Plant Methods

Background The construction and application of synthetic genetic circuits is frequently improved if gene expression can be orthogonally controlled, relative to the host. In plants, orthogonality can be achieved via the use of CRISPR-based transcription factors that are programmed to act on natural or synthetic promoters. The construction of complex gene circuits can require multiple, orthogonal regulatory interactions, and this in turn requires that the full programmability of CRISPR elements be adapted to non-natural and non-standard promoters that have few constraints on their design. Therefore, we have developed synthetic promoter elements in which regions upstream of the minimal 35S CaMV promoter are designed from scratch to interact via programmed gRNAs with dCas9 fusions that allow activation of gene expression. Results A panel of three, mutually orthogonal promoters that can be acted on by artificial gRNAs bound by CRISPR regulators were designed. Guide RNA expression targeting these promoters was in turn controlled by either Pol III (U6) or ethylene-inducible Pol II promoters, implementing for the first time a fully artificial Orthogonal Control System (OCS). Following demonstration of the complete orthogonality of the designs, the OCS was tied to cellular metabolism by putting gRNA expression under the control of an endogenous plant signaling molecule, ethylene. The ability to form complex circuitry was demonstrated via the ethylene-driven, ratiometric expression of fluorescent proteins in single plants. Conclusions The design of synthetic promoters is highly generalizable to large tracts of sequence space, allowing Orthogonal Control Systems of increasing complexity to potentially be generated at will. The ability to tie in several different basal features of plant molecular biology (Pol II and Pol III promoters, ethylene regulation) to the OCS demonstrates multiple opportunities for engineering at the system level. Moreover, given the fungibility of the core 35S CaMV promoter elements, the derived synthetic promoters can potentially be utilized across a variety of plant species.

Background Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results We developed the Multiple XL ab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the Multiple XL ab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion Multiple XL ab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.

Background Quantification of gene expression such as RNA-Seq is a popular approach to study various biological phenomena. Despite the development of RNA-Seq library preparation methods and sequencing platforms in the last decade, RNA extraction remains the most laborious and costly step in RNA-Seq of tissue samples of various organisms. Thus, it is still difficult to examine gene expression in thousands of samples. Results Here, we developed Direct-RT buffer in which homogenization of tissue samples and direct-lysate reverse transcription can be conducted without RNA purification. The DTT concentration in Direct-RT buffer prevented RNA degradation but not RT in the lysates of several plant tissues, yeast, and zebrafish larvae. Direct reverse transcription on these lysates in Direct-RT buffer produced comparable amounts of cDNA to those synthesized from purified RNA. To maximize the advantage of the Direct-RT buffer, we integrated Direct-RT and targeted RNA-Seq to develop a cost-effective, high-throughput quantification method for the expressions of hundreds of genes: DeLTa-Seq (Direct-Lysate reverse transcription and Targeted RNA-Seq). The DeLTa-Seq method could drastically improve the efficiency and accuracy of gene expression analysis. DeLTa-Seq analysis of 1056 samples revealed the temperature-dependent effects of jasmonic acid and salicylic acid in Arabidopsis thaliana . Conclusions The DeLTa-Seq method can realize large-scale studies using thousands of animal, plant, and microorganism samples, such as chemical screening, field experiments, and studies focusing on individual variability. In addition, Direct-RT is also beneficial for gene expression analysis in small tissues from which it is difficult to purify enough RNA for the experiments.

Background Genome editing is essential for crop molecular breeding. However, gene editing in turnip ( Brassica rapa var. rapa ) have not been reported owing to the very low transformation efficiency. Results In this study, we established a transformation procedure involving chemical-inducible activation of the BrrWUSa gene, which resulted in high transformation frequencies of turnip. Estradiol-inducible BrrWUSa transgenic plants were fertile and showed no obvious developmental defects. Furthermore, we used CRISPR/Cas9 gene-editing technology to edit BrrTCP4b and generated 20 BrrTCP4b -edited seedlings with an increase in leaf trichome number. Conclusion The results demonstrate that BrrWUSa improves the regeneration efficiency in turnip. The transformation procedure represents a promising strategy to improve genetic transformation and for functional characterization of genes in turnip.

Background Photosynthesis close interacts with respiration and nitrogen assimilation, which determine the photosynthetic efficiency of a leaf. Accurately quantifying the metabolic fluxes in photosynthesis, respiration and nitrogen assimilation benefit the design of photosynthetic efficiency improvement. To accurately estimate metabolic fluxes, time-series data including leaf metabolism and isotopic abundance changes should be collected under precisely controlled environments. But for isotopic labelled leaves under defined environments the, time cost of manually sampling usually longer than the turnover time of several intermediates in photosynthetic metabolism. In this case, the metabolic or physiological status of leaf sample would change during the sampling, and the accuracy of metabolomics data could be compromised. Results Here we developed an i ntegrated isotopic l abeling and freeze s ampling a pparatus (ILSA), which could finish freeze sampling automatically in 0.05 s. ILSA can not only be used for sampling of photosynthetic metabolism measurement, but also suit for leaf isotopic labeling experiments under controlled environments ([CO 2 ] and light). Combined with HPLC–MS/MS as the metabolic measurement method, we demonstrated: (1) how pool-size of photosynthetic metabolites change in dark-accumulated rice leaf, and (2) variation in photosynthetic metabolic flux between rice and Arabidopsis thaliana . Conclusions The development of ILSA supports the photosynthetic research on metabolism and metabolic flux analysis and provides a new tool for the study of leaf physiology.

Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

Background: The demand for productive economic plant resources is increasing with the continued growth of the human population. Ancient Pu'er tea trees [Camellia sinensis var. assamica (J. W. Mast.) Kitam.] are an important ecological resource with high economic value and large interests. The study intends to explore and evaluate critical drivers affecting the species' productivity, then builds formulas and indexes to make predicting the productivity of such valuable plant resources possible and applicable. Results: Our analysis identified the ideal values of the seven most important environmental variables and their relative contribution (shown in parentheses) to the distribution of ancient Pu'er tea trees: annual precipitation, ca. 1245 mm (28.73%); min temperature of coldest month, ca. 4.2 °C (18.25%); precipitation of driest quarter, ca. 47.5 mm (14.45%); isothermality, 49.9% to 50.4% (14.11%); precipitation seasonality, ca. 89.2 (6.77%); temperature seasonality, ca. 391 (4.46%); and solar radiation, 12,250 to 13,250 kJ m-2 day-1 (3.28%). Productivity was indicated by the total value (viz. fresh leaf harvested multiplied by unit price) of each tree. Environmental suitability, tree growth, and management positively affected productivity; regression weights were 0.325, 0.982, and 0.075, respectively. The degree of productivity was classified as follows: > 0.8, "highly productive"; 0.5-0.8, "productive"; 0.3-0.5, "poorly productive"; and < 0.3, "unproductive". Overall, 53% of the samples were categorized as "poorly productive" or "unproductive"; thus, the management of these regions require attention. Conclusions: This model improves the accuracy of the predictions of ancient Pu'er tea tree productivity and will aid future analyses of distribution shifts under climate change, as well as the identification of areas suitable for Pu'er tea tree plantations. Our modeling framework provides insights that facilitate the interpretation of abstract concepts and could be applied to other economically valuable plant resources.

