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“mean average precision (mAP)@0.5”

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Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv8 achieved a mean Average Precision (mAP@0.5) of 0.995 for chili pepper disease detection.

Taxon: chili pepperCapsicumt0
Outcome: mean Average Precision (mAP@0.5)e2
Value: 0.995e2

Verbatim

t0
“chili pepper disease detection”
✓ located verbatim in the source · title, characters 60–90

e2
“It achieved a mean Average Precision (mAP@0.5) of 0.995 and mAP@0.5–0.95 of 0.941”
✓ located verbatim in the source · abstract, characters 1308–1389

Title

Deep learning enables precision agriculture for sustainable chili pepper disease detection in Benin

Mireille Gloria Founmilayo Odounfa; Castro Gbêmêmali Hounmenou; Valère Kolawolé Salako; et al. Discover Artificial Intelligence 2025. doi:10.1007/s44163-025-00583-4

Other claims from this paper (4)

Method / resource YOLOv8 outperformed other models in detecting and localizing chili pepper leaf diseases in real time.
Method / resource YOLOv8 achieved a mAP@0.5–0.95 of 0.941 for chili pepper disease detection.
Method / resource YOLOv8 precision and recall exceeded 99%.
Method / resource MobileNetV2 and DenseNet121 achieved 96.25% accuracy in chili pepper disease classification.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Improved YOLOv5s reached 0.961 mean average precision (mAP) (0.5).

Taxon: RiceOryza sativat0
Outcome: mean average precision (mAP) (0.5)e0
Direction: higheste0
Value: 0.961e0

Verbatim

t0
“Rice”
✓ located verbatim in the source · title, characters 20–24

e0
“Improved YOLOv5s achieved the highest F1-Score of 0.931, 0.961 in mean average precision (mAP) (0.5), and 0.648 in mAP (0.5:0.9).”
✓ located verbatim in the source · abstract, characters 1119–1248

Title

Deep-Learning-Based Rice Disease and Insect Pest Detection on a Mobile Phone

Jizhong Deng; Chang Yang; Kanghua Huang; et al. Agronomy 2023. doi:10.3390/agronomy13082139

Other claims from this paper (14)

Method / resource Improved YOLOv5s reached 0.931 F1-Score.
Method / resource Improved YOLOv5s reached 0.648 mAP (0.5:0.9).
Method / resource Improved YOLOv5s reduced network parameters by 47.5.
Method / resource Improved YOLOv5s reduced model size by 45.7.
Method / resource Improved YOLOv5s reduced floating point operations per second (FLOPs) by 48.7%.
Method / resource Improved YOLOv5s increased inference speed by 38.6% versus original YOLOv5s.
Method / resource Improved YOLOv7-tiny outperformed its original version in detection accuracy but ranked below Improved YOLOv5s.
Method / resource Improved YOLOv5s performed better on large rice disease and pest targets.
Method / resource Improved YOLOv7-tiny was more accurate on small targets.
Method / resource On mobile FP16, Improved YOLOv5s achieved 0.925 precision.
Method / resource On mobile FP16, Improved YOLOv5s achieved 0.939 recall.
Method / resource On mobile FP16, Improved YOLOv5s achieved 374 ms/frame inference speed.
Method / resource Both improved models accurately identified rice diseases and insect pests.
Method / resource The mobile application provided a reference for fast field diagnosis.

Method / resourceobservedoriginal resultpositivemeasured

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

Verbatim

t0
“Field Broccoli Seedling Transplanting Quality”
✓ located verbatim in the source · title, characters 62–107

e3
“The model achieved a mean Average Precision of 0.5 (mAP@0.5) of 94.3%”
✓ located verbatim in the source · abstract, characters 1621–1690

Title

Seedling-YOLO: High-Efficiency Target Detection Algorithm for Field Broccoli Seedling Transplanting Quality Based on YOLOv7-Tiny

Tengfei Zhang; Jinhao Zhou; Wei Liu; et al. Agronomy 2024. doi:10.3390/agronomy14050931

Other claims from this paper (6)

Method / resource Seedling-YOLO outperforms YOLOv4-tiny, YOLOv5s, YOLOv7-tiny and YOLOv7 in speed and precision for broccoli planting-quality detection.
Method / resource Seedling-YOLO reached an Average Precision of 94.2% for exposed seedlings.
Method / resource Seedling-YOLO reached an Average Precision of 92.2% for missed hills.
Method / resource Seedling-YOLO ran at a frame rate of 29.7 frames per second.
Method / resource In field tests, Seedling-YOLO reached an actual detection precision of 93% for broccoli seedlings.
Method / resource In field tests, Seedling-YOLO reached a detection efficiency of 180 plants/min.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The SEYOLOX-tiny model detects maize tassels with a mean average precision (mAP@0.5) of 95.0%.

