Phenotyping of plants in competitive but controlled environments: a study of drought response in transgenic wheat

Frost Damage Index: The Antipode of Growing Degree Days

Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios.

Wheat-Net: An Automatic Dense Wheat Spike Segmentation Method Based on an Optimized Hybrid Task Cascade Model

Precise segmentation of wheat spikes from a complex background is necessary for obtaining image-based phenotypic information of wheat traits such as yield estimation and spike morphology. A new instance segmentation method based on a Hybrid Task Cascade model was proposed to solve the wheat spike detection problem with improved detection results. In this study, wheat images were collected from fields where the environment varied both spatially and temporally. Res2Net50 was adopted as a backbone network, combined with multi-scale training, deformable convolutional networks, and Generic ROI Extractor for rich feature learning. The proposed methods were trained and validated, and the average precision (AP) obtained for the bounding box and mask was 0.904 and 0.907, respectively, and the accuracy for wheat spike counting was 99.29%. Comprehensive empirical analyses revealed that our method (Wheat-Net) performed well on challenging field-based datasets with mixed qualities, particularly those with various backgrounds and wheat spike adjacence/occlusion. These results provide evidence for dense wheat spike detection capabilities with masking, which is useful for not only wheat yield estimation but also spike morphology assessments.

Impact of the overexpression of the StDREB1 transcription factor on growth parameters, yields, and chemical composition of tubers from greenhouse and field grown potato plants

Potato plants are often exposed to biotic and abiotic stresses that negatively impact their growth, development, and yield. Plants respond to different stresses by inducing large numbers of stress-responsive genes, which can be either functional or regulatory genes. Among regulatory genes, Dehydration Responsive Element Binding (DREB) genes are considered as one of the main groups of transcriptional regulators. The overexpression of these factors in several transgenic plants leads to enhancement of abiotic stress tolerance. However, a number of reports showed that the overexpression of DREB factors under control of constitutive promoter, affects their morphology and production. Therefore, it becomes interesting to evaluate the effect of the overexpression of this StDREB1 transcription factor on plant growth, morphology, yield and tuber composition under both greenhouse and field culture conditions. To our knowledge, there is no available data on the effect of DREBA-4 overexpression on potato plants morphology and yield. Indeed, most studies focused on DREB genes from A-1 and A-2 groups for other plant species. Our results showed that StDREB1, a A-4 group of DREB gene from potato (Solanum tuberosum L.), overexpressing plants did not show any growth retardation. On the contrary, they seem to be more vigorous, and produced higher tuber weight in greenhouse and field culture than the wild type (WT) plants. Moreover, the overexpression of StDREB1 transcription factor seemed to have an effect on tuber quality in terms of dry matter, starch contents and reducing sugars in comparison to the WT tubers. These data suggest that the StDREB1 gene from A-4 group of DREB subfamily can be a good candidate in potato breeding for stress tolerance.

Detection and analysis of wheat spikes using Convolutional Neural Networks

BACKGROUND

Field phenotyping by remote sensing has received increased interest in recent years with the possibility of achieving high-throughput analysis of crop fields. Along with the various technological developments, the application of machine learning methods for image analysis has enhanced the potential for quantitative assessment of a multitude of crop traits. For wheat breeding purposes, assessing the production of wheat spikes, as the grain-bearing organ, is a useful proxy measure of grain production. Thus, being able to detect and characterize spikes from images of wheat fields is an essential component in a wheat breeding pipeline for the selection of high yielding varieties.

RESULTS

We have applied a deep learning approach to accurately detect, count and analyze wheat spikes for yield estimation. We have tested the approach on a set of images of wheat field trial comprising 10 varieties subjected to three fertilizer treatments. The images have been captured over one season, using high definition RGB cameras mounted on a land-based imaging platform, and viewing the wheat plots from an oblique angle. A subset of in-field images has been accurately labeled by manually annotating all the spike regions. This annotated dataset, called SPIKE, is then used to train four region-based Convolutional Neural Networks (R-CNN) which take, as input, images of wheat plots, and accurately detect and count spike regions in each plot. The CNNs also output the spike density and a classification probability for each plot. Using the same R-CNN architecture, four different models were generated based on four different datasets of training and testing images captured at various growth stages. Despite the challenging field imaging conditions, e.g., variable illumination conditions, high spike occlusion, and complex background, the four R-CNN models achieve an average detection accuracy ranging from 88 to 94 % across different sets of test images. The most robust R-CNN model, which achieved the highest accuracy, is then selected to study the variation in spike production over 10 wheat varieties and three treatments. The SPIKE dataset and the trained CNN are the main contributions of this paper.

