Abstract
The rapid and accurate detection of broccoli seedling planting quality is crucial for the implementation of robotic intelligent field management. However, existing algorithms often face issues of false detections and missed detections when identifying the categories of broccoli planting quality. For instance, the similarity between the features of broccoli root balls and soil, along with the potential for being obscured by leaves, leads to false detections of “exposed seedlings”. Additionally, features left by the end effector resemble the background, making the detection of the “missed hills” category challenging. Moreover, existing algorithms require substantial computational resources and memory. To address these challenges, we developed Seedling-YOLO, a deep-learning model dedicated to the visual detection of broccoli planting quality. Initially, we designed a new module, the Efficient Layer Aggregation Networks-Pconv (ELAN_P), utilizing partial convolution (Pconv). This module serves as the backbone feature extraction network, effectively reducing redundant calculations. Furthermore, the model incorporates the Content-aware ReAssembly of Features (CARAFE) and Coordinate Attention (CA), enhancing its focus on the long-range spatial information of challenging-to-detect samples. Experimental results demonstrate that our Seedling-YOLO model outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7 in terms of speed and precision, particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively. The model achieved a mean Average Precision of 0.5 (mAP@0.5) of 94.3% and a frame rate of 29.7 frames per second (FPS). In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s, Seedling-YOLO exhibited optimal efficiency and precision. It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min, meeting the requirements for real-time and precise detection. This model can be deployed on seedling replenishment robots, providing a visual solution for robots, thereby enhancing vegetable yield.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
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Claims 7
Claim · model-written summary
Seedling-YOLO outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny and YOLOv7 in speed and precision for broccoli planting-quality detection.
- Taxon
- broccoli
Brassica oleracea var. italicat0 - Outcome
- speed and precisione1
- Direction
- outperformse1
- Comparator
- YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7e1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Field Broccoli Seedling Transplanting Quality”
e1“Experimental results demonstrate that our Seedling-YOLO model outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny, and YOLOv7 in terms of speed and precision”
Claim · model-written summary
Seedling-YOLO reached an Average Precision of 94.2% for exposed seedlings.
- Taxon
- broccoliBrassica oleracea var. italicat0
- Outcome
- Average Precision (AP)e2
- Value
- 94.2%e2
- Qualifier
- exposed seedlingse2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Field Broccoli Seedling Transplanting Quality”
e2“particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively”
Claim · model-written summary
Seedling-YOLO reached an Average Precision of 92.2% for missed hills.
- Taxon
- broccoliBrassica oleracea var. italicat0
- Outcome
- Average Precision (AP)e2
- Value
- 92.2%e2
- Qualifier
- missed hillse2
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Field Broccoli Seedling Transplanting Quality”
e2“particularly in detecting ‘exposed seedlings’ and ‘missed hills’-key categories impacting yield, with Average Precision (AP) values of 94.2% and 92.2%, respectively”
Claim · model-written summary
Seedling-YOLO achieved a mean Average Precision of 0.5 (mAP@0.5) of 94.3%.
- Taxon
- broccoliBrassica oleracea var. italicat0
- Outcome
- mean Average Precision of 0.5 (mAP@0.5)e3
- Value
- 94.3%e3
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Field Broccoli Seedling Transplanting Quality”
e3“The model achieved a mean Average Precision of 0.5 (mAP@0.5) of 94.3%”
Claim · model-written summary
Seedling-YOLO ran at a frame rate of 29.7 frames per second.
- Taxon
- broccoliBrassica oleracea var. italicat0
- Outcome
- frame ratee4
- Value
- 29.7 frames per second (FPS)e4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Field Broccoli Seedling Transplanting Quality”
e4“a frame rate of 29.7 frames per second (FPS)”
Claim · model-written summary
In field tests, Seedling-YOLO reached an actual detection precision of 93% for broccoli seedlings.
- Taxon
- broccoliBrassica oleracea var. italicat0
- Outcome
- actual detection precisione5
- Value
- 93%e5
- Setting
- field testse6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Field Broccoli Seedling Transplanting Quality”
e5“It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min”
e6“In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s”
Claim · model-written summary
In field tests, Seedling-YOLO reached a detection efficiency of 180 plants/min.
- Taxon
- broccoliBrassica oleracea var. italicat0
- Outcome
- detection efficiencye5
- Value
- 180 plants/mine5
- Setting
- field testse6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Field Broccoli Seedling Transplanting Quality”
e5“It achieved an actual detection precision of 93% and a detection efficiency of 180 plants/min”
e6“In field tests conducted with double-row vegetable ridges at a plant spacing of 0.4 m and robot speed of 0.6 m/s”