An end-to-end lightweight model for grape and picking point simultaneous detection

Zhao, Ruzhun; Zhu, Yuchang; Li, Yuanhong

doi:10.1016/j.biosystemseng.2022.08.013

Cited by 17 publications

(1 citation statement)

References 34 publications

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“…The mAP of target detection by the improved SSD algorithm was 97.32%, and the speed of detection is 41.15 FPS. Ruzhun Zhao et al [12] built a lightweight end-to-end model, YOLO-GP, for the accurate detection of grape clusters and their picking points, and the mAP reached 93.27% while reducing certain parameter weights. Zhaoyi Chen et al [13] designed a plant disease detection model, a new involute bottleneck module is used to capture remote information in space while reducing network parameters and computation.…”

Section: Introductionmentioning

confidence: 99%

A lightweight detection model for greenhouse-cultivated strawberries based on YOLOv5

Xue

2023

Preprint

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The efficient detection of strawberries has great significance in the realization of strawberry production estimation and automatic picking in the greenhouse. Factors such as the complex growing environment in the field and fruit aggregation shading, especially for immature strawberries at the turning stage and mature strawberries with high similarity in shape, size, and even color, resulted in low accuracy of detection. This research aims at establishing an improved lightweight model based on YOLOv5 which is for strawberry ripeness detection in natural environments, in response to the problems of some current detection models whose structures are complex and difficult to deploy on low-cost devices. The method first adds a smaller target detection layer to the original network, then removes part of the deep structure of the network to reduce the complexity of the model, by adjusting the depth-multiple and width-multiple partially to achieve a lighter model while ensuring accuracy. After that, the performance of the model was further improved by introducing the BiFPN structure and SimAM attention module. The experimental results show that the improved model provides an effective method for detecting strawberries in natural environments. Compared with the YOLOv5s model, the improved model has a 60.35% reduction in parameters and a 55.47% reduction in the model size, and the mAP, mAP0.5:0.95, and F1 are improved to 91.86%, 79.04%, and 90.9%, respectively. The model proposed in this research proves an excellent generalization performance in three strawberry test sets and provides a reference for subsequent deployment in small/low-cost picking robots.

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Section: Introductionmentioning

confidence: 99%

A lightweight detection model for greenhouse-cultivated strawberries based on YOLOv5

Xue

2023

Preprint

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Leveraging Computer Vision for Precision Viticulture

Vrochidou

Papakostas

2023

Computer Vision and Machine Learning in Agriculture, Volume 3

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Application of image analysis and machine learning for the assessment of grape (Vitis L.) berry behavior under different storage conditions

Ropelewska,

Noutfia

2023

Eur Food Res Technol

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Fresh grapes are characterized by a short shelf life and are often subjected to quality losses during post-harvest storage. The quality assessment of grapes using image analysis may be a useful approach using non-destructive methods. This study aimed to compare the effect of different storage methods on the grape image texture parameters of the fruit outer structure. Grape bunches were stored for 4 weeks using 3 storage methods ( – 18 °C, + 4 °C, and room temperature) and then were subjected subsequently to image acquisition using a flatbed scanner and image processing. The models for the classification of fresh and stored grapes were built based on selected image textures using traditional machine learning algorithms. The fresh grapes and stored fruit samples (for 4 weeks) in the freezer, in the refrigerator and in the room were classified with an overall accuracy reaching 96% for a model based on selected texture parameters from images in color channels R, G, B, L, a, and b built using Random Forest algorithm. Among the individual color channels, the carried-out classification for the R color channel produced the highest overall accuracies of up to 92.5% for Random Forest. As a result, this study proposed an innovative approach combining image analysis and traditional machine learning to assess changes in the outer structure of grape berries caused by different storage conditions.

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An end-to-end lightweight model for grape and picking point simultaneous detection

Cited by 17 publications

References 34 publications

A lightweight detection model for greenhouse-cultivated strawberries based on YOLOv5

A lightweight detection model for greenhouse-cultivated strawberries based on YOLOv5

Leveraging Computer Vision for Precision Viticulture

Application of image analysis and machine learning for the assessment of grape (Vitis L.) berry behavior under different storage conditions

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