Collect. Annotate. Validate. Build Better AI
Weeds that look like crops, early-stage disease, and animals that overlap in a herd are hard to label consistently. We annotate image, video, drone, and satellite data through a multi-stage QA process, so your agritech model trains on labels it can trust.
The accuracy of your labels is the accuracy of your AI model.
Agritech models learn from what annotators mark. Inconsistent labels become sprayers that treat the wrong plants, disease alerts that arrive late, inaccurate fruit and stand counts, and livestock monitoring that loses track of individual animals.
Qualitas Global labels crop, weed, disease, and yield data, livestock behavior and health data, and drone and satellite imagery, along with terrain and obstacle data for autonomous farm robots. Annotators are trained on your crop species, growth stages, and class definitions before production begins, and every batch passes internal and external quality checks.
Our semantic segmentation services separate crops from the background, while object detection annotation and bounding box annotation locate row crops, mixed crops, orchards, plantations, and nursery plants, and classify crop species and variety. Annotators also localize and classify weeds in inter-row and in-row positions, including banned and unwanted plants, and map weed density, maturity, and spread for AI annotation models.
We use polygon and bounding box annotation to label disease on leaves, fruit, stems, roots, and trees, score severity, track progression, and mark pests such as locusts and bird damage. Labels also cover nutrient deficiency, water, heat, and cold stress, flood, drought, wind, and lodging damage, and crop health status as healthy, stressed, or dead.
Our image annotation services label plant emergence, stand counts, gaps and fruit, including occluded fruit, and classify ripeness and harvest readiness. Annotators also capture fruit size, biomass, ear, head, pod and flower counts, plant height, leaf count, stem thickness, root traits, growth stage and growth tracking, producing human-annotated data for yield models.
We apply semantic segmentation to canopy and tree crowns, and label canopy width, height, volume, density, and leaf area, along with field, plot, and parcel boundaries, headlands, access roads, and water channels. Soil moisture, waterlogging, dry zones, erosion, compaction, cracks, bare soil, and irrigation leaks, including sprinkler and drip lines, are labeled for annotation for machine learning.
Our object detection annotation locates target weeds and crops, classifies regions as spray or no-spray, and labels spray coverage and quality. We also annotate obstacles, traversable terrain, and farm equipment, so autonomous tractor and robot perception models can navigate and work safely.
Our image annotation services and video annotation services label drone footage for crop scouting and infrastructure inspection, and satellite imagery for crop classification, NDVI interpretation, and yield prediction support. Labels for field change, flood and drought mapping, and deforestation give monitoring models consistent data to learn from.
We label livestock across breeds for detection and automated counting, and identify individual animals through faces, tags, and ID numbers. Multi-object, trajectory, and live herd tracking, long-term re-identification, and zone entry and exit labels support video annotation and object tracking annotation.
Our video annotation services tag behaviors such as standing, walking, lying, rumination, mounting, aggression, social interaction, maternal and isolation behavior, and mark lameness, injuries, skin disease, and lesions, with gait analysis. We also label body condition and weight, heat stress, respiratory distress, abnormal posture and mortality, and provide full-body segmentation, key point annotation and body part labels for hooves, udders, horns, joints and tail.
We annotate feed intake, feeding duration, feed bunk occupancy, feed wastage, water intake and water point occupancy in barn and field footage. Labels also cover herd clustering, dominance and social networks, stall occupancy, bedding quality, ventilation, manure accumulation and barn equipment faults.
Our object detection annotation counts fish, poultry and bees, estimates fish biomass and poultry density, and labels swimming and pecking behavior. We also mark dead fish, nets, water quality indicators, eggs, nest occupancy, and beehive activity.
Explore our data collection services.
Domain-trained expert annotators and daily throughput tracking against SLA targets.
A rigorous multi-level quality assurance process that ensures a 97%+ quality benchmark, validated on every project.
A risk-free 2-day pilot before full production, and team deployment within 24 business hours of sign-off. You see our work before you commit to volume.
Every engagement is tailored to your goals, with annotation guidelines, QA, and delivery cadence adapted to your project.
Pay-per-verified-productive-hour billing. You are not paying for our learning curve or mistakes, our internal rework, or idle bench time.
ISO/IEC 27001:2022, ISO 9001:2015, TISAX Level 3, HIPAA, and GDPR compliance. Biometric access, 24*7 CCTV monitoring, and policies and procedures that prohibit cameras and unauthorised storage devices on the floor.
100% in-house delivery. No outsourcing, no work-from-home access to client data, no exceptions.
Agricultural image annotation is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For agri-tech, precision agriculture & livestock, the work begins with a written label taxonomy, clear inclusion and exclusion rules, and representative sample data. The exact scope depends on the model objective and the client's acceptance criteria.
Depending on the approved scope, agri-tech, precision agriculture & livestock projects may involve crop rows, plants, weeds, fruits, livestock, animal posture, field conditions, and visible crop stress. The team should define each label, the unit being annotated, how to treat uncertain cases, and whether the task is frame-level, object-level, document-level, or text-level. These examples describe possible project requirements, not a guarantee that every modality or use case is currently offered.
Output formats and tooling should be confirmed during discovery rather than assumed from a general industry page. The project brief should specify the required schema, field names, class IDs, metadata, export structure, and any client platform access. Qualitas Global should confirm compatibility with the requested tools before the pilot begins; no specific format or third-party platform is promised here.
A quality plan should define the annotation guideline, reviewer responsibilities, sampling or review coverage, error categories, escalation rules, and acceptance criteria before production. Multi-level quality checks are a stated part of the intended service approach, but the exact workflow and quality SLA need internal confirmation. Disagreements should be documented, corrected, and reflected in updated guidance where the same issue could recur.
Data access, storage, transfer, retention, deletion, and physical work arrangements should be agreed before any sensitive files are shared. The project owner should confirm the applicable security controls and whether any certification or regulatory commitment applies to the specific engagement. Do not assume a particular ISO standard, HIPAA status, GDPR status, or other certification without current internal evidence.
Pricing should be scoped around the task definition, data complexity, annotation guideline, expected review effort, language or domain requirements, and the delivery arrangement. The stated commercial model is based on productive hours, with scope and acceptance criteria agreed in advance. A quote should clarify what counts as productive work, how rework is treated, and which activities are included, without relying on an unverified public price.
Yes, the recommended first step is to discuss a representative sample and agree on a pilot scope before committing a larger dataset. The pilot can test the label definitions, edge-case handling, reviewer workflow, output structure, and acceptance criteria. Start dates, sample size, turnaround, staffing, and scale-up capacity should be confirmed by the project team after reviewing the data and requirements.
Common challenges include Seasonal variation, lighting, growth stages, overlapping plants, animal movement, and differences between visually similar classes. A clear guideline should show positive and negative examples, explain ambiguous cases, and define when annotators should escalate rather than guess. To get started, share the intended model or business use, data types, approximate volume, required outputs, security constraints, and a sample if permitted. Qualitas Global can then confirm fit and propose a pilot.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.
