Tomato seedling stem and leaf segmentation method based on an improved ResNet architecture.

Abstract

Introduction: The phenotypic traits of tomato plants reflect their growth status, and investigating these characteristics can improve tomato production. Traditional deep learning models face challenges such as excessive parameters, high complexity, and susceptibility to overfitting in point cloud segmentation tasks. To address these limitations, this paper proposes a lightweight improved model based on the ResNet architecture. Methods: The proposed network optimizes the traditional residual block by integrating bottleneck modules and downsampling techniques. Additionally, by combining curvature features and geometric characteristics, we custom-designed specialized convolutional layers to enhance segmentation accuracy for tomato stem and leaf point clouds. The model further employs adaptive average pooling to improve generalization and robustness. Results: Experimental validation demonstrated that the optimized model achieved a training accuracy of 95.11%, a 3.26% improvement over the traditional ResNet18 model. Testing time was reduced to 4.02 seconds (25% faster than ResNet18's 5.37 seconds). Phenotypic parameter extraction yielded high correlation with manual measurements, with coefficients of determination (R²) of 0.941 (plant height), 0.752 (stem diameter), 0.945 (leaf area), and 0.943 (leaf inclination angle). The root mean square errors (RMSE) were 0.506, 0.129, 0.980, and 3.619, respectively, while absolute percentage errors (APE) remained below 6% (1.965%-5.526%). Discussion: The proposed X-ResNet model exhibits superior segmentation performance, demonstrating high accuracy in phenotypic trait extraction. The strong correlations and low errors between extracted and manually measured data validate the feasibility of 3D point cloud technology for tomato phenotyping. This study provides a valuable benchmark for plant phenotyping research, with significant practical and theoretical implications.

Plant phenotyping relevance

トマトの3D点群から茎・葉を分割し、草丈・茎径・葉面積・葉傾斜角を抽出する手法を開発・検証しており、植物表現型取得が研究の中心である。

Code and data availability

The paper's tomato seedling point cloud dataset (480 plants, annotated in CloudCompare) is paper-specific phenotyping data, but it is not publicly available; the Data Availability Statement requires contacting the author. No public code, model checkpoints, or dataset URLs are provided.

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