Phenotyping

faCRSA: an automated pipeline for high-throughput analysis of crop root system architecture

Abstract

Optimizing root system architecture (RSA) is essential for plants because of its critical role in acquiring water and nutrients from the soil. However, the subterranean nature of roots complicates the measurement of RSA traits. Recently developed rhizobox methods allow for the rapid acquisition of root images. Nevertheless, effective and precise approaches for extracting RSA features from these images remain underdeveloped. Deep learning (DL) technology can enhance image segmentation and facilitate RSA trait extraction. However, comprehensive pipelines that integrate DL technologies into image-based root phenotyping techniques are still scarce, hampering their implementation. To address this challenge, we present a reproducible pipeline (faCRSA) for automated RSA traits analysis, consisting of three modules: (1) the RSA traits extraction module functions to segment soil-root images and calculate RSA traits. A lightweight convolutional neural network (CNN) named RootSeg was proposed for efficient and accurate segmentation; (2) the data storage module, which stores image and text data from other modules; and (3) the web application module, which allows researchers to analyze data online in a user-friendly manner. The correlation coefficients (R2) of total root length, root surface area, and root volume calculated from faCRSA and manually measured results were 0.96**, 0.97**, and 0.93**, respectively, with root mean square errors (RMSE) of 8.13 cm, 1.68 cm2, and 0.05 cm3, processed at a rate of 9.74 seconds per image, indicating satisfying accuracy. faCRSA has also demonstrated satisfactory performance in dynamically monitoring root system changes under various stress conditions, such as drought or waterlogging. The detailed code and deployable package of faCRSA are provided for researchers with the potential to replace manual and semi-automated methods.

SenNet: A dual-branch image semantic segmentation network for wheat senescence evaluation and high-yielding variety screening

Abstract

Wheat is one of the three primary staple crops globally, with the senescence of its leaves having a direct effect on yield. However, conventional senescence evaluation methods are mainly based on visual scoring, which are subjective, time-consuming, and hamper the investigation of mechanisms between senescence process and yield formation. High-throughput image-based plant phenotyping techniques offer a promising approach. However, extracting senescence-related semantic information from images presents challenges, including blurred edge segmentation, inadequate characterization of senescence features, and interference from complex field environments. Therefore, this study proposes a dual-branch image senescence segmentation model (SenNet), which integrates edge priors and local–global attention mechanisms, including local–global hierarchical attention mechanisms, gated convolution, and positional encoding modules. First, a wheat senescence dynamics image dataset (19530 images) was constructed, comprising 509 wheat varieties from a two-year and two-replicate field experiments. Then, the SenNet model achieved senescence image segmentation for various wheat varieties, enabling senescence dynamics analysis and high-yielding variety screening. The results showed that: 1) The mean Intersection over Union (mIoU) of the SenNet model was 95.41 %, which represented a 4.01 % improvement over the average mIoU of seven state-of-the-art models. 2) The contributions of the local–global hierarchical attention mechanism, gated convolution, and positional encoding module to the accuracy improvement of SenNet were 3.15 %, 1.62 %, and 1.03 %, respectively. 3) SenNet can be transferred across years and locations. The mIoU accuracy of the SenNet across locations is 96.01 %. Furthermore, the model trained in 2023 can be transferred to 2022 and 2024, achieving mIoU accuracies of 93.75 % and 93.27 %. 4) High-yielding varieties typically experience a later onset of senescence and faster senescence in later stages. Based on the senescence law, this study further constructed new dynamic traits of senescence (e.g., AreaUnderCurve). Leveraging the random forest-based yield prediction (R^2^ = 0.68) from the dynamic traits, high-yielding varieties were screened with an average precision, recall, F1 score, and accuracy of 81 %, 79 %, 80 %, and 87 %, respectively. This study provides an efficient method for monitoring senescence dynamics and predicting yield, offering new insights into the screening of high-yielding varieties.

Enhancing wheat crop physiology monitoring through spectroscopic analysis of stomatal conductance dynamics

Abstract

Monitoring in-vivo stomatal conductance (gs) dynamics is essential for predicting crop water usage and yield sensitivity in response to climate change. Leaf and canopy spectroscopy offer a non-destructive method for gs monitoring; however, the underlying mechanisms connecting leaf spectra with stomatal anatomical and behavioral traits, and their subsequent impacts on gs, remain underexplored. In this study, we conducted a wheat field trial, collecting comprehensive measurements of stomatal anatomical (i.e., size, density) and behavioral (i. e., opening ratio, pore area) traits by a customized, high-resolution microscope, leaf spectra via a handheld spectroradiometer, and gs via a handheld AP4 Leaf Porometer across various genotypes, nitrogen treatments, growth stages, and diurnal environments. We observed substantial gs variability, with stomatal anatomical and behavioral traits jointly accounting for 79% of this variability. We further examined the relationship between leaf spectra and stomatal traits/conductance using a partial least square regression (PLSR) model and discovered that a single PLSR spectral model accurately predicted the variability of each of these traits and gs across our datasets. Furthermore, we demonstrated a strong correspondence between spectral variations resulting from gs and spectral alternation induced by stomatal anatomical and behavioral traits. By analyzing the diurnal association between spectral and gs variability, we revealed important biophysical mechanisms underlying relationships among spectra, stomatal anatomical and behavioral traits, and gs. Collectively, our findings highlight the potential of leaf spectroscopy in advancing crop physiology monitoring, contributing to enhanced food security and sustainability.

Anti-gravity stem-seeking restoration algorithm for maize seed root image phenotype detection

Abstract

Root phenotype detection is key to cultivating seeds with excellent traits, and requires a complete root image. However, soil occlusion, uneven lighting, and other factors cause broken points and segments in the root image. To solve this problem, an anti-gravity stem-seeking (AGSS) root image restoration algorithm is proposed in this paper to repair root images and extract root phenotype information for different resistant maize seeds. First, the obtained root image was processed using uniform illumination, grayscale, binarization, and morphological filtering to separate it from the background. Subsequently, a root skeleton map was generated using a thinning algorithm for pixel-level image processing, and the Taproot junction G was obtained. Subsequently, root pixel coordinates were obtained by traversing the root skeleton map. All root-segment endpoint coordinates were obtained using the endpoint judgment rule and stored in the endpoint list. The lateral and primary root endpoints were separated based on the lateral root judgment rule, and stored in the side root and primary root endpoint lists, respectively. Subsequently, the primary root endpoints were processed and fitted in the order short to long using arbitrary-two-endpoint spacing until all breakpoints of the primary root were found. The coordinates of the top endpoints of each principal root were obtained. Finally, the top endpoint was connected to point G based on the Bezier curve-fitting method to achieve complete root repair. The proposed AGSS root image restoration algorithm was applied to detect the root systems of maize with different resistances and wheat to evaluate its performance against the standard dataset. The results indicated a detection accuracy of greater than 90% for root taproot length and diameter. It was also found that maize drought resistance was positively correlated with root length and diameter, but negatively with the lateral root number. In contrast, the waterlogging and salt resistance traits of maize were positively correlated with the number of lateral roots. In conclusion, the proposed AGSS root image restoration algorithm can quickly and effectively repair root images, is suitable for different resistance evaluations of maize seeds, and is conducive to the detection of root phenotypes. Compared with deep learning methods, this algorithm displays advantages of fast repair, low hardware platform requirements, and less requirement of training images. The algorithm is highly suitable for deploying in small embedded systems, with broad application prospects.