TOMATOD: EVALUATION OF OBJECT DETECTION ALGORITHMS ON A NEW REAL-WORLD TOMATO DATASET

7 citations as recorded by crossref.

1. Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping Y. Zhang et al. https://doi.org/10.1038/s41597-026-06926-9
2. Real-Time Image Detection for Edge Devices: A Peach Fruit Detection Application E. Assunção et al. https://doi.org/10.3390/fi14110323
3. Easy domain adaptation method for filling the species gap in deep learning-based fruit detection W. Zhang et al. https://doi.org/10.1038/s41438-021-00553-8
4. Cherry CO Dataset: A Dataset for Cherry Detection, Segmentation and Maturity Recognition L. Cossio-Montefinale et al. https://doi.org/10.1109/LRA.2024.3393214
5. Excellent tomato detector based on pruning and distillation to balance accuracy and lightweight L. Huang et al. https://doi.org/10.1016/j.compag.2024.109520
6. YOLO-RCM: An Improved Tomato Maturity Detection Model for Complex Greenhouse Environments D. Chen et al. https://doi.org/10.3390/agronomy16121146
7. Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato G. Moreira et al. https://doi.org/10.3390/agronomy12020356