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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