Taxon: maizeZea mayse0
Outcome: mean average precision (mAP@0.5)e1
Value: 95.0%e1

Verbatim

e0
“Maize tassels detection is essential for future agronomic management in maize planting and breeding”
✓ located verbatim in the source · abstract, characters 0–99

e1
“the mean average precision (mAP@0.5) was 95.0%”
✓ located verbatim in the source · abstract, characters 1148–1194

Title

Detection of maize tassels for UAV remote sensing image with an improved YOLOX Model

Chao-yu SONG; Fan ZHANG; Jian-sheng LI; et al. Journal of Integrative Agriculture 2022. doi:10.1016/j.jia.2022.09.021

Other claims from this paper (4)

Method / resource The improved model increased mAP@0.5 by 1.5 over the original model for maize tassel detection.
Method / resource The improved model increased mAP@0.5-0.95 by 1.8 over the original model for maize tassel detection.
Method / resource The improved model increased mAP@0.5-0.95 for small tassels by 5.3 over the original model.
Method / resource The improved model increased mAP@0.5-0.95 for medium tassels by 1.7 over the original model.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The soybean organ-trait models achieved a mean average precision (mAP@0.5) exceeding 94%.

Taxon: soybeanGlycine maxe4
Outcome: mean average precision (mAP@0.5)e1
Value: exceeding 94%e1

Verbatim

e1
“All models achieved a mean average precision (mAP@0.5) exceeding 94%”
✓ located verbatim in the source · abstract, characters 1058–1126

e4
“we have constructed multiple high-definition datasets for soybean phenotypic traits”
✓ located verbatim in the source · abstract, characters 798–881

Title

Crop-IRM: An Intelligent Recognition and Management System for Organ Characteristics of Crop Germplasm Resources

Jie Zhang; Chenyao Yang; Hailin Peng; et al. Agriculture 2026. doi:10.3390/agriculture16090996

Other claims from this paper (4)

Method / resource The soybean organ-trait models achieved a top1_accuracy of 0.999.
Method / resource The models took between 0.71 and 3.03 s to make predictions for 100 images.
Method / resource In practical evaluations the models achieved an accuracy rate of over 98%.
Method / resource The system substantially enhances the efficiency and objectivity of soybean germplasm data collection and analysis compared with traditional methods.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

DCP-YOLOv7x reached a mean average precision (mAP@0.5) of 95.4% under low-light conditions.

Taxon: cottonGossypium hirsutume0
Outcome: Mean Average Precision (mAP@0.5)e2
Value: 95.4%e2
Qualifier: low-light environmentse2

Verbatim

e0
“Pests are important factors affecting the growth of cotton”
✓ located verbatim in the source · abstract, characters 14–72

e2
“the Mean Average Precision (mAP@0.5) is 95.4% under a low-light environments”
✓ located verbatim in the source · abstract, characters 1731–1807

Title

DCP-YOLOv7x: improved pest detection method for low-quality cotton image

Yukun Ma; Yajun Wei; Minsheng Ma; et al. Frontiers in Plant Science 2024. doi:10.3389/fpls.2024.1501043

Other claims from this paper (4)

Method / resource DCP-YOLOv7x detected cotton pests in low-light images with a Precision (P) of 95.9%.
Method / resource DCP-YOLOv7x improved detection precision by 14.4% compared with YOLOv7x in low-light environments.
Method / resource DCP-YOLOv7x improved mean average precision (mAP@0.5) by 15.6% compared with YOLOv7x in low-light environments.
Method / resource DCP-YOLOv7x achieved better detection results on the Exdark datasets.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

YOLOv5 achieved 13.3% mean average precision at mAP 0.5:0.95.

