Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images
For sustainability and efficiency in maintaining high crop yield and less chemically polluted agricultural lands, precise weed mapping is essential for the total implementation of site-specific weed management which currently stands as a major challenge in present day agriculture. In this research,...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375523000618 |
| _version_ | 1827962735996960768 |
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| author | Oluibukun Gbenga Ajayi John Ashi Blessed Guda |
| author_facet | Oluibukun Gbenga Ajayi John Ashi Blessed Guda |
| author_sort | Oluibukun Gbenga Ajayi |
| collection | DOAJ |
| description | For sustainability and efficiency in maintaining high crop yield and less chemically polluted agricultural lands, precise weed mapping is essential for the total implementation of site-specific weed management which currently stands as a major challenge in present day agriculture. In this research, the robustness of the training epochs of You Only Look Once (YOLO) v5s, a Convolutional Neural Network (CNN) model was evaluated for the development of an automatic crop and weeds classification using UAV images. The images were annotated using a bounding box and they were trained on google colaboratory over 100, 300, 500, 600, 700 and 1000 epochs. The model detected and categorized five different classes which are sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. To find the optimal performance on the test set, the model was trained across several epochs, and training was stopped when the test performance (classification accuracy, precision, and recall) began to drop. The obtained result shows that the performance of the classifier improved significantly as the range of training epochs tends to rise from 100 through to 600 epochs. Meanwhile, a slight decline was observed as the number of epoch was increased to 700 when the classification accuracy, the precision of weed and recall of 65, 43 and 43%, respectively, was recorded as against 67, 78 and 34% that was obtained as the classification accuracy, weed precision and recall, respectively, at 600 epochs. This decline continued even when the epoch was increased to 1000 where classification accuracy, weed precision and recall of 65%, 45% and 40%, respectively was obtained. The results showed that the training epoch of YOLOv5s significantly affects the model's robustness in automatic crop and weep classification and identified 600 as the epoch for optimal performance. |
| first_indexed | 2024-04-09T16:47:39Z |
| format | Article |
| id | doaj.art-2617ea9f5e914fbc85b8cc8a4e08fc5d |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-09T16:47:39Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-2617ea9f5e914fbc85b8cc8a4e08fc5d2023-04-22T06:23:54ZengElsevierSmart Agricultural Technology2772-37552023-10-015100231Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV imagesOluibukun Gbenga Ajayi0John Ashi1Blessed Guda2Department of Land and Spatial Sciences, Namibia University of Science and Technology, Windhoek, Namibia; Department of Surveying and Geoinformatics, Federal University of Technology, Minna, Nigeria; Corresponding author at: Department of Land and Spatial Sciences, Namibia University of Science and Technology, Windhoek, Namibia.Department of Surveying and Geoinformatics, Federal University of Technology, Minna, NigeriaEngineering Artificial Intelligence, Carnegie Mellon University-Africa, Kigali, RwandaFor sustainability and efficiency in maintaining high crop yield and less chemically polluted agricultural lands, precise weed mapping is essential for the total implementation of site-specific weed management which currently stands as a major challenge in present day agriculture. In this research, the robustness of the training epochs of You Only Look Once (YOLO) v5s, a Convolutional Neural Network (CNN) model was evaluated for the development of an automatic crop and weeds classification using UAV images. The images were annotated using a bounding box and they were trained on google colaboratory over 100, 300, 500, 600, 700 and 1000 epochs. The model detected and categorized five different classes which are sugarcane (Saccharum officinarum), banana trees (Musa), spinach (Spinacia oleracea), pepper (Capsicum), and weeds. To find the optimal performance on the test set, the model was trained across several epochs, and training was stopped when the test performance (classification accuracy, precision, and recall) began to drop. The obtained result shows that the performance of the classifier improved significantly as the range of training epochs tends to rise from 100 through to 600 epochs. Meanwhile, a slight decline was observed as the number of epoch was increased to 700 when the classification accuracy, the precision of weed and recall of 65, 43 and 43%, respectively, was recorded as against 67, 78 and 34% that was obtained as the classification accuracy, weed precision and recall, respectively, at 600 epochs. This decline continued even when the epoch was increased to 1000 where classification accuracy, weed precision and recall of 65%, 45% and 40%, respectively was obtained. The results showed that the training epoch of YOLOv5s significantly affects the model's robustness in automatic crop and weep classification and identified 600 as the epoch for optimal performance.http://www.sciencedirect.com/science/article/pii/S2772375523000618WeedsConvolutional neural networksDeep learningPrecision agricultureObject detection |
| spellingShingle | Oluibukun Gbenga Ajayi John Ashi Blessed Guda Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images Smart Agricultural Technology Weeds Convolutional neural networks Deep learning Precision agriculture Object detection |
| title | Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images |
| title_full | Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images |
| title_fullStr | Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images |
| title_full_unstemmed | Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images |
| title_short | Performance evaluation of YOLO v5 model for automatic crop and weed classification on UAV images |
| title_sort | performance evaluation of yolo v5 model for automatic crop and weed classification on uav images |
| topic | Weeds Convolutional neural networks Deep learning Precision agriculture Object detection |
| url | http://www.sciencedirect.com/science/article/pii/S2772375523000618 |
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