Background Precision spraying of postemergence herbicides according to the herbicide weed control spectrum can substantially reduce herbicide input. The objective of this research was to evaluate the effectiveness of using deep convolutional neural networks (DCNNs) for detecting and discriminating weeds growing in turfgrass based on their susceptibility to ACCase-inhibiting and synthetic auxin herbicides. Results GoogLeNet, MobileNet-v3, ShuffleNet-v2, and VGGNet were trained to discriminate the vegetation into three categories based on the herbicide weed control spectrum: weeds susceptible to ACCase-inhibiting herbicides, weeds susceptible to synthetic auxin herbicides, and turfgrass without weed infestation (no herbicide). ShuffleNet-v2 and VGGNet showed high overall accuracy (≥ 0.999) and F 1 scores (≥ 0.998) in the validation and testing datasets to detect and discriminate weeds susceptible to ACCase-inhibiting and synthetic auxin herbicides. The inference time of ShuffleNet-v2 was similar to MobileNet-v3, but noticeably faster than GoogLeNet and VGGNet. ShuffleNet-v2 was the most efficient and reliable model among the neural networks evaluated. Conclusion These results demonstrated that the DCNNs trained based on the herbicide weed control spectrum could detect and discriminate weeds based on their susceptibility to selective herbicides, allowing the precision spraying of particular herbicides to susceptible weeds and thereby saving more herbicides. The proposed method can be used in a machine vision-based autonomous spot-spraying system of smart sprayers.

Background Bulk segregant analysis (BSA) can help identify quantitative trait loci (QTLs), but this may result in substantial bycatch of functionally irrelevant genes. Results Here we develop a Gene Ontology-mediated approach to zoom in on specific genes located inside QTLs identified by BSA as implicated in a continuous trait. We apply this to a novel experimental system: flowering time in the giant woody Jersey kale, which we phenotyped in four bulks of flowering onset. Our inferred QTLs yielded tens of thousands of candidate genes. We reduced this by two orders of magnitude by focusing on genes annotated with terms contained within relevant subgraphs of the Gene Ontology. A pathway enrichment test then led to the circadian rhythm pathway. The genes that enriched this pathway are attested from previous research as regulating flowering time. Within that pathway, the genes CCA1 , FT , and TSF were identified as having functionally significant variation compared to Arabidopsis . We validated and confirmed our ontology-mediated results through genome sequencing and homology-based SNP analysis. However, our ontology-mediated approach produced additional genes of putative importance, showing that the approach aids in exploration and discovery. Conclusions Our method is potentially applicable to the study of other complex traits and we therefore make our workflows available as open-source code and a reusable Docker container.

Background The application of autopilot technology is conductive to achieving path planning navigation and liberating labor productivity. In addition, the self-driving vehicles can drive according to the growth state of crops to ensure the accuracy of spraying and pesticide effect. Navigation line detection is the core technology of self-driving technology, which plays a more important role in the development of Chinese intelligent agriculture. The general algorithms for seedling line extraction in the agricultural fields are for large seedling crops. At present, scholars focus more on how to reduce the impact of crop row adhesion on extraction of crop rows. However, for seedling crops, especially double-row sown seedling crops, the navigation lines cannot be extracted very effectively due to the lack of plants or the interference of rut marks caused by wheel pressure on seedlings. To solve these problems, this paper proposed an algorithm that combined edge detection and OTSU to determine the seedling column contours of two narrow rows for cotton crops sown in wide and narrow rows. Furthermore, the least squares were used to fit the navigation line where the gap between two narrow rows of cotton was located, which could be well adapted to missing seedlings and rutted print interference. Results The algorithm was developed using images of cotton at the seedling stage. Apart from that, the accuracy of route detection was tested under different lighting conditions and in maize and soybean at the seedling stage. According to the research results, the accuracy of the line of sight for seedling cotton was 99.2%, with an average processing time of 6.63 ms per frame; the accuracy of the line of sight for seedling corn was 98.1%, with an average processing time of 6.97 ms per frame; the accuracy of the line of sight for seedling soybean was 98.4%, with an average processing time of 6.72 ms per frame. In addition, the standard deviation of lateral deviation is 2 cm, and the standard deviation of heading deviation is 0.57 deg. Conclusion The proposed rows detection algorithm could achieve state-of-the-art performance. Besides, this method could ensure the normal spraying speed by adapting to different shadow interference and the randomness of crop row growth. In terms of the applications, it could be used as a reference for the navigation line fitting of other growing crops in complex environments disturbed by shadow.

Background Clubroot of canola ( Brassica napus ), caused by the soilborne pathogen Plasmodiophora brassicae , has become a serious threat to canola production in Canada. The deployment of clubroot-resistant (CR) cultivars is the most commonly used management strategy; however, the widespread cultivation of CR canola has resulted in the emergence of new pathotypes of P. brassicae capable of overcoming resistance. Several host differential sets have been reported for pathotype identification, but such testing is time-consuming, labor-intensive, and based on phenotypic classifications. The development of rapid and objective methods that allow for efficient, cost-effective and convenient pathotyping would enable testing of a much larger number of samples in shorter times. The aim of this study was to develop two pathotyping assays, an RNase H2-dependent PCR (rhPCR) assay and a SNaPshot assay, which could quickly differentiate P. brassicae pathotypes. Results Both assays clearly distinguished between pathotype clusters in a collection of 38 single-spore isolates of P. brassicae . Additional isolates pathotyped from clubbed roots and samples from blind testing also were correctly clustered. The rhPCR assay generated clearly differentiating electrophoretic bands without non-specific amplification. The SNaPshot assay was able to detect down to a 10% relative allelic proportion in a 10:90 template mixture with both single-spore isolates and field isolates when evaluated in a relative abundance test. Conclusions This study describes the development of two rapid and sensitive technologies for P. brassicae pathotyping. The high-throughput potential and accuracy of both assays makes them promising as SNP-based pathotype identification tools for clubroot diagnostics. rhPCR is a highly sensitive approach that can be optimized into a quantitative assay, while the main advantages of SNaPshot are its ability to multiplex samples and alleles in a single reaction and the detection of up to four allelic variants per target site.

