Claim · model-written summary
A tiered system architecture framework for IoT-based precision irrigation and crop monitoring has been developed.
Not reportedComparator
Verbatim
e0“A tiered system architecture framework has been developed”
Title
Mohamed Elhoseny; Khadija Jahid; Rachid Latif; et al. Scientific African 2026. doi:10.1016/j.sciaf.2026.e03222
Claim · model-written summary
Case studies illustrate that IoT technologies can address resource usage and improve yield outcomes.
Not reportedComparator
Verbatim
e0“case studies illustrate how these technologies can address resource usage and improve yield outcomes”
Title
Mohamed Elhoseny; Khadija Jahid; Rachid Latif; et al. Scientific African 2026. doi:10.1016/j.sciaf.2026.e03222
Claim · model-written summary
The automated fluorescence microscopy method reliably separated monocot and dicot plants by genome size, with accuracy close to flow cytofluorometry for dicots and comparable for monocots.
Verbatim
e0“enables reliable separation of both monocotyledonous and dicotyledonous plants by genome size with an accuracy close (for dicots) or comparable (for monocots) to flow cytofluorometry”
Title
Dmitriy A. Serov; Dmitry A. Zakharov; Natalia A. Semenova; et al. Inventions 2026. doi:10.3390/inventions11030056
Claim · model-written summary
The SeedlingNet deep learning model classified wheat seedling varieties with 99.26% accuracy, outperforming traditional machine learning methods and mainstream deep learning models.
Verbatim
t0“wheat seedling variety recognition”
e0“SeedlingNet achieves a remarkable classification accuracy of 99.26%, outperforming traditional machine learning methods and mainstream deep learning models”
Title
Multi branch model based on cross scale feature fusion for wheat seedling variety recognition
Zhang Wenbo; Zhang Ziyang; Xi Chengyu; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.101785
Claim · model-written summary
The authors established an image dataset of 13,600 images representing 17 wheat varieties at the early growth stage.
Triticum aestivume0Verbatim
e0“We established a comprehensive image dataset of 13,600 images representing 17 wheat varieties at the early growth stage”
Title
Multi branch model based on cross scale feature fusion for wheat seedling variety recognition
Zhang Wenbo; Zhang Ziyang; Xi Chengyu; et al. Smart Agricultural Technology 2026. doi:10.1016/j.atech.2026.101785
Claim · model-written summary
The improved MSW-YOLOv8n-Seg model reached bounding box precision of 90.8% on the test sets for cherry tomato maturity segmentation.
Solanum lycopersicum var. cerasiformet0Verbatim
t0“cherry tomato”
e0“the bounding box precision, recall and mean average precision (mAP)@0.5 of the improved model on the test sets were 90.8%, 86.3% and 83.9% respectively”
Title
Ronghui Miao; Zhiwei Li Frontiers in Plant Science 2026. doi:10.3389/fpls.2025.1731580
Claim · model-written summary
The improved MSW-YOLOv8n-Seg model reached recall of 86.3% on the test sets for cherry tomato maturity segmentation.
Solanum lycopersicum var. cerasiformet0Verbatim
t0“cherry tomato”
e0“the bounding box precision, recall and mean average precision (mAP)@0.5 of the improved model on the test sets were 90.8%, 86.3% and 83.9% respectively”
Title
Ronghui Miao; Zhiwei Li Frontiers in Plant Science 2026. doi:10.3389/fpls.2025.1731580
Claim · model-written summary
The improved MSW-YOLOv8n-Seg model reached mean average precision (mAP)@0.5 of 83.9% on the test sets for cherry tomato maturity segmentation.
Solanum lycopersicum var. cerasiformet0Verbatim
t0“cherry tomato”
e0“the bounding box precision, recall and mean average precision (mAP)@0.5 of the improved model on the test sets were 90.8%, 86.3% and 83.9% respectively”
Title
Ronghui Miao; Zhiwei Li Frontiers in Plant Science 2026. doi:10.3389/fpls.2025.1731580
Claim · model-written summary
The model size of MSW-YOLOv8n-Seg was 6.0 MB.
Solanum lycopersicum var. cerasiformet0Verbatim
t0“cherry tomato”
e0“the model size was 6.0 MB”
Title
Ronghui Miao; Zhiwei Li Frontiers in Plant Science 2026. doi:10.3389/fpls.2025.1731580
Claim · model-written summary
The improved model increased bounding box precision over YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN and Mask2Former by 9.6%, 5.2%, 5.7%, 12.3%, 13.3% and 5.0% respectively.
Verbatim
t0“cherry tomato”
e0“the bounding box precision increased by 9.6%, 5.2%, 5.7%, 12.3%, 13.3% and 5.0%”
e1“YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN”
Title
Ronghui Miao; Zhiwei Li Frontiers in Plant Science 2026. doi:10.3389/fpls.2025.1731580