Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments. Understanding Plant Stress Is Crucial for Yield ProtectionPlant stress is a state of plant growth under non-ideal environmental conditions caused by various biotic (pathogen, insect, pest, and weed) and abiotic (temperature stress, nutrient deficiency, toxicity, herbicide) factors. Significant crop yield loss due to various plant stresses has the potential to threaten global food security [1]. Plant disease epidemics are a constant threat and continue to emerge owing to complex host-pathogen-environment dynamics [2,3]. Global climate change can exacerbate this situation because of the predicted increases in insect and pathogen pressure for major grains including rice (Oryza sativa), maize (Zea mays L.), and wheat (Triticum aestivum) [4]. Moreover, weather-related challenges such as drought, flooding, hail, and windstorms adversely affect crop production. Yield preservation and protection is a dynamic challenge for pathologists, entomologists, plant breeders, and crop producers globally. Understanding plant stress is crucial for improving yield protection to meet with the growing demand for food production [5]. In the past decade, significant advances in image processing and machine learning (ML; see Glossary) algorithms have been made to handle image-based stress datasets for automated data analysis and application of trained models [6][7][8]. We review the development and application of ML algorithms for image-based plant stress phenotyping at multiple scales ranging from leaf and canopy (plot) to field (production) scale. We discuss some of the major challenges in the practical application of ML algorithms, and list future efforts that will be necessary to make ML a more mainstream tool in plant stress phenotyping applications and usage. HighlightsPlant stress phenotyping is challenging to implement at multiple organizational scales (leaf, canopy, field).There is a need to improve the speed, accuracy, reliability, and scalability of stress phenotyping while allowing flexibility for highly variable program goals.

This study determined the prevalence of the eaeA gene and its relationship to serotype and type of verotoxin produced in a collection of 432 verotoxigenic Escherichia coli (VTEC) obtained from the faeces of healthy cows and calves in a systematic random survey involving 80 dairy farms in Southwest Ontario. A PCR amplification procedure involving primer pairs which target the conserved central region of the O157:H7 eaeA gene showed that 151 (35.2%) strains were positive for the eaeA gene. All isolates (9-21 for each O group) of O groups 5, 26, 69, 84, 103, 111, 145 and 157 were positive, whereas all isolates (7-34 for each O group) of O groups 113, 132, and 153 and serotype O156:NM (38 isolates) were negative for eaeA. Seventy-three percent of 130 isolates of eaeA-positive serotypes produced VT1 only compared with 20% of 253 isolates of eaeA-negative serotypes. We conclude that there is a strong association between certain O groups and the eaeA gene, that serotypes of eaeA-positive and eaeA-negative VTEC implicated in human and cattle disease are present at high frequency in the faeces of healthy cattle, that VT1 is more frequently associated with eaeA-positive than with eaeA-negative serogroups, and that the eaeA gene is more frequently found in VTEC from calves compared with VTEC from adult cattle.

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Bovine brucellosis, caused by Brucella abortus, is a serious zoonotic disease manifested by reproductive disorders resulting in huge economic losses to dairy farmers. A random survey was conducted to study the epidemiology of brucellosis in Punjab (India) using sampling software Survey Toolbox. Two-stage sampling procedure was adopted; in the first step, villages were selected randomly from sampling frame of all the villages of Punjab followed by selection of owners, and animals in individual farms were identified using random sampling. In all, 32 villages were selected and then 345 animals (approximately 5%) were sampled from these villages. The milk samples collected were screened for brucella antibodies employing ELISA test. The overall apparent prevalence of brucellosis was found to be 18.26% (true prevalence -17.68%). The prevalence in the central zone of the state was significantly higher, viz. 23.2% (chi square = 11.34, p < 0.01) compared to 14.2% in the sub-mountainous zone and 5.8% in the arid irrigated zone. The disease prevalence was found to be non-significantly higher (chi square 1.029, p = 0.310) in cattle (20.67%) compared to buffaloes (16.41%) and increased with age (chi square = 8.572, p < 0.05) in both species. There was significant association between disease and abortion (chi square = 22.322, p < 0.01) and maximum abortion cases due to brucellosis were found in > 6 month of gestation (95.7%). The disease was significantly associated with the retention of placenta (chi square = 8.477, p < 0.01), however there was no significant relationship of the disease with repeat breeding (chi square = 0.044, p = 0.834). The results of the study suggested that the accurate epidemiological scenario of the disease may be obtained by employing multistage sampling procedures using milk-based ELISA.

Global and midwestern U.S. agriculture requires diversification and new sources of protein for sustainable crop production. Mung bean [Vigna radiata (L.) R. Wilczek] as a legume crop has a long cultivation history in Asia; however, its potential lays hitherto underexplored in the United States. As a first step towards exploring mung bean for crop diversification in northern latitudes, crop germplasm centers that harbor worldwide crop resources need to be used. This study explores the potential of mung bean in the U.S. northern latitudes through the utilization of the USDA germplasm resources. Complete collection of USDA mung bean germplasm was screened under Iowa field conditions in 2017, to shortlist 482 accessions to create an Iowa mung bean panel. The Iowa mung bean panel was further characterized for field adaptability traits in 2018 and 2019 and genotyped using genotype‐by‐sequencing (GBS) to conduct association mapping of important traits. Genetic markers were identified for both quantitative trait (days to flowering [DTF], plant height [PHT], leaf drop at maturity [LDMS], 100‐seed weight [SDWT], and Fusarium wilt score [WS]) and qualitative traits (seed color [SC], seed‐coat texture [ST], hypocotyl color [HC], and pod color [PC]). We report FERONIA, a known flowering‐pathway gene, as the candidate gene for the quantitative trait locus (QTL) with largest effect on DTF. In addition, important epistatic interactions were also uncovered for WS and SDWT. Further, accessions with desirable magnitude of traits were identified as potential parents. Diversity analyses and field phenotypic data indicate potential for mung bean improvement to suit midwestern U.S. cultivation.

Plants use light as a source of information via a suite of photomorphogenic photoreceptors to optimize growth in response to their light environment. Growth-promoting hormones such as brassinosteroids also can modulate many of these responses. BAS1 and SOB7 are brassinosteroid-catabolizing P450s in Arabidopsis thaliana that synergistically/redundantly modulate photomorphogenic traits such as flowering time. The role of BAS1 and SOB7 in photomorphogenesis has been investigated by studying null-mutant genetic interactions with the photoreceptors phyA, phyB, and cry1 with regard to seed germination and flowering time. The removal of BAS1 and/or SOB7 rescued the low germination rate of the phyA-211 phyB-9 double-null mutant. With regard to floral induction, bas1-2 and sob7-1 showed a complex set of genetic interactions with photoreceptor-null mutants. Histochemical analysis of transgenic plants harboring BAS1:BAS1-GUS and SOB7:SOB7-GUS translational fusions under the control of their endogenous promoters revealed overlapping and distinct expression patterns. BAS1’s expression in the shoot apex increases during the phase transition from short-to-long-day growth conditions and requires phyB in red light. In summary, BAS1 and SOB7 displayed both simple and complex genetic interactions with the phytochromes in a plant-stage specific manner.