Organ segmentation and phenotypic information extraction of cotton point clouds based on the CotSegNet network and machine learning

The precise segmentation of crop organs plays a crucial role in optimizing crop cultivation strategies and enhancing yield potential. This study proposes a novel deep learning network, CotSegNet, which enables precise and non-destructive segmentation of cotton organs facilitating the extraction of phenotypic characteristics. In CotSegNet, an improved attention mechanism known as CGLUConvFormer is designed. This mechanism significantly improves segmentation accuracy by emphasizing important features while diminishing redundant information. Furthermore, CotSegNet integrates the SegNext attention mechanism. This mechanism facilitates the efficient extraction and integration of multi-scale features, thereby significantly enhancing the ability of CotSegNet to comprehend and segment point cloud data. To address issues related to leaf adhesion and coplanarity that lead to over-segmentation problems, this study proposes an improved region-growing algorithm. This algorithm enhances the accuracy of leaf instance segmentation through the incorporation of distance constraints. In comparative experiments with five advanced deep learning networks (PointNet, PointNet++, DGCNN, SPoTr and CurveNet), CotSegNet demonstrated outstanding performance. Its Precision, Recall, F1-score, and IoU reached 95.06 %, 93.32 %, 94.61 %, and 89.80 %, respectively. The experimental results demonstrated that the proposed method effectively extracted the phenotypic parameters of stem height, leaf length, leaf width, and leaf area in cotton plants. These measurements exhibited a high degree of consistency with manual assessments, yielding determination coefficients of 0.947, 0.948, 0.955, and 0.961 for each parameter respectively. The corresponding root mean square errors were recorded as 0.852 cm, 0.492 cm, 0.551 cm, and 1.674 cm² respectively. The research findings demonstrate that this approach offers essential technical support for the collection and analysis of high throughput phenotyping data in field crops.