Abstract
Peanut leaf disease detection in complex field environments faces two major challenges: distinguishing visually similar symptoms and identifying severely occluded lesions. This study presents YOLOv8-MSDH, a lightweight detection model built upon an improved YOLOv8 framework to address these issues. Four architectural enhancements are introduced. First, the MHSA attention mechanism is integrated to enhance sequential feature dependency modeling and suppress background noise. Second, the Slim-Neck module is adopted for neck reconstruction, which lowers computational cost and facilitates multi-scale feature fusion. Third, the original C2f module is replaced with the C2f-Dual module to further reduce computational load. Last, the HWD downsampling module is incorporated into the backbone to improve the retention of disease-specific features while promoting lightweight design. Evaluated on a peanut leaf disease dataset, YOLOv8-MSDH achieves 90.14% precision, 82.16% recall, 90.71% mAP50 and 72.14% mAP50-95 under complex conditions—surpassing the baseline YOLOv8 by 3.5, 0.7, 1.5 and 2.89 percentage points, respectively. Parameter count and computational complexity are reduced by 12.9% and 20.7%, confirming effective lightweighting. Operating at 509.95 FPS, the model maintains strong real-time performance and exhibits high robustness across varying lighting conditions.
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 8
Claim · model-written summary
YOLOv8-MSDH precision on the peanut disease dataset was 90.14%, 3.5 percentage points above YOLOv8.
- Taxon
- peanut
Arachis hypogaeae0 - Outcome
- precisione0
- Direction
- surpassinge0
- Value
- 90.14%e0
- Qualifier
- 3.5e0
- Comparator
- baseline YOLOv8e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Evaluated on a peanut leaf disease dataset, YOLOv8-MSDH achieves 90.14% precision, 82.16% recall, 90.71% mAP50 and 72.14% mAP50-95 under complex conditions—surpassing the baseline YOLOv8 by 3.5, 0.7, 1.5 and 2.89 percentage points, respectively.”
Claim · model-written summary
YOLOv8-MSDH recall was 82.16%, 0.7 percentage points above YOLOv8.
- Taxon
- peanutArachis hypogaeae0
- Outcome
- recalle0
- Direction
- surpassinge0
- Value
- 82.16%e0
- Qualifier
- 0.7e0
- Comparator
- baseline YOLOv8e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Evaluated on a peanut leaf disease dataset, YOLOv8-MSDH achieves 90.14% precision, 82.16% recall, 90.71% mAP50 and 72.14% mAP50-95 under complex conditions—surpassing the baseline YOLOv8 by 3.5, 0.7, 1.5 and 2.89 percentage points, respectively.”
Claim · model-written summary
YOLOv8-MSDH mAP50 was 90.71%, 1.5 percentage points above YOLOv8.
- Taxon
- peanutArachis hypogaeae0
- Outcome
- mAP50e0
- Direction
- surpassinge0
- Value
- 90.71%e0
- Qualifier
- 1.5e0
- Comparator
- baseline YOLOv8e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Evaluated on a peanut leaf disease dataset, YOLOv8-MSDH achieves 90.14% precision, 82.16% recall, 90.71% mAP50 and 72.14% mAP50-95 under complex conditions—surpassing the baseline YOLOv8 by 3.5, 0.7, 1.5 and 2.89 percentage points, respectively.”
Claim · model-written summary
YOLOv8-MSDH mAP50-95 was 72.14%, 2.89 percentage points above YOLOv8.
- Taxon
- peanutArachis hypogaeae0
- Outcome
- mAP50-95e0
- Direction
- surpassinge0
- Value
- 72.14%e0
- Qualifier
- 2.89e0
- Comparator
- baseline YOLOv8e0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Evaluated on a peanut leaf disease dataset, YOLOv8-MSDH achieves 90.14% precision, 82.16% recall, 90.71% mAP50 and 72.14% mAP50-95 under complex conditions—surpassing the baseline YOLOv8 by 3.5, 0.7, 1.5 and 2.89 percentage points, respectively.”
Claim · model-written summary
The model reduced parameter count by 12.9%.
- Taxon
- PeanutArachis hypogaeat0
- Outcome
- Parameter counte0
- Direction
- reducede0
- Value
- 12.9%e0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Parameter count and computational complexity are reduced by 12.9% and 20.7%”
t0“Peanut”
Claim · model-written summary
The model reduced computational complexity by 20.7%.
- Taxon
- PeanutArachis hypogaeat0
- Outcome
- computational complexitye0
- Direction
- reducede0
- Value
- 20.7%e0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Parameter count and computational complexity are reduced by 12.9% and 20.7%”
t0“Peanut”
Claim · model-written summary
The model operated at 509.95 FPS.
- Taxon
- PeanutArachis hypogaeat0
- Outcome
- real-time performancee0
- Value
- 509.95 FPSe0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Operating at 509.95 FPS, the model maintains strong real-time performance”
t0“Peanut”
Claim · model-written summary
The model showed high robustness across varying light conditions.
- Taxon
- PeanutArachis hypogaeat0
- Outcome
- high robustnesse0
- Qualifier
- varying lighting conditionse0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“exhibits high robustness across varying lighting conditions”
t0“Peanut”