TomDetLeaf: A Realistic Multi-Source Dataset for Real-Time Tomato Leaf Detection

Plant diseases remain a major threat to crop productivity, especially where timely diagnosis is difficult. This paper introduces TomDetLeaf, a new annotated dataset designed for tomato leaf detection in diverse agricultural environments, supporting the development of generalizable deep learning models for edge AI deployment. Unlike existing datasets such as PlantVillage, which consist mainly of single-leaf images captured under controlled conditions, TomDetLeaf integrates heterogeneous sources including the Taiwan dataset, climate-controlled green-houses, hydroponic systems and farm environments. The dataset combines single-leaf and multi-leaf images, realistic backgrounds and varying illumination, addressing a key gap that limits the real-world robustness of current models. To demonstrate its utility, we trained and evaluated YOLOv8 on both the original Taiwan dataset and our proposed TomDetLeaf. Results show that YOLOv8 trained on TomDetLeaf achieved 88.3% mAP@0.5, 81.8% precision, and 82.7% recall, exceeding the Taiwan-subset baseline of 77.4% mAP@0.5, 81.6% precision, and 67.6% recall. This validates the contribution of TomDetLeaf in improving detection accuracy and generalization under realistic conditions. By providing a diverse, deployment-ready dataset, this work bridges the gap between theoretical benchmarks and practical real-time applications.

@article{Dhiab2025,
  title     = {TomDetLeaf: A Realistic Multi-Source Dataset for Real-Time Tomato Leaf Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {9},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yassmine Ben Dhiab and Mohamed Ould-Elhassen Aoueileyine and Abdallah Namoun and Ridha Bouallegue},
  doi       = {10.14569/IJACSA.2025.0160991},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160991}
}