ArticleFrontiers in plant science2023
An efficient tomato-detection method based on improved YOLOv4-tiny model in complex environment.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 23 citations in OpenAlex.
- Rethinking feature representation and attention mechanisms in intelligent recognition of leaf pests and diseases in wheat.Scientific reports · 2025Article
- An improved YOLOv7 model based on Swin Transformer and Trident Pyramid Networks for accurate tomato detection.Frontiers in plant science · 2024Article
- An occluded cherry tomato recognition model based on improved YOLOv7.Frontiers in plant science · 2023Article
- Accurate and fast detection of tomatoes based on improved YOLOv5s in natural environments.Frontiers in plant science · 2023Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
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Abstract
Automatic and accurate detection of fruit in greenhouse is challenging due to complicated environment conditions. Leaves or branches occlusion, illumination variation, overlap and cluster between fruits make the fruit detection accuracy to decrease. To address this issue, an accurate and robust fruit-detection algorithm was proposed for tomato detection based on an improved YOLOv4-tiny model. First, an improved backbone network was used to enhance feature extraction and reduce overall computational complexity. To obtain the improved backbone network, the BottleneckCSP modules of the original YOLOv4-tiny backbone were replaced by a Bottleneck module and a reduced version of BottleneckCSP module. Then, a tiny version of CSP-Spatial Pyramid Pooling (CSP-SPP) was attached to the new backbone network to improve the receptive field. Finally, a Content Aware Reassembly of Features (CARAFE) module was used in the neck instead of the traditional up-sampling operator to obtain a better feature map with high resolution. These modifications improved the original YOLOv4-tiny and helped the new model to be more efficient and accurate. The experimental results showed that the precision, recall,
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