Sweet cherry (Prunus avium) is an economically significant fruit species in the genus Prunus. However, in contrast to other important fruit trees in this genus, only one draft genome assembly is available for sweet cherry, which was assembled using only Illumina short-read sequences. The incompleteness and low quality of the current sweet cherry draft genome limit its use in genetic and genomic studies. A high-quality chromosome-scale sweet cherry reference genome assembly is therefore needed. A total of 65.05 Gb of Oxford Nanopore long reads and 46.24 Gb of Illumina short reads were generated, representing ~190x and 136x coverage, respectively, of the sweet cherry genome. The final de novo assembly resulted in a phased haplotype assembly of 344.29 Mb with a contig N50 of 3.25 Mb. Hi-C scaffolding of the genome resulted in eight pseudochromosomes containing 99.59% of the bases in the assembled genome. Genome annotation revealed that more than half of the genome (59.40%) was composed of repetitive sequences, and 40,338 protein-coding genes were predicted, 75.40% of which were functionally annotated. With the chromosome-scale assembly, we revealed that gene duplication events contributed to the expansion of gene families for salicylic acid/jasmonic acid carboxyl methyltransferase and ankyrin repeat-containing proteins in the genome of sweet cherry. Four auxin-responsive genes (two GH3s and two SAURs) were induced in the late stage of fruit development, indicating that auxin is crucial for the sweet cherry ripening process. In addition, 772 resistance genes were identified and functionally predicted in the sweet cherry genome. The high-quality genome assembly of sweet cherry obtained in this study will provide valuable genomic resources for sweet cherry improvement and molecular breeding.

Leaf water potential is a critical indicator of plant water status, integrating soil moisture status, plant physiology, and environmental conditions. There are few tools for measuring plant water status (water potential) in situ, presenting a critical barrier for developing appropriate phenotyping (measurement) methods for crop development and modeling efforts aimed at understanding water transport in plants. Here, we present the development of an in situ, minimally disruptive hydrogel nanoreporter (AquaDust) for measuring leaf water potential. The gel matrix responds to changes in water potential in its local environment by swelling; the distance between covalently linked dyes changes with the reconfiguration of the polymer, leading to changes in the emission spectrum via Förster Resonance Energy Transfer (FRET). Upon infiltration into leaves, the nanoparticles localize within the apoplastic space in the mesophyll; they do not enter the cytoplasm or the xylem. We characterize the physical basis for AquaDust’s response and demonstrate its function in intact maize (Zea mays L.) leaves as a reporter of leaf water potential. We use AquaDust to measure gradients of water potential along intact, actively transpiring leaves as a function of water status; the localized nature of the reporters allows us to define a hydraulic model that distinguishes resistances inside and outside the xylem. We also present field measurements with AquaDust through a full diurnal cycle to confirm the robustness of the technique and of our model. We conclude that AquaDust offers potential opportunities for high-throughput field measurements and spatially resolved studies of water relations within plant tissues. Significance Gaps in our ability to document local water relations in leaves compromise our ability to build complete models of leaf and plant function and our understanding of ecophysiological phenomena, such as response and adaptation to drought. Macroscopically, leaf water potential has been shown to impact vegetative growth and yield, susceptibility to disease, and, in extreme drought, plant viability, making it a promising candidate trait to improve water-use efficiency in plants. In this paper, we present a nanoscale sensor (AquaDust) that provides minimally disruptive measurements of water potential in leaves of intact plants at high spatial and temporal resolution. This creates opportunities for improving our understanding of the mechanisms coupling variations in water potential to biological and physical processes.

Estimation of crop biophysical and biochemical characteristics is the key element for crop growth monitoring with remote sensing. With the application of unmanned aerial vehicles (UAV) as a remote sensing platform worldwide, it has become important to develop general estimation models, which can interpret remote sensing data of crops by different sensors and in different agroclimatic regions into comprehensible agronomy parameters. Leaf chlorophyll content (LCC), which can be measured as a soil plant analysis development (SPAD) value using a SPAD-502 Chlorophyll Meter, is one of the important parameters that are closely related to plant production. This study compared the estimation of rice (Oryza sativa L.) LCC in two different regions (Ningxia and Shanghai) using UAV-based spectral images. For Ningxia, images of rice plots with different nitrogen and biochar application rates were acquired by a 125-band hyperspectral camera from 2016 to 2017, and a total of 180 samples of rice LCC were recorded. For Shanghai, images of rice plots with different nitrogen application rates, straw returning, and crop rotation systems were acquired by a 5-band multispectral camera from 2017 to 2018, and a total of 228 samples of rice LCC were recorded. The spectral features of LCC in each study area were analyzed and the results showed that the rice LCC in both regions had significant correlations with the reflectance at the green, red, and red-edge bands and 8 vegetation indices such as the normalized difference vegetation index (NDVI). The estimation models of LCC were built using the partial least squares regression (PLSR), support vector regression (SVR), and artificial neural network (ANN) methods. The PLSR models tended to be more stable and accurate than the SVR and ANN models when applied in different regions with R² values higher than 0.7 through different validations. The results demonstrated that the rice canopy LCC in different regions, cultivars, and different types of sensor-based data shared similar spectral features and could be estimated by general models. The general models can be implied to a wider geographic extent to accurately quantify rice LCC, which is helpful for growth assessment and production forecasts.

The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R² values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.

Recognizing plant cultivars reliably and efficiently can benefit plant breeders in terms of property rights protection and innovation of germplasm resources. Although leaf image-based methods have been widely adopted in plant species identification, they seldom have been applied in cultivar identification due to the high similarity of leaves among cultivars. Here, we propose an automatic leaf image-based cultivar identification pipeline called MFCIS (Multi-feature CombinedCultivarIdentificationSystem), which combines multiple leaf morphological features collected by persistent homology and a convolutional neural network (CNN). Persistent homology, a multiscale and robust method, was employed to extract the topological signatures of leaf shape, texture, and venation details. A CNN-based algorithm, the Xception network, was fine-tuned for extracting high-level leaf image features. For fruit species, we benchmarked the MFCIS pipeline on a sweet cherry (Prunus aviumL.) leaf dataset with >5000 leaf images from 88 varieties or unreleased selections and achieved a mean accuracy of 83.52%. For annual crop species, we applied the MFCIS pipeline to a soybean (Glycine max L. Merr.) leaf dataset with 5000 leaf images of 100 cultivars or elite breeding lines collected at five growth periods. The identification models for each growth period were trained independently, and their results were combined using a score-level fusion strategy. The classification accuracy after score-level fusion was 91.4%, which is much higher than the accuracy when utilizing each growth period independently or mixing all growth periods. To facilitate the adoption of the proposed pipelines, we constructed a user-friendly web service, which is freely available athttp://www.mfcis.online.