to 0.759, 0.770, 0.759, and 0.756, respectively, validating the effectiveness of the hybrid feature selection and hyperparameter optimization strategy in plant nutrient stress classification. The self-propelled high-throughput phenotypic monitoring system proposed and constructed demonstrated significant advantages in the accurate and efficient tracking of plant nutrient stress, providing reliable technical support and research methods for precision fertilization, stress-resistant variety breeding, and large-scale forest nutrient monitoring.