Plant Phenomics最新文献

Efficient measurement of photosynthetic traits, such as the maximum carboxylation rate of Rubisco (Vcmax) and electron transport rate (Jmax), is essential for advancing research and breeding aimed at enhancing crop productivity. Traditional methods are time-intensive, which limits their scalability. Remote sensing presents an opportunity for estimating these traits; however, it often lacks an affordable platform for effective spatial mapping, a critical aspect of phenotyping. This study explored the use of unmanned aerial vehicle (UAV) multispectral data to estimate and spatially map photosynthetic traits in tea chrysanthemums during the branching and budding stages under an open canopy. Over six field experiments across varieties conducted in 2022-2023, we captured canopy reflectance using UAV-mounted multispectral sensors, calculated spectral indices, and measured the photosynthetic traits of the upper leaves using a portable photosynthesis system. The results indicated that certain indices, particularly those incorporating green and red-edge bands, effectively estimated photosynthetic traits, with the simplified canopy chlorophyll content index (SCCCI) yielding the most accurate Vcmax estimates (R² ​= ​0.52) and the chlorophyll vegetation index (CVI) providing the best estimates for Jmax (R² ​= ​0.38). The integration of variable selection with partial least squares regression (PLSR) modeling further enhanced the precision of the model (Vcmax: R² ​= ​0.70; Jmax: R² ​= ​0.63). Our findings demonstrate that UAV-acquired multispectral data can effectively map photosynthetic traits with high spatial resolution, establishing it as a valuable tool for rapid phenotyping and spatial assessment of photosynthetic capacity in crop fields.

Precisely identifying missing virus-free strawberry mother plants in nutrient pots post-transplantation is crucial for optimizing seedling management and maximizing yields in glass greenhouses. Thus, we present an automated method for detecting and counting missing seedlings based on SSP-MambaNet. Challenges in this process include the variable growth morphology of seedlings and complex environmental conditions in the greenhouse. Our approach starts with SPDFFA (Spatial-to-Depth Feature Fusion Attention) to enhance feature representation while retaining critical information, ensuring the preservation of key details. Additionally, the multi-scale CVSSB(Complex Visual State Space) and CVSSB-E(Expanded CVSSB) modules combine multi-scale and multi-directional spatial features, augmenting the model's capacity to recognize inter-image dependencies. Secondly, the MPDIoU is a novel loss function to tackle the optimization challenge of bounding boxes with similar shapes but different sizes, which enhances the accuracy of localizing strawberry seedlings and nutrient pots. Finally, Distance Intersection over Union is utilized for establishing a belongingness relationship between strawberry seedlings and pots, accurately identifying missing seedlings and counting the corresponding pots. Experimental results demonstrate that SSP-MambaNet achieves 94.9 %in average precision, 92.8 ​% in recall rate,88.1 ​% in precision, and 90.4 ​% F1 score for strawberry seedlings and pots. It outperforms the YOLOv7 by 4.7 ​% in average precision, and 2.6 ​% in recall rate while reducing 66.7 f/s in FPS. Furthermore, the proposed method shows 94.29 ​% accuracy in detecting missing seedlings and 97.14 ​% accuracy in counting nutrient pots with missing seedlings. These results showcase its effectiveness in improving overall seedling quality and providing timely replanting guidance in glass greenhouses.

The freshness phenotype of fruit and vegetables is a critical determinant of consumer satisfaction, selection, and public health, which plays a pivotal role in postharvest quality management. This paper presents a review of the definition and detection techniques used to assess and maintain this vital freshness phenotype. Advanced intelligent packaging technologies, that incorporate sensors, indicators, and data carrier systems, and their roles in dynamically monitoring the freshness phenotype during storage and transportation are discussed. The integration of nondestructive testing (NDT) methods such as near-infrared spectroscopy (NIR), hyperspectral imaging (HSI), machine vision, and light detection and ranging (LiDAR) offers real-time, precise assessments of the freshness phenotype without compromising the integrity of the produce. By understanding the underlying mechanisms of the fruit and vegetable freshness phenotype, this paper discusses the definition, detection technologies, and gaps that require further research. The integration of advanced quantitative models with NDT and intelligent packaging solutions has the potential to reduce food waste. This advancement will lead to better quality control, extended shelf life, and increased consumer confidence in fresh produce, driving innovation and application within the food industry.