Background Salicylic acid (SA) is one of the plant hormones, which plays crucial roles in signaling transduction in plant growth, disease resistance, and leaf senescence. Arabidopsis (Arabidopsis thaliana) SA 3-hydroxylase (S3H) and 5-hydroxylase (S5H) are key enzymes which maintain SA homeostasis by catalyzing SA to 2,3-dihydroxybenzoic acid (DHBA) and 2,5-DHBA, respectively. Results SA deficient transgenic Arabidopsis lines were generated by introducing two binary vectors S5Hpro::EGFP-S3H and 35Spro::EGFP-S3H respectively, in which the expression of S3H is under the control of the S5H promoter or CaMV 35S promoter. Compared with the constitutive expression of S3H gene under the control of 35S promoter, the S3H gene under the native S5H promoter is activated by endogenous SA and results in a dynamic control of SA catabolism in a feedback mode. The SA accumulation, growth, leaf senescence, and pathogen resistance of the S5Hpro::GFP-S3H transgenic plants were investigated in parallel with NahG transgenic plants. The SA levels in the S5Hpro::EGFP-S3H transgenic plants were similar to or slightly lower than those of NahG transgenic Arabidopsis and resulted in SA deficient phenotypes. The low-SA trait of the S5Hpro::EGFP-S3H transgenic lines was inherited stably in the later generations. Conclusions Compared with NahG transgenic lines producing by-product catechol, S5Hpro::EGFP-S3H transgenic lines reduce SA levels by converting SA to a native product 2,3-DHBA for catabolism. Together, we provide new SA-deficient germplasms for the investigations of SA signaling in plant development, leaf senescence, and disease resistance.

Background: Jubaea chilensis (Molina) Baillon, is a uniquely large palm species endemic to Chile. It is under threatened status despite its use as an ornamental species throughout the world. This research seeks to identify the phyllotaxis of the species based on an original combination of non-destructive data acquisition technologies, namely Magnetic Resonance Imaging (MRI) in saplings and young individuals and Terrestrial Laser Scanning (TLS) in standing specimens, and a novel analysis methodology. Results: Two phyllotaxis parameters, parastichy pairs and divergence angle, were determined by analyzing specimens at different developmental stages. Spiral phyllotaxis patterns of J. chilensis progressed in complexity from parastichy pairs (3,2) and (3,5) in juvenile specimens and (5,3), (8,5) and (8,13) for adult specimens. Divergence angle was invariable and averaged 136.9°, close to the golden angle. Phyllotactic pattern changes associated with establishment phase, the adult vegetative and the adult reproductive phases were observed. Both technologies, MRI and TLS proved to be adequate for the proposed analysis. Conclusions: Understanding phyllotactic transitions may assist identification of developmental stages of wild J. chilensis specimens. The proposed methodology may also be useful for the study of other palm species.

Quercetin is one of the most important bioflavonoids having positive effects on biological processes and human health. Typically, it is extracted from plant matrices using conventional methods such as maceration, sonification, infusion, and Soxhlet extraction with high solvent consumption. Our study aimed to optimize the environmentally friendly carbon dioxide-based method for the extraction of quercetin from quince fruit with an emphasis on extraction yield, repeatability, and short extraction time. Results: A two-step design of experiments was used for the optimization of the key parameters affecting physico-chemical properties, including CO2/co-solvent ratio, co-solvent type, temperature, and pressure. Finally, gas expanded liquid combining CO2/ethanol/H2O in a ratio of 10/81/9 (v/v/v) provided the best extraction yield. Extraction temperature of 66 °C and pressure 22.3 MPa were the most suitable conditions after careful optimization, although both parameters did not significantly affect the process. It was confirmed by experiments in various pressure and temperature conditions and statistical comparison of obtained data. The optimized extraction procedure at a flow rate of 3 mL/min took 30 min. The repeatability of the extraction method exhibited an RSD of 20.8%. Conclusions: The optimized procedure enabled very fast extraction in 30 min using environmentally friendly sol�vents and it was successfully applied to 16 different plant samples, including 14 bulbs and 2 fruits from South Africa. The quercetin content in extracts was quantified using ultra-high-performance liquid chromatography (UHPLC) with tandem mass spectrometry. UHPLC hyphenated with high-resolution mass spectrometry was used to confirm the chemical identity of quercetin in the analyzed samples. We quantified quercetin in 11 samples of all 16 tested plants. The quercetin was found in Agapanthus praecox from the Amaryllidaceae family and its presence in this specie was reported for the first time.

Background Methods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. Results In this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava- Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas , a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. Conclusions Both image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.

Background There is great productivity of rice( Oryza sativa L. spp. japonica ) straw in China, which is a potential source of biomass for biofuel and forage. However, the high levels of lignins in rice straw limited its usage and induced the formation of agricultural waste. In order to modify the lignins contents to improve biofuel production and forage digestibility, we selected Soybean hull peroxidase (SHP) and Glyoxal oxidase (GLOX) as candidate genes to improve quality of rice straw. SHP, a class III plant peroxidase, is derived from multiple sources. It has several advantages, such as high resistance to heat, high stability under acidic and alkaline conditions, and a broad substrate range. SHP is speculated to be useful for lignin degradation. Glyoxal oxidase (GLOX) is an extracellular oxidase that can oxidize glyoxal and methylglyoxal in the extracellular medium to generate H 2 O 2 . Results In the present study, the SHP and GLOX genes in pCAMBIA3301-glycine-rich protein (GRP)-SHP-GLOX, designated the K167 vector, were optimized and introduced into rice embryos using Agrobacterium -mediated transformation. Positive transgenic rice embryos were examined using molecular, physiological, biochemical and fermentation tests. The outcomes suggested that SHP degraded lignin effectively. Conclusions This research has created a rice breeding material with normal growth and yield but stalks that are more amenable to degradation in the later stage for use in breeding rice varieties whose stalks are easily used for energy. Our results will improve the industrial and commercial applications of rice straw.

Background Elaeagnus angustifolia L. is a deciduous tree in the family Elaeagnaceae. It is widely used to study abiotic stress tolerance in plants and to improve desertification-affected land because of its ability to withstand diverse types of environmental stress, such as drought, salt, cold, and wind. However, no studies have examined the mechanisms underlying the resistance of E. angustifolia to environmental stress and its adaptive evolution. Methods Here, we used PacBio, Hi-C, resequencing, and RNA-seq to construct the genome and transcriptome of E. angustifolia and explore its adaptive evolution. Results The reconstructed genome of E. angustifolia was 526.80 Mb, with a contig N50 of 12.60 Mb and estimated divergence time of 84.24 Mya. Gene family expansion and resequencing analyses showed that the evolution of E. angustifolia was closely related to environmental conditions. After exposure to salt stress, GO pathway analysis showed that new genes identified from the transcriptome were related to ATP-binding, metal ion binding, and nucleic acid binding. Conclusion The genome sequence of E. angustifolia could be used for comparative genomic analyses of Elaeagnaceae family members and could help elucidate the mechanisms underlying the response of E. angustifolia to drought, salt, cold, and wind stress. Generally, these results provide new insights that could be used to improve desertification-affected land.