CONCLUSION

With the availability of good training datasets such us the SPIKE dataset proposed in this article, deep learning techniques can achieve high accuracy in detecting and counting spikes from complex wheat field images. The proposed robust R-CNN model, which has been trained on spike images captured during different growth stages, is optimized for application to a wider variety of field scenarios. It accurately quantifies the differences in yield produced by the 10 varieties we have studied, and their respective responses to fertilizer treatment. We have also observed that the other R-CNN models exhibit more specialized performances. The data set and the R-CNN model, which we make publicly available, have the potential to greatly benefit plant breeders by facilitating the high throughput selection of high yielding varieties.

Land-based crop phenotyping by image analysis: Accurate estimation of canopy height distributions using stereo images

In this paper we report on an automated procedure to capture and characterize the detailed structure of a crop canopy by means of stereo imaging. We focus attention specifically on the detailed characteristic of canopy height distribution—canopy shoot area as a function of height—which can provide an elaborate picture of canopy growth and health under a given set of conditions. We apply the method to a wheat field trial involving ten Australian wheat varieties that were subjected to two different fertilizer treatments. A novel camera self-calibration approach is proposed which allows the determination of quantitative plant canopy height data (as well as other valuable phenotypic information) by stereo matching. Utilizing the canopy height distribution to provide a measure of canopy height, the results compare favourably with manual measurements of canopy height (resulting in an R² value of 0.92), and are indeed shown to be more consistent. By comparing canopy height distributions of different varieties and different treatments, the methodology shows that different varieties subjected to the same treatment, and the same variety subjected to different treatments can respond in much more distinctive and quantifiable ways within their respective canopies than can be captured by a simple trait measure such as overall canopy height.

Breeding next generation tree fruits: technical and legal challenges

The new plant breeding technologies (NPBTs) have recently emerged as powerful tools in the context of 'green' biotechnologies. They have wide potential compared to classical genetic engineering and they are attracting the interest of politicians, stakeholders and citizens due to the revolutionary impact they may have on agriculture. Cisgenesis and genome editing potentially allow to obtain pathogen-resistant plants or plants with enhanced qualitative traits by introducing or disrupting specific genes in shorter times compared to traditional breeding programs and by means of minimal modifications in the plant genome. Grapevine, the most important fruit crop in the world from an economical point of view, is a peculiar case for NPBTs because of the load of cultural aspects, varietal traditions and consumer demands, which hinder the use of classical breeding techniques and, furthermore, the application of genetic engineering to wine grape cultivars. Here we explore the technical challenges which may hamper the application of cisgenesis and genome editing to this perennial plant, in particular focusing on the bottlenecks of the Agrobacterium-mediated gene transfer. In addition, strategies to eliminate undesired sequences from the genome and to choose proper target sites are discussed in light of peculiar features of this species. Furthermore is reported an update of the international legislative frameworks regulating NPBT products which shows conflicting positions and, in the case of the European Union, a prolonged lack of regulation.

Dehydration responsive element binding transcription factors and their applications for the engineering of stress tolerance

Dehydration responsive element binding (DREB) factors or CRT element binding factors (CBFs) are members of the AP2/ERF family, which comprises a large number of stress-responsive regulatory genes. This review traverses almost two decades of research, from the discovery of DREB/CBF factors to their optimization for application in plant biotechnology. In this review, we describe (i) the discovery, classification, structure, and evolution of DREB genes and proteins; (ii) induction of DREB genes by abiotic stresses and involvement of their products in stress responses; (iii) protein structure and DNA binding selectivity of different groups of DREB proteins; (iv) post-transcriptional and post-translational mechanisms of DREB transcription factor (TF) regulation; and (v) physical and/or functional interaction of DREB TFs with other proteins during plant stress responses. We also discuss existing issues in applications of DREB TFs for engineering of enhanced stress tolerance and improved performance under stress of transgenic crop plants.