Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models
Presented study evaluates and compares two deep learning models, i.e., YOLOv8n and Faster R-CNN, for automated detection of date fruits in natural orchard environments. Both models were trained and tested using a publicly available annotated dataset. YOLO, a single-stage detector, achieved a mAP@0.5...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/6/149 |
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| author | Seweryn Lipiński Szymon Sadkowski Paweł Chwietczuk |
| author_facet | Seweryn Lipiński Szymon Sadkowski Paweł Chwietczuk |
| author_sort | Seweryn Lipiński |
| collection | DOAJ |
| description | Presented study evaluates and compares two deep learning models, i.e., YOLOv8n and Faster R-CNN, for automated detection of date fruits in natural orchard environments. Both models were trained and tested using a publicly available annotated dataset. YOLO, a single-stage detector, achieved a mAP@0.5 of 0.942 with a training time of approximately 2 h. It demonstrated strong generalization, especially in simpler conditions, and is well-suited for real-time applications due to its speed and lower computational requirements. Faster R-CNN, a two-stage detector using a ResNet-50 backbone, reached comparable accuracy (mAP@0.5 = 0.94) with slightly higher precision and recall. However, its training required significantly more time (approximately 19 h) and resources. Deep learning metrics analysis confirmed both models performed reliably, with YOLO favoring inference speed and Faster R-CNN offering improved robustness under occlusion and variable lighting. Practical recommendations are provided for model selection based on application needs—YOLO for mobile or field robotics and Faster R-CNN for high-accuracy offline tasks. Additional conclusions highlight the benefits of GPU acceleration and high-resolution inputs. The study contributes to the growing body of research on AI deployment in precision agriculture and provides insights into the development of intelligent harvesting and crop monitoring systems. |
| format | Article |
| id | doaj-art-921fa8aa259644c390a9db368484a310 |
| institution | Kabale University |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-921fa8aa259644c390a9db368484a3102025-08-20T03:26:11ZengMDPI AGComputation2079-31972025-06-0113614910.3390/computation13060149Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN ModelsSeweryn Lipiński0Szymon Sadkowski1Paweł Chwietczuk2Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, 10-036 Olsztyn, PolandFaculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, 10-036 Olsztyn, PolandFaculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, 10-036 Olsztyn, PolandPresented study evaluates and compares two deep learning models, i.e., YOLOv8n and Faster R-CNN, for automated detection of date fruits in natural orchard environments. Both models were trained and tested using a publicly available annotated dataset. YOLO, a single-stage detector, achieved a mAP@0.5 of 0.942 with a training time of approximately 2 h. It demonstrated strong generalization, especially in simpler conditions, and is well-suited for real-time applications due to its speed and lower computational requirements. Faster R-CNN, a two-stage detector using a ResNet-50 backbone, reached comparable accuracy (mAP@0.5 = 0.94) with slightly higher precision and recall. However, its training required significantly more time (approximately 19 h) and resources. Deep learning metrics analysis confirmed both models performed reliably, with YOLO favoring inference speed and Faster R-CNN offering improved robustness under occlusion and variable lighting. Practical recommendations are provided for model selection based on application needs—YOLO for mobile or field robotics and Faster R-CNN for high-accuracy offline tasks. Additional conclusions highlight the benefits of GPU acceleration and high-resolution inputs. The study contributes to the growing body of research on AI deployment in precision agriculture and provides insights into the development of intelligent harvesting and crop monitoring systems.https://www.mdpi.com/2079-3197/13/6/149precision agriculturesmart farmingdate fruit detectioncomputer visionimage recognitiondeep learning |
| spellingShingle | Seweryn Lipiński Szymon Sadkowski Paweł Chwietczuk Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models Computation precision agriculture smart farming date fruit detection computer vision image recognition deep learning |
| title | Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models |
| title_full | Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models |
| title_fullStr | Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models |
| title_full_unstemmed | Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models |
| title_short | Application of AI in Date Fruit Detection—Performance Analysis of YOLO and Faster R-CNN Models |
| title_sort | application of ai in date fruit detection performance analysis of yolo and faster r cnn models |
| topic | precision agriculture smart farming date fruit detection computer vision image recognition deep learning |
| url | https://www.mdpi.com/2079-3197/13/6/149 |
| work_keys_str_mv | AT sewerynlipinski applicationofaiindatefruitdetectionperformanceanalysisofyoloandfasterrcnnmodels AT szymonsadkowski applicationofaiindatefruitdetectionperformanceanalysisofyoloandfasterrcnnmodels AT pawełchwietczuk applicationofaiindatefruitdetectionperformanceanalysisofyoloandfasterrcnnmodels |