Background On tomato plants, the flowering truss is a group or cluster of smaller stems where flowers and fruit develop, while the growing truss is the most extended part of the stem. Because the state of the growing truss reacts sensitively to the surrounding environment, it is essential to control its growth in the early stages. With the recent development of information and artificial intelligence technology in agriculture, a previous study developed a real-time acquisition and evaluation method for images using robots. Furthermore, we used image processing to locate the growing truss to extract growth information. Among the different vision algorithms, the CycleGAN algorithm was used to generate and transform unpaired images using generated learning images. In this study, we developed a robot-based system for simultaneously acquiring RGB and depth images of the growing truss of the tomato plant. Results The segmentation performance for approximately 35 samples was compared via false negative (FN) and false positive (FP) indicators. For the depth camera image, we obtained FN and FP values of 17.55 ± 3.01% and 17.76 ± 3.55%, respectively. For the CycleGAN algorithm, we obtained FN and FP values of 19.24 ± 1.45% and 18.24 ± 1.54%, respectively. When segmentation was performed via image processing through depth image and CycleGAN, the mean intersection over union (mIoU) was 63.56 ± 8.44% and 69.25 ± 4.42%, respectively, indicating that the CycleGAN algorithm can identify the desired growing truss of the tomato plant with high precision. Conclusions The on-site possibility of the image extraction technique using CycleGAN was confirmed when the image scanning robot drove in a straight line through a tomato greenhouse. In the future, the proposed approach is expected to be used in vision technology to scan tomato growth indicators in greenhouses using an unmanned robot platform.

Background Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. Results Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. Conclusions This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

Lasers enable modification of living and non-living matter with submicron precision in a contact-free manner which has raised the interest of researchers for decades. Accordingly, laser technologies have drawn interest across disciplines. They have been established as a valuable tool to permeabilize cellular membranes for molecular delivery in a process termed photoinjection. Laser-based molecular delivery was first reported in 1984, when normal kidney cells were successfully transfected with a frequency-multiplied Nd:YAG laser. Due to the rapid development of optical technologies, far more sophisticated laser platforms have become available. In particular, near infrared femtosecond (NIR fs) laser sources enable an increasing progress of laser-based molecular delivery procedures and opened up multiple variations and applications of this technique. This review is intended to provide a plant science audience with the physical principles as well as the application potentials of laser-based molecular delivery. The historical origins and technical development of laser-based molecular delivery are summarized and the principle physical processes involved in these approaches and their implications for practical use are introduced. Successful cases of laser-based molecular delivery in plant science will be reviewed in detail, and the specific hurdles that plant materials pose will be discussed. Finally, we will give an outlook on current limitations and possible future applications of laser-based molecular delivery in the field of plant science.

Background The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. Conclusions Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance.

Background The interaction between canopy and droplets is very important in the process of crop spraying. During the actual air-assisted application process, air-mist flow inevitably disturbs the leaves before droplets reaching them, which will also affect the final deposition state of the droplets on the leaf. Currently, researches on the interaction between droplets and the target leaf surface mainly focuses on the deposition behaviour on the surface of stationary target leaves rather than the dynamic leaves. Therefore, the deposition characteristics after the collision between the droplets and dynamic leaves are important for practical application and worth further study. Results Computational fluid dynamics simulations were performed to characterise the surface roughness, contact angle, and mechanical vibration. The interaction platform between the droplet and the vibrating pear leaf was built for experimental verification under laboratory conditions. The simulation results are in good agreement with the experimental results, which revealed the main reason for the droplet spreading and sliding was the inertial force generated by the relative velocity. It also indicated that the pear leaf vibration can improve the deposition of low-velocity and small droplets, which is different from that of static pear leaves. Conclusion The deposition effect of droplets in vibrating pear leaves was investigated. This study also provides a simulation method for the collision between a vibrating leaf and moving droplets, and provides reference for the study of droplet deposition characteristics under the vibration of fruit trees.

Background Many significant ecosystems, including important non-forest woody ecosystems such as the Cerrado (Brazilian savannah), are under threat from climate change, yet our understanding of how increasing temperatures will impact native vegetation remains limited. Temperature manipulation experiments are important tools for investigating such impacts, but are often constrained by access to power supply and limited to low-stature species, juvenile individuals, or heating of target organs, perhaps not fully revealing how entire or mature individuals and ecosystems will react to higher temperatures. Results We present a novel, modified open top chamber design for in situ passive heating of whole individuals up to 2.5 m tall (but easily expandable) in remote field environments with strong solar irradiance. We built multiple whole-tree heating structures (WTHSs) in an area of Cerrado around native woody species Davilla elliptica and Erythroxylum suberosum to test the design and its effects on air temperature and humidity, while also studying the physiological responses of E. suberosum to short-term heating. The WTHSs raised internal air temperature by approximately 2.5 °C above ambient during the daytime. This increased to 3.4 °C between 09:00 and 17:00 local time when thermal impact was greatest, and during which time mean internal temperatures corresponded closely with maximum ambient temperatures. Heating was consistent over time and across WTHSs of variable size and shape, and they had minimal effect on humidity. E. suberosum showed no detectable response of photosynthesis or respiration to short-term experimental heating, but some indication of acclimation to natural temperature changes. Conclusions Our WTHSs produced a consistent and reproducible level of daytime heating in line with mid-range climate predictions for the Cerrado biome by the end of the century. The whole-tree in situ passive heating design is flexible, low-cost, simple to build using commonly available materials, and minimises negative impacts associated with passive chambers. It could be employed to investigate the high temperature responses of many understudied species in a range of complex non-forest environments with sufficient solar irradiance, providing new and important insights into the possible impacts of our changing climate.

Background The superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is an important way to increase grain yield. However, most of the existing phenotyping platforms have the disadvantages of high construction and maintenance costs, immobile and limited in use by climatic factors, while the traditional climate chambers lack phenotypic data acquisition, which makes crop phenotyping research and development difficult. Crop breeding progress is slow. At present, there is an urgent need to develop a low-cost, easy-to-promote, climate- and site-independent facility that combines the functions of crop cultivation and phenotype acquisition. We propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system. Result We selected two wheat varieties with different early vigor to carry out the cultivation experiments and phenotype acquisition of wheat under different nitrogen fertilizer application rates in an intelligent artificial climate chamber. With the help of the crop phenotype acquisition system, images of wheat at the trefoil stage, pre-tillering stage, late tillering stage and jointing stage were collected, and then the phenotypic information including wheat leaf area, plant height, and canopy temperature were extracted by the crop type acquisition system. We compared systematic and manual measurements of crop phenotypes for wheat phenotypes. The results of the analysis showed that the systematic measurements of leaf area, plant height and canopy temperature of wheat in four growth periods were highly correlated with the artificial measurements. The correlation coefficient (r) is positive, and the determination coefficient ( R 2 ) is greater than 0.7156. The root mean square error ( RSME ) is less than 2.42. Among them, the crop phenotype-based collection system has the smallest measurement error for the phenotypic characteristics of wheat trefoil stage. The canopy temperature RSME is only 0.261. The systematic measurement values of wheat phenotypic characteristics were significantly positively correlated with the artificial measurement values, the fitting degree was good, and the errors were all within the acceptable range. The experiment showed that the phenotypic data obtained with the intelligent artificial climate chamber has high accuracy. We verified the feasibility of wheat cultivation and phenotype acquisition based on intelligent artificial climate chamber. Conclusion It is feasible to study wheat cultivation and canopy phenotype with the help of intelligent artificial climate chamber. Based on a variety of environmental monitoring sensors and environmental regulation equipment, the growth environment factors of crops can be adjusted. Based on high-precision mechanical transmission and multi-dimensional imaging sensors, crop images can be collected to extract crop phenotype information. Its use is not limited by environmental and climatic factors. Therefore, the intelligent artificial climate chamber is expected to be a powerful tool for breeders to develop excellent germplasm varieties.

Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties ¹ . Both methods predicted the kernel mass with R ² > 0.93 (XRT: R ² = 0.93 and mean error estimate (MAE) = 0.17, CNN: R ² = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R ² = 0.91, MAE = 0.09) compared to XRT (R ² = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

The sorting of RNA transcripts dictates their ultimate post-transcriptional fates, such as translation, decay or degradation by RNA interference (RNAi). This sorting of RNAs into distinct fates is mediated by their interaction with RNA-binding proteins. While hundreds of RNA binding proteins have been identified, which act to sort RNAs into different pathways is largely unknown. Particularly in plants, this is due to the lack of reliable protein-RNA artificial tethering tools necessary to determine the mechanism of protein action on an RNA in vivo. Here we generated a protein-RNA tethering system which functions on an endogenous Arabidopsis RNA that is tracked by the quantitative flowering time phenotype. Unlike other protein-RNA tethering systems that have been attempted in plants, our system circumvents the inadvertent triggering of RNAi. We successfully in vivo tethered a protein epitope, deadenylase protein and translation factor to the target RNA, which function to tag, decay and boost protein production, respectively. We demonstrated that our tethering system (1) is sufficient to engineer the downstream fate of an RNA, (2) enables the determination of any protein’s function upon recruitment to an RNA, and (3) can be used to discover new interactions with RNA-binding proteins.

Background Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. Methods As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0–10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. Results For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). Conclusion We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

Background Triticum aestivum is the most important staple food grain of the world. In recent years, the outbreak of a major seed-borne disease, common bunt, in wheat resulted in reduced quality and quantity of the crop. The disease is caused by two fungal pathogens, Tilletia caries and Tilletia laevis , which show high similarity to each other in terms of life cycle, germination, and disease symptoms. The host–pathogen protein–protein interactions play a crucial role in initiating the disease infection mechanism as well as in plant defense responses. Due to the availability of limited information on Tilletia species, the elucidation of infection mechanisms is hampered. Results We constructed a database WeCoNET ( http://bioinfo.usu.edu/weconet/ ), providing functional annotations of the pathogen proteins and various tools to exploit host–pathogen interactions and other relevant information. The database implements a host–pathogen interactomics tool to predict protein–protein interactions, followed by network visualization, BLAST search tool, advanced ‘keywords-based’ search module, etc. Other features in the database include various functional annotations of host and pathogen proteins such as gene ontology terms, functional domains, and subcellular localization. The pathogen proteins that serve as effector and secretory proteins have also been incorporated in the database, along with their respective descriptions. Additionally, the host proteins that serve as transcription factors were predicted, and are available along with the respective transcription factor family and KEGG pathway to which they belong. Conclusion WeCoNET is a comprehensive, efficient resource to the molecular biologists engaged in understanding the molecular mechanisms behind the common bunt infection in wheat. The data integrated into the database can also be beneficial to the breeders for the development of common bunt-resistant cultivars.

Background Although the genome for the allotetraploid bioenergy crop switchgrass ( Panicum virgatum ) has been established, limitations in mutant resources have hampered in planta gene function studies toward crop optimization. Virus-induced gene silencing (VIGS) is a versatile technique for transient genetic studies. Here we report the implementation of foxtail mosaic virus (FoMV)-mediated gene silencing in switchgrass in above- and below-ground tissues and at different developmental stages. Results The study demonstrated that leaf rub-inoculation is a suitable method for systemic gene silencing in switchgrass. For all three visual marker genes, Magnesium chelatase subunit D ( ChlD ) and I ( ChlI ) as well as phytoene desaturase ( PDS ), phenotypic changes were observed in leaves, albeit at different intensities. Gene silencing efficiency was verified by RT-PCR for all tested genes. Notably, systemic gene silencing was also observed in roots, although silencing efficiency was stronger in leaves (~ 63–94%) as compared to roots (~ 48–78%). Plants at a later developmental stage were moderately less amenable to VIGS than younger plants, but also less perturbed by the viral infection. Conclusions Using FoMV-mediated VIGS could be achieved in switchgrass leaves and roots, providing an alternative approach for studying gene functions and physiological traits in this important bioenergy crop.

Background Leaf hydration is controlled by feedback mechanisms, e.g. stomatal responses, adjustments of osmotic potential and hydraulic conductivity. Leaf water content thus is an input into related feedback-loops controlling the balance of water uptake and loss. Apoplastic alkalisation upon leaf dehydration is hypothesized to be involved together and in interaction with abscisic acid (ABA) in water stress related signaling on tissue level. However, important questions are still unresolved, e.g. the mechanisms leading to pH changes and the exact nature of its interaction with ABA. When studying these mechanisms and their intermediate signaling steps, an experimenter has only poor means to actually control the central experimental variable, leaf water content (LWC), because it is not only dependent on external variables (e.g. air humidity), which are under experimental control, but is also governed by the biological influences controlling transpiration and water uptake. Those are often unknown in their magnitude, unpredictable and fluctuating throughout an experiment and will prevent true repetitions of an experiment. The goal of the method presented here is to experimentally control and manipulate leaf water content (LWC) of attached intact leaves enclosed in a cuvette while simultaneously measuring physiological parameters like, in this case, apoplastic pH. Results An experimental setup was developed where LWC is measured by a sensor based on IR-transmission and its signal processed to control a pump which circulates air from the cuvette through a cold trap. Hereby a feedback-loop is formed, which by adjusting vapour pressure deficit (VPD) and consequently leaf transpiration can precisely control LWC. This technique is demonstrated here in a combination with microscopic fluorescence imaging of apoplastic pH (pH apo ) as indicated by the excitation ratio of the pH sensitive dye OregonGreen. Initial results indicate that pH apo of the adaxial epidermis of Vicia faba is linearly related to reductions in LWC. Conclusions Using this setup, constant LWC levels, step changes or ramps can be experimentally applied while simultaneously measuring physiological responses. The example experiments demonstrate that bringing LWC under experimental control in this way allows better controlled and more repeatable experiments to probe quantitative relationships between LWC and signaling and regulatory processes.

