The Chlorophyll Fluorescence Parameter Fv/Fm Correlates with Loss of Grain Yield after Severe Drought in Three Wheat Genotypes Grown at Two CO2 Concentrations.

Three genotypes of wheat grown at two CO2 concentrations were used in a drought experiment, where water was withheld from the pots at anthesis until stomatal conductance (gs) dropped below 10% of the control and photosynthesis (A) approached zero. The genotypes had different leaf area (Gladius < LM19 < LM62) and while photosynthesis and shoot growth were boosted by elevated CO2, the water use and drying rate were more determined by canopy size than by stomatal density and conductance. The genotypes responded differently regarding number of fertile tillers, seeds per spike and 1000 kernel weight and, surprisingly, the largest genotype (LM62) with high water use showed the lowest relative decrease in grain yield. The maximum photochemical efficiency of photosystem II (Fv/Fm) was only affected on the last day of the drought when the stomata were almost closed although some variation in A was still seen between the genotypes. A close correlation was found between Fv/Fm and % loss of grain yield. It indicates that the precise final physiological stress level measured by Fv/Fm at anthesis/early kernel filling could effectively predict percentage final yield loss, and LM62 was slightly less stressed than the other genotypes, due to only a small discrepancy in finalising the drying period. Therefore, Fv/Fm can be used as a proxy for estimating the yield performance of wheat after severe drought at anthesis.

RootPainter: deep learning segmentation of biological images with corrective annotation.

Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without a machine-learning background. We present RootPainter, an open-source graphical user interface based software tool for the rapid training of deep neural networks for use in biological image analysis. We evaluate RootPainter by training models for root length extraction from chicory (Cichorium intybus L.) roots in soil, biopore counting, and root nodule counting. We also compare dense annotations with corrective ones that are added during the training process based on the weaknesses of the current model. Five out of six times the models trained using RootPainter with corrective annotations created within 2 h produced measurements strongly correlating with manual measurements. Model accuracy had a significant correlation with annotation duration, indicating further improvements could be obtained with extended annotation. Our results show that a deep-learning model can be trained to a high accuracy for the three respective datasets of varying target objects, background, and image quality with < 2 h of annotation time. They indicate that, when using RootPainter, for many datasets it is possible to annotate, train, and complete data processing within 1 d.

Digging roots is easier with AI.

The scale of root quantification in research is often limited by the time required for sampling, measurement, and processing samples. Recent developments in convolutional neural networks (CNNs) have made faster and more accurate plant image analysis possible, which may significantly reduce the time required for root measurement, but challenges remain in making these methods accessible to researchers without an in-depth knowledge of machine learning. We analyzed root images acquired from three destructive root samplings using the RootPainter CNN software that features an interface for corrective annotation for easier use. Root scans with and without non-root debris were used to test if training a model (i.e. learning from labeled examples) can effectively exclude the debris by comparing the end results with measurements from clean images. Root images acquired from soil profile walls and the cross-section of soil cores were also used for training, and the derived measurements were compared with manual measurements. After 200 min of training on each dataset, significant relationships between manual measurements and RootPainter-derived data were noted for monolith (R2=0.99), profile wall (R2=0.76), and core-break (R2=0.57). The rooting density derived from images with debris was not significantly different from that derived from clean images after processing with RootPainter. Rooting density was also successfully calculated from both profile wall and soil core images, and in each case the gradient of root density with depth was not significantly different from manual counts. Differences in root-length density (RLD) between crops with contrasting root systems were captured using automatic segmentation at soil profiles with high RLD (1-5 cm cm-3) as well with low RLD (0.1-0.3 cm cm-3). Our results demonstrate that the proposed approach using CNN can lead to substantial reductions in root sample processing workloads, increasing the potential scale of future root investigations.

Core-labelling technique (CLT): a novel combination of the ingrowth-core method and tracer technique for deep root study.

BACKGROUND: Ingrowth-core method is a useful tool to determine fine root growth of standing crops by inserting root-free soil in mesh-bags for certain period of time. However, the root density observed by the method does not directly explain the nutrient uptake potential of crop plants as it varies over soil depth and incubation time. We have inserted an access-tube up to 4.2 m of soil depth with openings directly under crop plants, through which ingrowth-cores containing labelled soil with nutrient tracers were installed, called core-labelling technique (CLT). The main advantage of CLT would be its capacity to determine both root density and root activity from the same crop plants in deep soil layers. We tested the validity of the new method using a model crop species, alfalfa (Medicago sativa) against three depth-levels (1.0, 2.5 and 4.2 m), three sampling spots with varying distance (0-0.36, 0.36-0.72 and > 5 m from core-labelled spot), two sampling times (week 4 and 8), and two plant parts (young and old leaves) under two field experiments (spring and autumn). RESULTS: Using CLT, we were able to observe both deep root growth and root activity up to 4.2 m of soil depth. Tracer concentrations revealed that there was no sign of tracer-leakage to adjacent areas which is considered to be advantageous over the generic tracer-injection. Root activity increased with longer incubation period and tracer concentrations were higher in younger leaves only for anionic tracers. CONCLUSIONS: Our results indicate that CLT can lead to a comprehensive deep root study aiming at measuring both deep root growth and root activity from the same plants. Once produced and installed, the access-tubes and ingrowth-cores can be used for a long-term period, which reduces the workload and cost for the research. Therefore, CLT has a wide range of potential applications to the research involving roots in deep soil layers, which requires further confirmation by future experiments.