A single view leaf reconstruction method based on the fusion of ResNet and differentiable render in plant growth digital twin system

Abstract

In modern agriculture, plant growth digital twin system helps breeders monitor plant growth, increase yield, and provide growth management advice. Research on the single view leaf 3D reconstruction in digital twin systems has achieved relative success. However, in traditional single-view reconstruction algorithms, the leaf reconstruction often contains the problems of low precision, achieving complexity, and slow speed, making it difficult for recovering three-dimensional information about leaves. Consequently, the reconstruction precision is significantly reduced, which further affects the accuracy of single-view leaf 3D reconstruction. In response to this problem, this study proposed a single-view leaf reconstruction approach in plant growth digital twin systems based on deep learning. The method in this paper mainly fuses the advantages of ResNet and differentiable rendering, and the model is used for further enhancing feature extraction capability and reconstruction precision. Finally, the experiment presented in this paper suggests that the method allows for the 3D reconstruction of plant leaves with different shapes using a single view. Moreover, the experiment results show that the F-Score, CD, EMD reached 76.192, 0.808, and 3.567. Compared with other models, the proposed model in this study has higher reconstruction accuracy, 3D evaluation indicators, and prediction results, providing important ideas and methods for recovering the leaves from a single view in a plant growth digital twin system.

Plant phenotyping relevance

単一画像から植物葉の3D形状を再構成する手法を開発・評価しており、葉の形態表現型取得が研究の中心である。

Code and data availability

公開本文の所在を確認できませんでした。非公開または購読が必要な可能性があります。

No evidence-backed public reproduction asset is currently recorded.