High-throughput phenotyping has a tremendous capacity to advance our understanding of plant biology. Integrating growth parameters with information on a plant's physiology through multispectral imaging can provide a holistic picture of its health status and its responses to environmental stressors. Furthermore, the screening of large-scale populations of genotypes or germplasms, using such platforms, can identify lines with desirable traits to help feed a growing world population in the background of climate change. Here, we present a novel platform, the Multispectral Automated Dynamic Imager (MADI), which combines visible and near-infrared reflectance, thermal imaging, and chlorophyll fluorescence for the dynamic monitoring of growth, leaf temperature, and photosynthetic efficiency. Additionally, we have integrated and validated a fluorescence-based parameter to non-destructively assess chlorophyll content. The utility of the MADI system was demonstrated through four case studies in which lettuce and Arabidopsis plants were exposed to various abiotic stress conditions. We demonstrate that plant compactness is a useful marker for stress responses, including drought, and could serve as a biomarker to study plant hormones. Additionally, we observed the phenomenon of chlorophyll hormesis under salt stress, a rather poorly understood process. In conclusion, the MADI is a multifunctional, adaptable system that can be employed to gain insights into plant stress responses and help to improve agricultural practices. It can be used primarily for rosette-growing species, such as leafy greens, which represent a significant portion of cultivated crops worldwide.

Norway spruce (Picea abies Karst L.) is one of the most ecologically and economically significant tree species in Europe, accounting for nearly half of the continent's forest economic value. However, drought is a significant stress factor associated with increasing Norway spruce mortality across Europe. Provenance trials, a traditional approach to assess adaptive variation, face limitations stemming from the finite number of sites, seed sources involved, and their required labor-intensive nature. In response, we developed a comprehensive multisensor high-throughput phenotyping method and integrated it with metabolomics, transcriptomics, and anatomical analyses to study the drought stress responses in two climatically contrasting but geographically proximal provenances at the seedling stage by exposing them to drought stress for a period of 21 days. Based on more than 50 physiological and growth-related traits assessed by the phenotyping platform, it was possible to characterize early and late drought stress responses. Consistent with phenotypic data, mRNA-seq, and metabolic profiles revealed apparent differences between treatments. While during the drought stress the metabolic data indicated an increased production of ABA, α-tocopherol, zeaxanthin, lutein, and phenolics, mRNA-seq showed modulation of related pathways and downregulation of photosystem transcripts. Although drought responses were largely conserved between the two provenances, they differed phenotypically in traits related to the activation of re-oxidation of the plastoquinone pool, and molecularly in transcriptional and phenolic profiles. In conclusion, our study demonstrates the potential of the high-throughput phenotyping approach for evaluating drought stress adaptation in Norway spruce thus accelerating the screening and selection of best adapted provenances.

The breeding of high-yield wheat varieties is needed to ensure food security. Accurately and rapidly predicting wheat yield at the plot level via UAVs would enable breeders to identify meaningful genotypic variations and select superior lines, thus accelerating the selection of climate-adapted high-yield varieties. Although current prediction models have already utilized multivariate time series data, these models usually adopt a simple concatenation operation to embed all the raw data, resulting in low prediction accuracy. To address these limitations, we propose an improved transformer-based wheat yield prediction model with a variate-independent tokenization approach. The proposed variate-independent tokenization approach facilitates the embedding of 14 vegetation indices and 28 morphological traits via the feature dimension, enabling the learning of variate-centric representations. We also apply a multivariate attention mechanism to evaluate the contribution of each variate and capture the multivariate correlation. Extensive experiments are conducted to verify the effectiveness of our model, including comparisons across 3 nitrogen treatments, 2 years, and 56 wheat varieties. We also compare our model with state-of-the-art approaches. The experimental results indicate that our model achieves the optimal prediction performance, with an R² of 0.862, surpassing those of the classical recurrent neural network and transformer variants. We also confirm that combining both the vegetation indices and morphological traits is advantageous over using single-source data for the prediction task, achieving an approximately 4 ​% prediction performance gain. In conclusion, this study provides a novel approach for utilizing an improved transformer model and multivariate time series data to quantitatively predict plot-level wheat yield, thus enabling the rapid selection of high-yield varieties for breeding.

This article describes an immersive virtual reality reconstruction tool for root system architectures from 3D scans of soil columns. In practical scenarios, experimental conditions will be adapted to fit the need of the data analysis pipeline, including sieving and drying the soil before scanning. Based on previous reports of automatic systems that do not represent what experts would annotate, we developed a virtual reality system to assist with the extraction of root systems in cases in which automated approaches fall short of expert knowledge. The aim of the present study is to evaluate whether our immersive method is superior to classical annotation approaches when tested on synthetic data sets using untrained participants. Our laboratory user study consists of evaluating the root extractions of participants, along with their rating on central user experience and usability measures. We show significant improvement in F1 score across conditions (noisy or clear data) as well as an improved usability. Our study highlights that using virtual reality in root extraction improves accuracy, and we perform an in-depth evaluation of biases that occur when users trace roots in soil volumes.

