Rezvan Hassan, Valadan Zoej Mohammad Javad, Youssefi Fahimeh, Ghaderpour Ebrahim
Department of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology, Tehran 19967-15433, Iran.
Institute of Artificial Intelligence, USX, Shaoxing University, 508 West Huancheng Road, Yuecheng District, Shaoxing 312000, China.
Sensors (Basel). 2025 Sep 5;25(17):5546. doi: 10.3390/s25175546.
This research presents a fully automated two-step method for segmenting rice seedlings and assessing their health by integrating spectral, morphological, and textural features. Driven by the global need for increased food production, the proposed method enhances monitoring and control in agricultural processes. Seedling locations are first identified by the excess green minus excess red index, which enables automated point-prompt inputs for the segment anything model to achieve precise segmentation and masking. Morphological features are extracted from the generated masks, while spectral and textural features are derived from corresponding red-green-blue imagery. Health assessment is conducted through anomaly detection using a one-class support vector machine, which identifies seedlings exhibiting abnormal morphology or spectral signatures suggesting stress. The proposed method is validated by visual inspection and Silhouette score, confirming effective separation of anomalies. For segmentation, the proposed method achieved mean dice scores ranging from 72.6 to 94.7. For plant health assessment, silhouette scores ranged from 0.31 to 0.44 across both datasets and various growth stages. Applied across three consecutive rice growth stages, the framework facilitates temporal monitoring of seedling health. The findings highlight the potential of advanced segmentation and anomaly detection techniques to support timely interventions, such as pruning or replacing unhealthy seedlings, to optimize crop yield.
本研究提出了一种全自动的两步法，通过整合光谱、形态和纹理特征来分割水稻幼苗并评估其健康状况。受全球对增加粮食产量的需求驱动，该方法增强了农业生产过程中的监测和控制。首先通过过量绿色减去过量红色指数确定幼苗位置，这为“分割一切”模型提供自动点提示输入，以实现精确分割和掩膜。从生成的掩膜中提取形态特征，而光谱和纹理特征则从相应的红-绿-蓝图像中获取。通过使用一类支持向量机进行异常检测来进行健康评估，该方法可识别出表现出异常形态或光谱特征（表明存在压力）的幼苗。所提出的方法通过目视检查和轮廓分数进行验证，证实了对异常情况的有效分离。对于分割，该方法的平均骰子分数在72.6至94.7之间。对于植物健康评估，两个数据集和不同生长阶段的轮廓分数在0.31至0.44之间。该框架应用于水稻连续三个生长阶段，有助于对幼苗健康进行时间监测。研究结果突出了先进的分割和异常检测技术在支持及时干预（如修剪或替换不健康幼苗）以优化作物产量方面的潜力。
