Dronitech
Website:
dronitech.com
Job details:
Senior Field Plant Pathologist / Senior Agronomist
Crop Disease Surveillance, Field Protocol & Data Quality
Location: India, with field operations across selected rice- and wheat-growing states
Engagement: Senior consulting or contract role, aligned to mobilisation, pilot and seasonal field operations
About the role
We are seeking a Senior Field Plant Pathologist / Senior Agronomist to lead disease-surveillance design and quality control for a multi-state rice and wheat crop-health data-collection programme in India.
The role combines practical plant pathology, field-protocol design, disease identification, training, cross-calibration and end-to-end dataset quality ownership. You will work closely with the Operations Manager and field teams to ensure that disease observations are scientifically defensible, consistently scored and collected at the right crop growth stages and locations.
The programme will use drone-based multispectral surveys, ground observations, photographs, GPS-linked records and ArcGIS Survey123. Each field team is expected to comprise two pilots and one agronomist or field plant pathologist. The Senior Field Plant Pathologist will provide central technical leadership across the operation.
Key responsibilities
Survey design and disease timing
Advise which diseases, pests, deficiencies and abiotic stresses should be recorded for rice and wheat in the target states and seasons.
Define suitable incidence and severity scales for each condition.
Specify the crop growth stages at which each disease or stress becomes detectable and distinguishable.
Recommend when sampling should start and stop in each crop and season.
Define rules for distinguishing diseases from pests, nutrient deficiencies, herbicide injury, water stress, heat stress, lodging, senescence and other non-disease causes.
Disease-risk-based site selection
Advise where and when strong, findable disease signals are most likely.
Use crop stage, local weather, irrigation, varietal choice, cropping history and disease pressure to inform site selection.
Help identify representative sites and higher-risk areas, including fixed fields suitable for repeat visits.
Advise how routes and field productivity should be adapted to disease-risk windows.
Sampling parameters
Define the disease parameters that inform the sampling design, including expected landscape-scale prevalence, within-field incidence, severity categories, minimum observation/sample sizes, and rules for recording absence, uncertainty, mixed symptoms and multiple diseases. Specify the minimum photo, GPS, crop-stage and contextual information required to support each diagnosis.
Disease-identification tables
Review the current tables of diseases, pests, deficiencies and stresses to confirm that they reflect the disease environment in the target states and seasons. Assess whether categories can be distinguished visually by a Field Plant Pathologist with high confidence, whether important look-alike conditions are included, and whether appropriate “unknown,” “other” and mixed-condition options are available for Survey123.
Low-confidence and “unknown” diagnoses
Design the checking process for low-confidence or unknown cases. Recommend when photo review, second opinion, additional field evidence or laboratory confirmation is required; define who performs the review and the expected turnaround; estimate likely low-confidence rates; set the confidence threshold for mandatory review; and ensure that original and corrected diagnoses are both retained. Use recurring patterns to improve training, tables and protocols.
Training and cross-calibration
Deliver initial disease-identification and scoring training to Field Plant Pathologists and run cross-calibration exercises so that different people score the same symptoms consistently. Training should cover rice and wheat disease recognition, look-alike conditions, incidence and severity scoring, photo standards, GPS-linked Survey123 records, confidence categories and appropriate use of “unknown.” Maintain consistency between people, teams, states, crops and points in the season.
End-to-end data quality
Own the quality of the disease-label dataset by reviewing incoming records, auditing photographs against diagnoses, checking for missing or inconsistent information, monitoring systematic error or diagnostic drift by person, team, crop, state and growth stage, and maintaining a review log for low-confidence, corrected and rejected records. Recommend retraining, protocol clarification or changes to the identification tables where required, and provide regular quality summaries to project leadership.
Rotas and workload
Work with the Operations Manager on rotas, coverage and workload across states so surveying remains on schedule through both seasons. Advise on disease-risk windows, repeat-visit timing, additional field capacity, site prioritisation, outbreak escalation and the practical daily limit for diagnoses and quality reviews.
Expected deliverables
Rice and wheat disease-surveillance and field-sampling protocol.
Reviewed disease, pest, deficiency and stress identification tables.
Disease-specific incidence and severity scales.
Crop-stage guidance for starting and stopping surveillance.
Disease-risk-based site-selection and route-planning guidance.
Low-confidence and unknown-diagnosis review rules.
Initial training materials and cross-calibration exercises.
Disease-data quality-control, audit and escalation process.
Recurring quality reports covering errors, corrections, drift and retraining actions.
Technical input into field rotas, workload planning and seasonal mobilisation.
Essential experience
Senior experience in plant pathology, crop protection, agronomy or a related discipline.
Strong practical experience diagnosing crop diseases under field conditions.
Ability to distinguish diseases from pests, deficiencies and abiotic stresses.
Experience with rice and wheat; experience across both crops is strongly preferred.
Experience developing or reviewing field protocols, disease scales or crop-health survey methods.
Experience training field staff and running cross-calibration or inter-observer consistency exercises.
Experience reviewing field photographs and GPS-linked observations.
Ability to define practical quality thresholds and escalation rules.
Strong communication skills and ability to convert scientific advice into simple field instructions.
Willingness to coordinate remotely and travel across multiple states as required.
Desirable experience
Drone-based crop-health surveys or multispectral imagery; ArcGIS Survey123 or similar digital field-data platforms; sampling design using prevalence, incidence and severity; multi-state agricultural surveys; diagnostic laboratories or expert-review networks; and familiarity with Indian rice and wheat production systems.
Working relationships and success measures
You will work with the central Operations Manager, Field Plant Pathologists, agronomists, drone pilots, field operators, Dronitech project leadership and Outbreak Labs. Success will be measured by a scientifically defensible protocol, consistent labels and scores, timely review of uncertain cases, complete GPS-linked and photographically supported records, early detection of diagnostic drift, disease-risk-informed routes and effective team training.
How to apply
Please send your CV or professional profile, a short summary of relevant rice and wheat disease-diagnostic experience, examples of field protocols or training programmes led or reviewed, availability during mobilisation and seasonal operations, preferred engagement model and fee expectations, and two relevant professional references.
Subject: Senior Field Plant Pathologist / Senior Agronomist — Crop Disease Surveillance
This job description is a draft for recruitment use and will be finalised against the confirmed project scope, states, crop calendar and engagement terms.
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