The unmanned aerial vehicle (UAV) platform has emerged as a powerful tool in soybean (Glycine max (L.) Merr.) breeding phenotype research due to its high throughput and adaptability. However, previous studies have predominantly relied on statistical features like vegetation indices and textures, overlooking the crucial structural information embedded in the data. Feature fusion has often been confined to a one-dimensional exponential form, which can decouple spatial and spectral information and neglect their interactions at the data level. In this study, we leverage our team's cross-circling oblique (CCO) route photography and Structure-from-Motion with Multi-View Stereo (SfM-MVS) techniques to reconstruct the three-dimensional (3D) structure of soybean canopies. Newly point cloud deep learning models SoyNet and SoyNet-Res were further created with two novel data-level fusion that integrate spatial structure and color information. Our results reveal that incorporating RGB color and vegetation index (VI) spectral information with spatial structure information, leads to a significant reduction in root mean square error (RMSE) for yield estimation (22.55 ​kg ​ha^-1) and an improvement in F1-score for five-class lodging discrimination (0.06) at S7 growth stage. The SoyNet-Res model employing multi-task learning exhibits better accuracy in both yield estimation (RMSE: 349.45 ​kg ​ha^-1) when compared to the H2O-AutoML. Furthermore, our findings indicate that multi-task deep learning outperforms single-task learning in lodging discrimination, achieving an accuracy top-2 of 0.87 and accuracy top-3 of 0.97 for five-class. In conclusion, the point cloud deep learning method exhibits tremendous potential in learning multi-phenotype tasks, laying the foundation for optimizing soybean breeding programs.

Ensuring food security has become a global challenge owing to climate change and population growth. High-throughput phenotyping can effectively drive crop genetic enhancement, which can potentially solve food crisis. Phenotyping robot is an essential part of crop ground phenotyping information monitoring, although there are challenges such as the inability to adjust the fixed track width, poor load capacity of the detection robotic arm, and inability to fuse information in real-time. This study reports a phenotyping robot with a gantry-style chassis featuring an adjustable wheeltrack (1400-1600 ​mm) to adapt to different row spacing arrangements and reduced damage, and function effectively in both dry field and paddy field environments. A six-degree-of-freedom sensor gimbal with high payload capacity is also developed to enable precise height (1016-2096 ​mm) and angle adjustments. Additionally, this study introduces an enhanced method for data acquisition from multiple imaging sensors through registration and fusion using Zhang's calibration and feature point extraction algorithm, calculating a homography matrix for high-throughput data collection at fixed positions and heights. The experimental validation results demonstrate that the RMSE of the registration algorithm does not exceed 3 pixels. The gimbal data strongly correlated with that of a handheld instrument data (r² ​> ​0.90). The robot is practical, reliable, and fully functional, offering a solid theoretical foundation and equipment support for high-throughput phenotyping.

Roses are renowned for their ornamental value and are available in a wide range of colors and shapes due to extensive breeding and ease of hybridization. During post-harvest, roses are highly susceptible to fungal decay by the grey mould fungus Botrytis cinerea. No complete resistance to Botrytis is known, and several studies indicate a quantitative nature of resistance. This implies that multiple genes are involved, and that each contribution may only have a slight effect on resistance. Accurate, fast, and objective phenotyping discriminating between minor effects would be essential for breeding selections and discovering novel resistance- or susceptibility genes against Botrytis. Spotibot, a phenotyping software available both as a web application and mobile application, utilizes deep learning and mobile computing for automatically detecting Botrytis lesions on rose petals making it highly applicable for breeding selection. The algorithm can measure petal area (mm²), lesion area (mm²), lesion diameter (mm) and lesion to petal ratio. The deep learning-based algorithm features a coarse-to-fine segmentation approach using two instance segmentation models. The first model (F ₁-score ​= ​0.99) detects and segments each petal, while the second model (F ₁-score ​= ​0.96) detects and segments Botrytis lesions on each petal. Spearman Rank correlation analysis showed a high near-monotonic relationship between human-assessed subjective scores and the objective data generated using Spotibot. An analysis of variance indicated that objective variables reveal more and stronger differences between rose genotypes than using subjective data alone. This is the first work on developing a fast and user-friendly application for image analysis of rose petals to screen Botrytis resistance and susceptibility.