Background Seeds were an important medium for long-distance transmission of plant viruses. Therefore, appropriate, more sensitive methods for detecting low concentrations of virus-infected in seeds were crucial to ensure the quality of seed lots. In this study, we have developed a one-step pre-amplification reverse transcription quantitative PCR (RT-qPCR) assay based on the TaqMan technology to detect Cucumber green mottle mosaic virus (CGMMV) in zucchini seeds. Result Seed powder samples with simulated CGMMV-infected at a low concentration were prepared (the mass ratio 1:900 and 1:1000), and their uniformity were verified using one-step pre-amplification RT-qPCR. We used one-step pre-amplification RT-qPCR to detect CGMMV in low-concentration virus-infected seeds and compared this method with universal RT-qPCR and double antibody sandwich–enzyme-linked immunosorbent (DAS–ELISA) assay, the main methods used for virus detection in seeds. The minimum limit of detection (LOD) of the improved one-step pre-amplification RT-qPCR assays for simulated CGMMV-infected seeds in large lots seeds samples were 0.1%. Conclusions One-step pre-amplification RT-qPCR assays could reliably and stably detected a single CGMMV-infected seed in 1000 seeds and demonstrated a higher detection sensitivity than universal RT-qPCR (infected seeds versus healthy seeds 1:900) and DAS–ELISA assay (infected seeds versus healthy seeds 1:500). Our improved one-step pre-amplification RT-qPCR assay have proved to be very suitable for the analysis of large seed lots. Graphical Abstract

Background The bimolecular fluorescence complementation (BiFC) assay has emerged as one of the most popular methods for analysing protein–protein interactions (PPIs) in plant biology. This includes its increasing use as a tool for dissecting the molecular mechanisms of chloroplast function. However, the construction of chloroplast fusion proteins for BiFC can be difficult, and the availability and selection of appropriate controls is not trivial. Furthermore, the challenges of performing BiFC in restricted cellular compartments has not been specifically addressed. Results Here we describe the development of a flexible modular cloning-based toolkit for BiFC (MoBiFC) and proximity labelling in the chloroplast and other cellular compartments using synthetic biology principles. We used pairs of chloroplast proteins previously shown to interact (HSP21/HSP21 and HSP21/PTAC5) and a negative control (HSP21/ΔPTAC5) to develop standardised Goldengate-compatible modules for the assembly of protein fusions with fluorescent protein (FP) fragments for BiFC expressed from a single multigenic T-DNA. Using synthetic biology principles and transient expression in Nicotiana benthamiana, we iteratively improved the approach by testing different FP fragments, promoters, reference FPs for ratiometric quantification, and cell types. A generic negative control (mCHERRY) was also tested, and modules for the identification of proximal proteins by Turbo-ID labelling were developed and validated. Conclusions MoBiFC facilitates the cloning process for organelle-targeted proteins, allows robust ratiometric quantification, and makes available model positive and negative controls. Development of MoBiFC underlines how Goldengate cloning approaches accelerate the development and enrichment of new toolsets, and highlights several potential pitfalls in designing BiFC experiments including the choice of FP split, negative controls, cell type, and reference FP. We discuss how MoBiFC could be further improved and extended to other compartments of the plant cell and to high throughput cloning approaches.

Background Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion. Methods To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression. Results The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R² raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R² = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R² = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat. Conclusions The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

Background The chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil–plant analysis development (SPAD) value are positively correlated, it is feasible to predict the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby evaluating the severity of plant diseases. However, current indices simply adopt few wavelengths of the hyperspectral information, which may decrease the prediction accuracy. Besides, few researches explored the applicability of VIs over rice under the bacterial blight disease stress. Methods In this study, the SPAD value was predicted by calculating the spectral fractal dimension index (SFDI) from a hyperspectral curve (420 to 950 nm). The correlation between the SPAD value and hyperspectral information was further analyzed for determining the sensitive bands that correspond to different disease levels. In addition, a SPAD prediction model was built upon the combination of selected indices and four machine learning methods. Results The results suggested that the SPAD value of rice leaves under different disease levels are sensitive to different wavelengths. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and the SFDI, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with support vector regression and SFDI achieved the best performance, reaching R ² , RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively. Conclusions This work provides an in-depth insight for accurately and robustly predicting the SPAD value of rice leaves under the bacterial blight disease stress, and the SFDI is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.

Background Although quantitative single-cell analysis is frequently applied in animal systems, e.g. to identify novel drugs, similar applications on plant single cells are largely missing. We have exploited the applicability of high-throughput microscopic image analysis on plant single cells using tobacco leaf protoplasts, cell-wall free single cells isolated by lytic digestion. Protoplasts regenerate their cell wall within several days after isolation and have the potential to expand and proliferate, generating microcalli and finally whole plants after the application of suitable regeneration conditions. Results High-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts during the initial days following cultivation by immobilization in multi-well-plates. The focus on early protoplast responses allowed to study cell expansion prior to the initiation of proliferation and without the effects of shape-compromising cell walls. We compared growth parameters of wild-type tobacco cells with cells expressing the antiapoptotic protein Bcl2-associated athanogene 4 from Arabidopsis ( AtBAG4). Conclusions AtBAG4 -expressing protoplasts showed a higher proportion of cells responding with positive area increases than the wild type and showed increased growth rates as well as increased proliferation rates upon continued cultivation. These features are associated with reported observations on a BAG4-mediated increased resilience to various stress responses and improved cellular survival rates following transformation approaches. Moreover, our single-cell expansion results suggest a BAG4-mediated, cell-independent increase of potassium channel abundance which was hitherto reported for guard cells only. The possibility to explain plant phenotypes with single-cell properties, extracted with the single-cell processing and analysis pipeline developed, allows to envision novel biotechnological screening strategies able to determine improved plant properties via single-cell analysis.

Background Quinoa is an increasingly popular seed crop frequently studied for its tolerance to various abiotic stresses as well as its susceptibility to heat. Estimations of quinoa pollen viability through staining methods have resulted in conflicting results. A more effective alternative to stains is to estimate pollen viability through in vitro germination. Here we report a method for in vitro quinoa pollen germination that could be used to understand the impact of various stresses on quinoa fertility and therefore seed yield or to identify male-sterile lines for breeding. Results A semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community. Pollen collected on day 4 after first anthesis at zeitgeber time 5 was optimum for pollen germination with an average germination of 68% for accession QQ74 (PI 614886). The optimal length of pollen incubation was found to be 48 h, because it maximizes germination rates while minimizing contamination. The pollen germination medium’s pH, boric acid, and sucrose concentrations were optimized. The highest germination rates were obtained with 16% sucrose, 0.03% boric acid, 0.007% calcium nitrate, and pH 5.5. This medium was tested on quinoa accessions QQ74, and cherry vanilla with 68%, and 64% germination efficiencies, respectively. Conclusions We provide an in vitro pollen germination method for quinoa with average germination rates of 64 and 68% on the two accessions tested. This method is a valuable tool to estimate pollen viability in quinoa, and to test how stress affects quinoa fertility. We also developed an image analysis tool to semi-automate the process of counting germinating pollen. Quinoa produces many new flowers during most of its panicle development period, leading to significant variation in pollen maturity and viability between different flowers of the same panicle. Therefore, collecting pollen at 4 days after first anthesis is very important to collect more uniformly developed pollen and to obtain high germination rates.

