Data Labeler
The project focused on developing a high-quality labeled dataset to support machine learning models for plant classification within the field of botany. The primary emphasis was on maize (Zea mays), with the goal of improving automated identification, trait analysis, and disease recognition systems. The dataset was designed to support agricultural research, crop monitoring tools, and AI-driven agronomy solutions. Project Size and Scale i. Labeled and validated over 10,000 records related to maize cultivation and research. ii. Worked within a structured dataset comprising multiple annotation layers (taxonomSpecific Data Labeling Tasks Performed). iii. Annotated and classified plant-related textual data, including field notes, experimental records, and agronomic descriptions related to maize. iv. Categorized data into predefined taxonomic and phenotypic classes such as growth stage, leaf morphology, disease indicators, and environmental conditions. v. Standardized botanical terminology to ensure consistency across the dataset. vi. Performed entity tagging for key agronomic variables (e.g., plant height, yield traits, pest/disease mentions). vii. Conducted data cleaning, including removal of ambiguous, duplicate, or inconsistent entries. viii. Collaborated with agronomists and lab researchers to validate complex classifications and edge cases. Quality Assurance and Measures Adhered To: i. Maintained ≥95% labeling accuracy through regular audits and peer reviews. ii. Followed strict annotation guidelines aligned with botanical standards and project-specific ontologies. iii. Implemented double-blind validation for sensitive or complex classifications. iv. Conducted periodic inter-annotator agreement (IAA) checks to ensure consistency across labelers. v. Utilized version control and documentation protocols to track changes and maintain dataset integrity. vi. Participated in continuous feedback sessions with supervisors to improve annotation precision and efficiency Project / Research was conducted at: Obafemi Awolowo University Central Botany Laboratory Obafemi Awolowo University Farms March 2024 – February 2025