Olalekan K.

Key Skills

Software

Data Annotation Tech

Mindrift

Other

Top Subject Matter

Agriculture - Crop science

Data Analytics

Remote sensing - Phenotyping

Top Data Types

3D Sensor

Text

Image

Top Task Types

Data Collection

Classification

Object Detection

Text Generation

Computer Programming/Coding

Question Answering

Freelancer Overview

Graduate Research Fellow (AI Trainer; Data Labeler/Annotator; Domain Expert Reviewer; Image/Remote-Sensing Data Reviewer. Brings 5+ years of professional experience across professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Master of Science, University of Ibadan (UI) (2024) and Bachelor of Science, Federal University of Agriculture, Abeokuta (FUNAAB) (2019). AI-training focus includes data types such as Phenotypinhg with 3D Sensor and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

Research Supervisor, Cassava Breeding Program (AI-ready dataset validation; domain-expert reviewer) | IITA

OtherImage

Acts as an AI-ready data specialist by supervising the generation, validation, and review of multi-source datasets used for cassava phenotyping and crop-model development. Performs annotation-quality style checks through guideline adherence, metadata verification, and ground-truth validation across drone imagery, sensor measurements, and lab assay inputs. Validates model-ready datasets by identifying outliers/error patterns and ensuring consistency between farm records, imagery-derived traits, and environmental time-series. • Produces structured validation datasets using drone/UAV workflows and sensor-collected observations • Applies rubric-like review practices to ensure labeled data quality and metadata correctness • Integrates farm records (planting methods, fertilizer, pest/disease, yield outcomes) to check data consistency and drivers • Translates imaging and sensor outputs into reviewable, documented technical findings

2024 - Present

Graduate Research Fellow (Time-Series/Sensor Data Evaluator; ML Output Validator) | IITA

Other3D Sensor

Evaluated time-series sensor and environmental monitoring data to support AI/ML modeling and crop performance analyses. Conducted quality-control review and outlier/error detection to improve dataset cleanliness and ground-truth comparability. Helped validate that IoT weather/soil readings and related sensor outputs remained consistent with agronomic and experimental records. • Reviewed IoT weather/soil sensing data streams for completeness and correctness • Performed metadata checks and data wrangling for time-series integrity • Supported quality assurance for sensor-to-label alignment across monitoring periods • Helped verify ML-consistency by assessing correlations and predictive reliability

2022 - 2024

Graduate Research Fellow (AI Trainer; Data Labeler/Annotator; Domain Expert Reviewer; Image/Remote-Sensing Data Reviewer; Time-Series/Sensor Data Evaluator; ML Output Validator) | IITA

Other

Served as an AI trainer and domain expert reviewer by evaluating remote-sensing and image-derived datasets for suitability in crop and cassava breeding models. Performed guideline-adherence checks, metadata verification, and quality-control review to ensure data reliability and consistent ground-truth alignment. Supported ML output validation by confirming model-readiness signals and identifying labeling errors, outliers, and data-model inconsistencies. • Reviewed drone/UAV imagery and vegetation-index related artifacts for correct interpretation • Conducted rubric-based scoring and data quality checks across multi-source inputs • Compared image-derived predictors against field-based ground truth for reliability assessment • Verified model outputs and flagged issues affecting prediction consistency and accuracy

2022 - 2024

Research Assistant | Cassava Breeding Program (data quality support for multi-source agronomic datasets) | IITA

OtherTextData Collection

Supported data preparation and curation for breeding and research workflows by maintaining consistent agronomic field-data capture and review practices. Ensured trial outputs were complete, accurately documented, and suitable for downstream analysis and machine-learning readiness. Contributed to cleaning and quality improvement of datasets by checking observation completeness and consistency. • Supported trial design and standardized collection of trait, soil, and weather records • Reviewed captured field observations for completeness and basic quality issues • Helped organize agronomic documentation for reproducible research decisions • Assisted team workflows requiring accuracy, quality control, and timely submission

2019 - 2020

Education

Work History