Background The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vitality. To address the problems of high labor intensity and subjectivity in traditional measurement methods, a fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed. Results First, during the nutritional growth period of banana, three-dimensional (3D) point cloud data of two measured fields were obtained by TLS. Second, the point cloud data was preprocessed. And the single plant segmentation of the canopy closed banana plant point cloud was realized furtherly. Finally, the number of banana plants was obtained by counting the number of pseudo-stems, and the diameter of pseudo-stems was measured using a cylindrical segmentation algorithm. A sliding window recognition method was proposed to determine the junction position between leaves and pseudo-stems, and the height of the pseudo-stems was measured. Compared with the measured value of artificial point cloud, when counting the number of banana plants, the precision,recall and percentage error of field 1 were 93.51%, 94.02%, and 0.54% respectively; the precision,recall and percentage error of field 2 were 96.34%, 92.00%, and 4.5% respectively; In the measurement of pseudo-stem diameter and height of banana, the root mean square error (RMSE) of pseudo-stem diameter and height of banana plant in field 1 were 0.38 cm and 0.2014 m respectively, and the mean absolute percentage error (MAPE) were 1.30% and 5.11% respectively; the RMSE of pseudo-stem diameter and height of banana plant in field 2 were 0.39 cm and 0.2788 m respectively, and the MAPE were 1.04% and 9.40% respectively. Conclusion The results show that the method proposed in this paper is suitable for the field measurement of banana count, pseudo-stem diameter, and height and can provide a fast field measurement method for banana plantation management.

Background Rapid-cycling Brassica napus ( B. napus -RC) has potential as a rapid trait testing system for canola ( B. napus ) because its life cycle is completed within 2 months while canola usually takes 4 months, and it is susceptible to the same range of diseases and abiotic stress as canola. However, a rapid trait testing system for canola requires the development of an efficient transformation and tissue culture system for B. napus -RC. Furthermore, effectiveness of this system needs to be demonstrated by showing that a particular trait can be rapidly introduced into B. napus -RC plants. Results An in-vitro regeneration protocol was developed for B. napus -RC using 4-day-old cotyledons as the explant. High regeneration percentages, exceeding 70%, were achieved when 1-naphthaleneacetic acid (0.10 mg/L), 6-benzylaminopurine (1.0 mg/L), gibberellic acid (0.01 mg/L) and the ethylene antagonist silver nitrate (5 mg/L) were included in the regeneration medium. An average transformation efficiency of 16.4% was obtained using Agrobacterium -mediated transformation of B. napus -RC cotyledons using Agrobacterium strain GV3101 harbouring a plasmid with an NPTII (kanamycin-selectable) marker gene and the Arabidopsis thaliana cDNA encoding ACYL-COA-BINDING PROTEIN6 (AtACBP6). Transgenic B. napus -RC overexpressing AtACBP6 displayed better tolerance to freezing/frost than the wild type, with enhanced recovery from cellular membrane damage at both vegetative and flowering stages. AtACBP6 -overexpressing B. napus -RC plants also exhibited lower electrolyte leakage and improved recovery following frost treatment, resulting in higher yields than the wild type. Ovules from transgenic AtACBP6 lines were better protected from frost than those of the wild type, while the developing embryos of frost-treated AtACBP6 -overexpressing plants showed less freezing injury than the wild type. Conclusions This study demonstrates that B. napus -RC can be successfully regenerated and transformed from cotyledon explants and has the potential to be an effective trait testing platform for canola. Additionally, AtACBP6 shows potential for enhancing cold tolerance in canola however, larger scale studies will be required to further confirm this outcome.

Background The accurate estimation of leaf hydraulic conductance ( K leaf ) is important for revealing leaf physiological characteristics and function. However, the K leaf values are largely incomparable in previous studies for a given species indicating some uncertain influencing factors in K leaf measurement. Result We investigated the potential impacts of plant sampling method, measurement setup, environmental factors, and transpiration steady state identification on K leaf estimation in Oryza sativa and Cinnamomum camphora using evaporation flux method (EFM). The effects of sampling and rehydration time, the small gravity pressure gradients between water sources and leaves, and water degassing on K leaf estimation were negligible. As expected, the estimated steady flow rate ( E ) was significantly affected by multiple environmental factors including airflow around leaf, photosynthetically active radiation (PARa) on leaf surfaces and air temperature. K leaf decreased by 40% when PARa declined from 1000 to 500 µmol m ⁻² s ⁻¹ and decreased by 15.1% when air temperature increased from 27 to 37 °C. In addition, accurate steady-state flow rate identification and leaf water potential measurement were important for K leaf estimation. Conclusions Based on the analysis of influencing factors, we provided a format for reporting the metadata of EFM-based K leaf to achieve greater comparability among studies and interpretation of differences.

Background Despite the advances in the techniques of indirect estimation of leaf area, the destructive measurement approaches have still remained as the reference and the most accurate methods. However, even utilizing the modern sensors and applications usually requires the laborious and time-consuming practice of unfolding and analyzing the single leaves, separately. In the present study, a volumetric approach was tested to determine the pile leaf area based on the ratio of leaf volume divided by thickness. For this purpose, the suspension technique was used for volumetry, which is based on the simple practice and calculations of the Archimedes’ principle. Results Wheat volumetric leaf area (VLA), had a high agreement and approximately 1:1 correlation with the conventionally measured optical leaf area (OLA). Exclusion of the midrib volume from calculations, did not affect the estimation error (NRMSE < 2.61%); however, improved the slope of the linear model by about 6%, and also reduced the bias between the methods. The error of sampling for determining mean leaf thickness of the pile, was also less than 2% throughout the season. Besides, a more practical and facilitated version of pile volumetry was tested using Specific Gravity Bench (SGB), which is currently available as a laboratory equipment. As an important observation, which was also expectable according to the leaf 3D expansion (i.e., in a given 2D plane), it was evidenced that the variations in the OLA exactly follows the pattern of the changes in the leaf volume. Accordingly, it was suggested that the relative leaf areas of various experimental treatments might be compared directly based on volume, and independently of leaf thickness. Furthermore, no considerable difference was observed among the OLAs measured using various image resolutions (NRMSE < 0.212%); which indicates that even the superfast scanners with low resolutions as 200 dpi may be used for a precision optical measurement of leaf area. Conclusions It is expected that utilizing the reliable and simple concept of volumetric leaf area, based on which the measurement time might be independent of sample size, facilitate the laborious practice of leaf area measurement; and consequently, improve the precision of field experiments.

