Search

The real-time monitoring of wheat phenology variations among different varieties and their adaptive responses to environmental conditions is essential for advancing breeding efforts and improving cultivation management. Many remote sensing efforts have been made to relieve the challenges of key phenophase detection. However, existing solutions are not accurate enough to discriminate adjacent phenophases with subtle organ changes, and they are not real-time, such as the vegetation index curve-based methods relying on entire growth stage data after the experiment was finished. Furthermore, it is key to improving the efficiency, scalability, and availability of phenological studies. This study proposes a two-stage deep learning framework called PhenoNet for the accurate, efficient, and real-time classification of key wheat phenophases. PhenoNet comprises a lightweight encoder module (PhenoViT) and a long short-term memory (LSTM) module. The performance of PhenoNet was assessed using a well-labeled, multi-variety, and large-volume dataset (WheatPheno). The results show that PhenoNet achieved an overall accuracy (OA) of 0.945, kappa coefficients (Kappa) of 0.928, and F1-score (F1) of 0.941. Additionally, the network parameters (Params), number of operations measured by multiply-adds (MAdds), and graphics processing unit memory required for classification (Memory) were 0.889 million (M), 0.093 Giga times (G), and 8.0 Megabytes (MB), respectively. PhenoNet outperformed eleven state-of-the-art deep learning networks, achieving an average improvement of 3.7% in OA, 5.1% in Kappa, and 4.1% in F1, while reducing average Params, MAdds, and Memory by 78.4%, 85.0%, and 75.1%, respectively. The feature visualization and ablation analysis explained that PhenoNet mainly benefited from using time-series information and lightweight modules. Furthermore, PhenoNet can be effectively transferred across years, achieving a high OA of 0.981 using a two-stage transfer learning strategy. Furthermore, an extensible web platform that integrates WheatPheno and PhenoNet and ensures that the work done in this study is accessible, interoperable, and reusable has been developed (https://phenonet.org/). [ABSTRACT FROM AUTHOR]

Plant phenomics is a new avenue for linking plant genomics and environmental studies, thereby improving plant breeding and management. Remote sensing techniques have improved high-throughput plant phenotyping. However, the accuracy, efficiency, and applicability of three-dimensional (3D) phenotyping are still challenging, especially in field environments. Light detection and ranging (lidar) provides a powerful new tool for 3D phenotyping with the rapid development of facilities and algorithms. Numerous efforts have been devoted to studying static and dynamic changes of structural and functional phenotypes using lidar in agriculture. These progresses also improve 3D plant modeling across different spatial–temporal scales and disciplines, providing easier and less expensive association with genes and analysis of environmental practices and affords new insights into breeding and management. Beyond agriculture phenotyping, lidar shows great potential in forestry, horticultural, and grass phenotyping. Although lidar has resulted in remarkable improvements in plant phenotyping and modeling, the synthetization of lidar-based phenotyping for breeding and management has not been fully explored. We identify three main challenges in lidar-based phenotyping development: 1) developing low cost, high spatial–temporal, and hyperspectral lidar facilities, 2) moving into multi-dimensional phenotyping with an endeavor to generate new algorithms and models, and 3) embracing open source and big data. [ABSTRACT FROM AUTHOR]

• A well-labeled dataset for stem-leaf semantic and instance segmentation was constructed manually, which contains 5460 point clouds using a self-designed data sampling and augmentation method, i.e., 3D Edge-Preserving Sampling (3DEPS). • A point-based dual-function network for stem-leaf semantic and instance segmentation was proposed, which achieves average Precision of 92.49% in semantic segmentation and average Mean Precision of 83.30% in instance segmentation of three different species of crops. • The PlantNet outperforms state-of-the-art deep learning networks including PointNet, PointNet++, SGPN, and ASIS with an average improvement of 5.56% in Precision on semantic segmentation and an average improvement of 22.18% in Mean Precision on instance segmentation. The accurate plant organ segmentation is crucial and challenging to the quantification of plant architecture and selection of plant ideotype. The popularity of point cloud data and deep learning methods make plant organ segmentation a feasible and cutting-edge research. However, current plant organ segmentation methods are specially designed for only one species or variety, and they rarely perform semantic segmentation (stems and leaves) and instance segmentation (individual leaf) simultaneously. This study innovates a dual-function deep learning neural network (PlantNet) to realize semantic segmentation and instance segmentation of two dicotyledons and one monocotyledon from point clouds. The innovations of the PlantNet include a 3D Edge-Preserving Sampling (3DEPS) strategy for preprocessing input points, a Local Feature Extraction Operation (LFEO) module based on dynamic graph convolutions, and a semantic-instance Feature Fusion Module (FFM). The semantic segmentation results of tobacco, tomato, and sorghum in average Precision , Recall , F1-score , and IoU reached 92.49%, 92.04%, 92.13%, and 85.86%, respectively; and the instance segmentation results in the mean precision (mPrec) , the mean recall (mRec) , the mean coverage (mCov) , and the mean weighted coverage (mWCov) reached 83.30%, 74.08%, 78.62%, and 84.38%, respectively. The PlantNet outperformed state-of-the-art deep learning networks including PointNet, PointNet++, SGPN, and ASIS, which achieved an average improvement of 5.56%, 3.58%, 4.78%, and 6.74% in Precision, Recall, F1-score, IoU on semantic segmentation, and an average improvement of 22.18%, 16.37%, 14.13%, and 13.35% in mPrec , mRec , mCov , and mWCov on instance segmentation. In addition, the effectiveness of 3DEPS, sub-modules, and the new loss function were verified separately by the ablation analysis, in which the removal of any of them can result in a segmentation performance decline of up to 2.0% on average quantitative measures. This study may contribute to the development of plant phenotype extraction, ideotype selection, and intelligent agriculture. [ABSTRACT FROM AUTHOR]