Abstract
Crop growth status detection is significant in agriculture and is vital in planting planning, crop yield, and reducing the consumption of fertilizers and workforce. However, little attention has been paid to detecting the growth status of each crop. Accuracy remains a challenging problem due to the small size of individual targets in the image. This paper proposes an object detection model, HR-YOLOv8, where HR means High-Resolution, based on a self-attention mechanism to alleviate the above problem. First, we add a new dual self-attention mechanism to the backbone network of YOLOv8 to improve the model’s attention to small targets. Second, we use InnerShape(IS)-IoU as the bounding box regression loss, computed by focusing on the shape and size of the bounding box itself. Finally, we modify the feature fusion part by connecting the convolution streams from high resolution to low resolution in parallel instead of in series. As a result, our method can maintain a high resolution in the feature fusion part rather than recovering high resolution from low resolution, and the learned representation is more spatially accurate. Repeated multiresolution fusion improves the high-resolution representation with the help of the low-resolution representation. Our proposed HR-YOLOv8 model improves the detection performance on crop growth states. The experimental results show that on the oilpalmuav dataset and strawberry ripeness dataset, our model has fewer parameters compared to the baseline model, and the average detection accuracy is 5.2% and 0.6% higher than the baseline model, respectively. Our model’s overall performance is much better than other mainstream models. The proposed method effectively improves the ability to detect small objects.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 5
Claim · model-written summary
HR-YOLOv8 improved average detection accuracy on the oilpalmuav dataset by 5.2% against baseline.
Verbatim
e0“on the oilpalmuav dataset and strawberry ripeness dataset, our model has fewer parameters compared to the baseline model, and the average detection accuracy is 5.2% and 0.6% higher than the baseline model, respectively”
t0“Crop Growth Status”
Claim · model-written summary
HR-YOLOv8 improved average detection accuracy on strawberry ripeness data by 0.6%.
- Taxon
- strawberry
_single2.jpg?width=60)
Fragaria ×ananassae0 - Outcome
- average detection accuracye0
- Direction
- highere0
- Value
- 0.6%e0
- Comparator
- baseline modele0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
_single2.jpg?width=500)
Verbatim
e0“strawberry ripeness dataset, our model has fewer parameters compared to the baseline model, and the average detection accuracy is 5.2% and 0.6% higher than the baseline model, respectively”
Claim · model-written summary
HR-YOLOv8 used fewer parameters than baseline on the two crop datasets.
- Taxon
- strawberryFragaria ×ananassae0
- Outcome
- parameterse0
- Direction
- fewere0
- Comparator
- baseline modele0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“on the oilpalmuav dataset and strawberry ripeness dataset, our model has fewer parameters compared to the baseline model”
Claim · model-written summary
The model had better overall performance than other mainstream models.
Not reportedTaxon
Verbatim
e0“Our model’s overall performance is much better than other mainstream models.”
Claim · model-written summary
The method improved small object detection ability.
Not reportedTaxonValue
Verbatim
e0“The proposed method effectively improves the ability to detect small objects.”
t0“Crop Growth Status”