New systems for agrochemical delivery in plants will foster precise agricultural practices and provide new tools to study plants and design crop traits, as standard spray methods suffer from elevated loss and limited access to remote plant tissues. Silk‐based microneedles can circumvent these limitations by deploying a known amount of payloads directly in plants’ deep tissues. However, plant response to microneedles’ application and microneedles’ efficacy in deploying physiologically relevant biomolecules are unknown. Here, it is shown that gene expression associated with Arabidopsis thaliana wounding response decreases within 24 h post microneedles’ application. Additionally, microinjection of gibberellic acid (GA3) in A. thaliana mutant ft‐10 provides a more effective and efficient mean than spray to activate GA3 pathways, accelerating bolting and inhibiting flower formation. Microneedle efficacy in delivering GA3 is also observed in several monocot and dicot crop species, i.e., tomato (Solanum lycopersicum), lettuce (Lactuca sativa), spinach (Spinacia oleracea), rice (Oryza Sativa), maize (Zea mays), barley (Hordeum vulgare), and soybean (Glycine max). The wide range of plants that can be successfully targeted with microinjectors opens the doors to their use in plant science and agriculture.

Calcium (Ca) deficiency causes necrotic symptoms of foliar edges known as tipburn; however, the underlying cellular mechanisms have been poorly understood due to the lack of an ideal plant model and research platform.Using a phenotyping system that quantitates growth and tipburn traits in the model bryophyte Marchantia polymorpha, we evaluated metabolic compounds and the Gβ-null mutant (gpb1) that modulate the occurrence and expansion of the tipburn. Transcriptomic comparisons between wild-type and gpb1 plants revealed the phenylalanine/phenylpropanoid biosynthesis pathway and reactive oxygen species (ROS) important for Ca deficiency responses.gpb1 plants reduced ROS production possibly through transcriptomic regulations of class III peroxidases and induced the expression of phenylpropanoid pathway enzymes without changing downstream lignin contents. Supplementation of intermediate metabolites and chemical inhibitors further confirmed the cell-protective mechanisms of the phenylpropanoid and ROS pathways. Marchantia polymorpha, Arabidopsis thaliana, and Lactuca sativa showed comparable transcriptomic changes where genes related to phenylpropanoid and ROS pathways were enriched in response to Ca deficiency.In conclusion, our study demonstrated unresolved signaling and metabolic pathways of Ca deficiency response. The phenotyping platform can speed up the discovery of chemical and genetic pathways, which could be widely conserved between M. polymorpha and angiosperms.

Leaf color patterns vary depending on leaf age, pathogen infection, and environmental and nutritional stresses; thus, they are widely used to diagnose plant health statuses in agricultural fields. The visible-near infrared-shortwave infrared (VIS-NIR-SWIR) sensor measures the leaf color pattern from a wide spectral range with high spectral resolution. However, spectral information has only been employed to understand general plant health statuses (e.g., vegetation index) or phytopigment contents, rather than pinpointing defects of specific metabolic or signaling pathways in plants. Here, we report feature engineering and machine learning methods that utilize VIS-NIR-SWIR leaf reflectance for robust plant health diagnostics, pinpointing physiological alterations associated with the stress hormone, abscisic acid (ABA). Leaf reflectance spectra of wild-type, ABA2 -overexpression, and deficient plants were collected under watered and drought conditions. Drought- and ABA-associated normalized reflectance indices (NRIs) were screened from all possible pairs of wavelength bands. Drought associated NRIs showed only a partial overlap with those related to ABA deficiency, but more NRIs were associated with drought due to additional spectral changes within the NIR wavelength range. Interpretable support vector machine classifiers built with 20 NRIs predicted treatment or genotype groups with an accuracy greater than those with conventional vegetation indices. Major selected NRIs were independent from leaf water content and chlorophyll content, 2 well-characterized physiological changes under drought. The screening of NRIs, streamlined with the development of simple classifiers, serves as the most efficient means of detecting reflectance bands that are highly relevant to characteristics of interest.

Advanced precision agriculture requires the objective measurement of the structural and functional properties of plants. Biochemical profiles in leaves can differ depending on plant growing conditions. By quantitatively detecting these changes, farm production processes can be optimized to achieve high-yield, high-quality, and nutrient dense agricultural products. To enable the rapid and non-destructive detection on site, this study demonstrates the development of a new custom-designed portable handheld Vis-NIR spectrometer that collects leaf reflectance spectra, wirelessly transfers the spectral data through Bluetooth, and provides both raw spectral data and processed information. The spectrometer has two preprogramed methods: anthocyanin and chlorophyll quantification. Anthocyanin content of red and green lettuce estimated with the new spectrometer showed an excellent correlation coefficient of 84% with those determined by a destructive gold standard biochemical method. The differences in chlorophyll content were measured using leaf senescence as a case study. Chlorophyll Index calculated with the handheld spectrometer gradually decreased with leaf age as chlorophyll degrades during the process of senescence. The estimated chlorophyll values were highly correlated with those obtained from a commercial fluorescence-based chlorophyll meter with a correlation coefficient of 77%. The developed portable handheld Vis-NIR spectrometer could be a simple, cost-effective, and easy to operate tool that can be used to non-invasively monitor plant pigment and nutrient content efficiently.