PhenoSnap: An AI-Powered Web Application for Automated Specialty Crop Trait Extraction

Abstract

Manual quantification of specialty crop traits, such as flowers and fruits, is often labor-intensive, time-consuming, and inconsistent, limiting scalability and precision. We present PhenoSnap, an artificial intelligence (AI)-powered web application that provides an intuitive and efficient interface for automated specialty crop trait extraction from images. PhenoSnap bridges the gap between advanced computer vision technologies and practical agricultural applications by eliminating the need for programming expertise. This ready-to-use solution can enable growers, breeders, and Extension faculty to accelerate field work and enhance decision-making related to strawberry and tomato yield estimation for breeding selections and strawberry runner management. Written by Santhi Daggubati, Xu Wang, Xue Zhou, Shubham Singh, and Jessica Chitwood-Brown, and published by the UF/IFAS Department of Agricultural and Biological Engineering, June 2026.

Code and data availability

The article describes PhenoSnap, a publicly accessible AI web application for specialty crop trait extraction, and cites a publicly released Dryad imagery dataset (Zhou et al. 2025b) that is a subset of the training data for the Strawberry Runner model. Both are paper-specific, public, and actionable. No author code or