Outcome: mAP 0.5:0.95e0
Value: 13.3%e0

Verbatim

e0
“13.3% (mAP 0.5:0.95)”
✓ located verbatim in the source · abstract, characters 727–747

Title

Design and Development of a Leaf Blight Detection System Using Image Processing with Intrusion Notification via SMS

Gina L. Hanopol; Jennifer C. Dela Cruz 2023 IEEE 14th Control and System Graduate Research Colloquium (ICSGRC) 2023. doi:10.1109/icsgrc57744.2023.10215447

Other claims from this paper (3)

Method / resource YOLOv5 detected leaf blight with mean average precision of 38.80% at mAP 0.5.
Method / resource YOLOv5 precision was 56%.
Method / resource YOLOv5 recall was 37.90%.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLOv11s-pose achieved 98.5% mean average precision at 0.5.

Taxon: CabbageBrassica oleracea var. capitatat0
Outcome: mean average precision (mAP)e0
Value: 98.5%e0
Comparator: YOLOv11s-posee0

Verbatim

e0
“mean average precision (mAP) 98.5% at 0.5, YOLOv11s-pose emerged as the best-performing model”
✓ located verbatim in the source · abstract, characters 683–776

t0
“Cabbage”
✓ located verbatim in the source · title, characters 80–87

Title

Integrating UAV-Derived Diameter Estimations and Machine Learning for Precision Cabbage Yield Mapping

Sara Tokhi Arab; Akane Takezaki; Masayuki Kogoshi; et al. Sensors 2025. doi:10.3390/s25185652

Other claims from this paper (6)

Method / resource YOLOv11s-pose estimated cabbage head diameter with 4.6% mean relative error.
Method / resource YOLOv11s-pose was the best performing pose model.
Method / resource CatBoost had the lowest fresh-weight prediction MSE of 0.025 kg/cabbage.
Method / resource CatBoost had the highest R2 of 0.89.
Method / resource CatBoost outperformed other models in the Diebold–Mariano test.
Method / resource The authors conclude the integrated AI framework improves noninvasive cabbage yield estimation.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Mean average precision at 0.5 reached 97.87%.

Taxon: red cloverTrifolium pratenset0
Outcome: mean average precision@0.5 (mAP@0.5)e4
Value: 97.87%e4

Verbatim

t0
“coated red clover seeds”
✓ located verbatim in the source · title, characters 22–45

e4
“a mean average precision@0.5 (mAP@0.5) of 97.87%”
✓ located verbatim in the source · abstract, characters 1447–1495

Title

Recognition model for coated red clover seeds using YOLOv5s optimized with an attention module

Xiwen Zhang; Chuanzhong Xuan; Zhanfeng Hou International journal of agricultural and biological engineering 2023. doi:10.25165/j.ijabe.20231606.7773

Other claims from this paper (8)

Method / resource YOLO-CSR achieved the best recognition performance on the self-built coated seed image dataset compared with other classical recognition models.
Method / resource Average precision for one of the three coated seed types reached 98.43%.
Method / resource Average precision for one of the three coated seed types reached 97.91%.
Method / resource Average precision for one of the three coated seed types reached 97.26%.
Method / resource YOLO-CSR improved mAP@0.5 by 1.18% compared with YOLOv5.
Method / resource YOLO-CSR has a model size of 14.9 MB.
Method / resource YOLO-CSR has an average recognition time of 10.1 ms.
Method / resource YOLO-CSR runs at a frame per second of 99.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The proposed tea disease detector reached 96.34% mean average precision at 0.5.

Taxon: Teat0
Outcome: mAP@0.5e0
Value: 96.34%e0

Verbatim

e0
“the proposed method achieved an mAP@0.5 (where mAP is mean average precision) of 96.34%.”
✓ located verbatim in the source · abstract, characters 1177–1265

t0
“Tea”
✓ located after whitespace/quote normalisation · title, characters 36–39

Title

A YOLOv8‐based method for detecting tea disease in natural environments

Xiutong Li; Taiheng Zhang; Mei Yu; et al. Agronomy Journal 2025. doi:10.1002/agj2.70043

Other claims from this paper (5)

Method / resource The tea disease detector used 8.81 M parameters.
Method / resource The tea disease detector required 20.3 G floating point operations.
Method / resource Mean average precision at 0.5 rose 5.08% versus original YOLOv8s.
Method / resource Parameter count fell 26.14% versus original YOLOv8s.
Method / resource Detection was fastest at 153.3 frames per second.

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“mean average precision (mAP)@0.5”

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