Apple detection during different growth stages in orchards using the improved YOLO-V3 model
By Yunong Tian, Guodong Yang, Zhe Wang, Hao Wang, En Li, Zi-ze Liang. Computers and Electronics in Agriculture, 2019
963 citations recorded by the source corpus.
Abstract Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304 s per frame at 3000 × 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/71a0f60d000253f9ca13ba4819e2c8ac91d68715
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In Analytical Chemistry
In Plant Science
Citations in the corpus
- Cucumber Fruits Detection in Greenhouses Based on Instance Segmentation (2019, methodology): “[20] proposed an improved YOLO V3 model to detect apples during different growth stages in orchards.”
- A Review on Deep Learning in UAV Remote Sensing (2021, background): “Counting fruits is not something entirely new in DL-based approaches, some papers demonstrated the e ff ectiveness of bounding-box and point-feature methods to extract it [22, 182, 100] aside from several di ff erences in occlusion, lightning, fruit size, and image corruption.”
- A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond (2023, methodology): “YOLO models have been used in agriculture to detect and classify crops [11, 12], pests, and diseases [13], assisting in precision agriculture techniques and automating farming processes.”
- A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS (2023, methodology): “YOLO models have been used in agriculture to detect and classify crops [21, 22], pests, and diseases [23], assisting in precision agriculture techniques and automating farming processes.”
- A Review on State-of-the-Art Power Line Inspection Techniques (2020, methodology): “Vision detection is a common and mature detection and measurement method, and it has been applied to many scenarios, such as industrial robot [91], agricultural plant protection [92], and underwater measurement [93].”
- Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar (2020, background): “For the purpose of detecting apples at varying stages of the life cycle in an orchard, Tian et al. [15] evaluate different deep learning techniques for detection.”