Background There is a demand for non-destructive systems in plant phenotyping which could precisely measure plant traits for growth monitoring. In this study, the growth of chilli plants (Capsicum annum L.) was monitored in outdoor conditions. A non-destructive solution is proposed for growth monitoring in 3D using a single mobile phone camera based on a structure from motion algorithm. A method to measure leaf length and leaf width when the leaf is curled is also proposed. Various plant traits such as number of leaves, stem height, leaf length, and leaf width were measured from the reconstructed and segmented 3D models at different plant growth stages. Results The accuracy of the proposed system is measured by comparing the values derived from the 3D plant model with manual measurements. The results demonstrate that the proposed system has potential to non-destructively monitor plant growth in outdoor conditions with high precision, when compared to the state-of-the-art systems. Conclusions In conclusion, this study demonstrated that the methods proposed to calculate plant traits can monitor plant growth in outdoor conditions.

Background Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results The models with a single color feature from RGB images predicted chlorophyll content with R ² ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R ² ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R ² of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R ² of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.

Background Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated, thanks to the automated image processing systems, MGW estimations have been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which were harvested from a 2-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images including more than 72,000 grains). Results It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area × Circularity, Perimeter × Circularity, and Area/Perimeter indices. Furthermore, it was found that (i) grain width and the Area/Perimeter ratio were the common factors in the structure of the superior predictive indices; and (ii) the superior indices had the highest correlation with grain width, rather than with their mathematical components. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.

Background The study of the regulatory mechanisms of evolutionarily conserved Nucleotide-binding leucine-rich repeat (NLR) resistance (R) proteins in animals and plants is of increasing importance due to understanding basic immunity and the value of various crop engineering applications of NLR immune receptors. The importance of temperature is also emerging when applying NLR to crops responding to global climate change. In particular, studies of pathogen effector recognition and autoimmune activity of NLRs in plants can quickly and easily determine their function in tobacco using agro-mediated transient assay. However, there are conditions that should not be overlooked in these cell death-related assays in tobacco. Results Environmental conditions play an important role in the immune response of plants. The system used in this study was to establish conditions for optimal hypertensive response (HR) cell death analysis by using the paired NLR RPS4/RRS1 autoimmune and AvrRps4 effector recognition system. The most suitable greenhouse temperature for growing plants was fixed at 22 °C. In this study, RPS4/RRS1-mediated autoimmune activity, RPS4 TIR domain-dependent cell death, and RPS4/RRS1-mediated HR cell death upon AvrRps4 perception significantly inhibited under conditions of 65% humidity. The HR is strongly activated when the humidity is below 10%. Besides, the leaf position of tobacco is important for HR cell death. Position #4 of the leaf from the top in 4–5 weeks old tobacco plants showed the most effective HR cell death. Conclusions As whole genome sequencing (WGS) or resistance gene enrichment sequencing (RenSeq) of various crops continues, different types of NLRs and their functions will be studied. At this time, if we optimize the conditions for evaluating NLR-mediated HR cell death, it will help to more accurately identify the function of NLRs. In addition, it will be possible to contribute to crop development in response to global climate change through NLR engineering.

Background China has a unique cotton planting pattern. Cotton is densely planted in alternating wide and narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate estimation of cotton yield using remote sensing in such field with branches occluded and overlapped. Results In this study, unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate densely planted cotton yield. Images of cotton fields were acquired by the UAV at an altitude of 5 m. Cotton bolls were manually harvested and weighed afterwards. Then, a modified DCNN model (CD-SegNet) was constructed for pixel-level segmentation of cotton boll images by reorganizing the encoder-decoder and adding dilated convolutions. Besides, linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Finally, the estimated yield for four cotton fields were verified by weighing harvested cotton. The results showed that CD-SegNet outperformed the other tested models, including SegNet, support vector machine (SVM), and random forest (RF). The average error in yield estimates of the cotton fields was as low as 6.2%. Conclusions Overall, the estimation of densely planted cotton yields based on low-altitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.

Background Stalk lodging (breaking of agricultural plant stalks prior to harvest) is a multi-billion dollar a year problem. Stalk lodging occurs when high winds induce bending moments in the stalk which exceed the bending strength of the plant. Previous biomechanical models of plant stalks have investigated the effect of cross-sectional morphology on stalk lodging resistance (e.g., diameter and rind thickness). However, it is unclear if the location of stalk failure along the length of stem is determined by morphological or compositional factors. It is also unclear if the crops are structurally optimized, i.e., if the plants allocate structural biomass to create uniform and minimal bending stresses in the plant tissues. The purpose of this paper is twofold: (1) to investigate the relationship between bending stress and failure location of maize stalks, and (2) to investigate the potential of phenotyping for internode-level bending stresses to assess lodging resistance. Results 868 maize specimens representing 16 maize hybrids were successfully tested in bending to failure. Internode morphology was measured, and bending stresses were calculated. It was found that bending stress is highly and positively associated with failure location. A user-friendly computational tool is presented to help plant breeders in phenotyping for internode-level bending stress. Phenotyping for internode-level bending stresses could potentially be used to breed for more biomechanically optimal stalks that are resistant to stalk lodging. Conclusions Internode-level bending stress plays a potentially critical role in the structural integrity of plant stems. Equations and tools provided herein enable researchers to account for this phenotype, which has the potential to increase the bending strength of plants without increasing overall structural biomass.

Background Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically. Results In this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the “size-free” space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences. Conclusions The proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.

Background From an economic perspective, cotton is one of the most important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. Anther dehiscence or indehiscence directly determines the probability of fertilization in cotton. Thus, rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers. Result The single-stage model based on YOLOv5 has higher recognition speed and the ability to deploy to the mobile end. Breeding researchers can apply this model to terminals to achieve a more intuitive understanding of cotton anther dehiscence status. Moreover, three improvement strategies are proposed for the Faster R-CNN model, where the improved model has higher detection accuracy than the YOLOv5 model. We have made three improvements to the Faster R-CNN model and after the ensemble of the three models and original Faster R-CNN model, R² of “open” reaches to 0.8765, R² of “close” reaches to 0.8539, R² of “all” reaches to 0.8481, higher than the prediction results of either model alone, which are completely able to replace the manual counting results. We can use this model to quickly extract the dehiscence rate of cotton anthers under high temperature (HT) conditions. In addition, the percentage of dehiscent anthers of 30 randomly selected cotton varieties were observed from the cotton population under normal conditions and HT conditions through the ensemble of the Faster R-CNN model and manual counting. The results show that HT decreased the percentage of dehiscent anthers in different cotton lines, consistent with the manual method. Conclusions Deep learning technology have been applied to cotton anther dehiscence status recognition instead of manual methods for the first time to quickly screen HT–tolerant cotton varieties. Deep learning can help to explore the key genetic improvement genes in the future, promoting cotton breeding and improvement.