A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Ismail Ismail Tijjani, Sunusi Muhammad Ibrahim, Amina Ibrahim Khaleel, Lanre Olusegun Akinola, Fatima Isa Jibrin, Muhammad Bashir Aliyu, Abdullahi Abdussalam Dalhat, Abdullahi Suiudeen
• Venue: arXiv: cs.CV
• Published: 2026-08-11
• Collected: 2026-08-12
• URL: http://arxiv.org/abs/2608.11053v1
• PDF: https://arxiv.org/pdf/2608.11053v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

A study compares six object detection models on a dataset collected from Nigerian farms, called AgriAISeg, which includes sesame, cabbage, and tomato crops under varying environmental conditions. The models tested are YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR. RT-DETR performed the best overall with a precision of 0.768 and mAP@0.5:0.95 of 0.624. YOLOv8 and YOLO11 also showed strong performance. Faster R-CNN had lower accuracy, particularly under complex field conditions. YOLO-based models were more efficient during training.

What the Document Says

• The study evaluates six object detection models on a real-world agricultural dataset named AgriAISeg.
• AgriAISeg contains images of sesame, cabbage, and tomato crops from Nigerian farms under varying environmental conditions.
• The models tested are YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR.
• The performance of the models was assessed using precision, recall, mAP@0.5, and mAP@0.5:0.95.
• RT-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 of 0.624.
• YOLOv8 and YOLO11 also demonstrated strong and consistent performance.
• Faster R-CNN recorded significantly lower accuracy, with an overall mAP@0.5 of 0.466.
• YOLO-based models exhibited superior training efficiency compared to Faster R-CNN.

Key Concepts Mentioned

• computer vision
• agriculture
• object detection models
• AgriAISeg dataset
• Nigerian farms
• sesame
• cabbage
• tomato crops
• environmental conditions
• performance assessment
• precision
• recall
• mAP@0.5
• mAP@0.5:0.95

Claims Made by the Document

• RT-DETR achieved the highest overall performance
• YOLOv8 and YOLO11 demonstrated strong and consistent performance
• Faster R-CNN had lower accuracy under complex field conditions
• YOLO-based models were more efficient during training

Evidence Used

• AgriAISeg comprises 3,382 images of sesame, cabbage, and tomato crops... Models were trained, and performance was assessed using precision, recall, mAP@0.5, and mAP@0.5:0.95... The results show that RT-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 of 0.624, while YOLOv8 and YOLO11 also demonstrated strong and consistent performance... In contrast, Faster R-CNN recorded significantly lower accuracy, with an overall mAP@0.5 of 0.466, indicating reduced effectiveness under complex field conditions... These findings demonstrate that modern one-stage and transformer-based detectors provide more reliable and efficient solutions for plant detection in realworld agricultural environments.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

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Open Questions

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Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

The application of computer vision in agriculture has shown significant potential for improving crop monitoring and precision farming. However, many existing approaches rely on controlled datasets that do not adequately represent realworld farming conditions, particularly in underrepresented regions such as Africa. This study presents a comparative evaluation of six object detection models YOLOv5, YOLOv8, YOLO11, YOLO26, Faster R-CNN, and RT-DETR using a real-world dataset, AgriAISeg 1 , collected manually from Nigerian farms. AgriAISeg comprises 3,382 images of sesame, cabbage, and tomato crops captured under varying environmental conditions, including changes in illumination, occlusion, and viewing perspectives. Models were trained, and performance was assessed using precision, recall, mAP@0.5, and mAP@0.5:0.95. The results show that RT-DETR achieved the highest overall performance with a precision of 0.768 and mAP@0.5:0.95 of 0.624, while YOLOv8 and YOLO11 also demonstrated strong and consistent performance. In contrast, Faster R-CNN recorded significantly lower accuracy, with an overall mAP@0.5 of 0.466, indicating reduced effectiveness under complex field conditions. In addition, YOLO-based models exhibited superior training efficiency compared to Faster R-CNN.These findings demonstrate that modern one-stage and transformer-based detectors provide more reliable and efficient solutions for plant detection in realworld agricultural environments.