Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping

TL;DR

This paper introduces TomatoMAP, a large, annotated dataset of tomato images capturing multiple poses and growth stages, enabling improved AI-based fine-grained plant phenotyping.

Contribution

We created a comprehensive, multi-angle tomato dataset with detailed annotations and validated its effectiveness with deep learning models matching expert accuracy.

Findings

Deep learning models achieved expert-level accuracy and speed.

The dataset enables reliable automated phenotyping.

Models showed high inter-rater agreement with human experts.

Abstract

Observer bias and inconsistencies in traditional plant phenotyping methods limit the accuracy and reproducibility of fine-grained plant analysis. To overcome these challenges, we developed TomatoMAP, a comprehensive dataset for Solanum lycopersicum using an Internet of Things (IoT) based imaging system with standardized data acquisition protocols. Our dataset contains 64,464 RGB images that capture 12 different plant poses from four camera elevation angles. Each image includes manually annotated bounding boxes for seven regions of interest (ROIs), including leaves, panicle, batch of flowers, batch of fruits, axillary shoot, shoot and whole plant area, along with 50 fine-grained growth stage classifications based on the BBCH scale. Additionally, we provide 3,616 high-resolution image subset with pixel-wise semantic and instance segmentation annotations for fine-grained phenotyping. We validated our dataset using a cascading model deep learning framework combining MobileNetv3 for classification, YOLOv11 for object detection, and MaskRCNN for segmentation. Through AI vs. Human analysis involving five domain experts, we demonstrate that the models trained on our dataset achieve accuracy and speed comparable to the experts. Cohen's Kappa and inter-rater agreement heatmap confirm the reliability of automated fine-grained phenotyping using our approach.