Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images
This study explored the use of TensorFlow image recognition model to identify herbaceous mimosa (Mimosa strigillosa) from digital images. There is a demand for such technology toward digital mapping of the spatial distribution of these important perennial legumes in the context of pasture management...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-03-01
|
| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524000054 |
| _version_ | 1797246814260297728 |
|---|---|
| author | T. Setiyono T. Gentimis F. Rontani D. Duron G. Bortolon R. Adhikari B. Acharya K.J. Han W.D. Pitman |
| author_facet | T. Setiyono T. Gentimis F. Rontani D. Duron G. Bortolon R. Adhikari B. Acharya K.J. Han W.D. Pitman |
| author_sort | T. Setiyono |
| collection | DOAJ |
| description | This study explored the use of TensorFlow image recognition model to identify herbaceous mimosa (Mimosa strigillosa) from digital images. There is a demand for such technology toward digital mapping of the spatial distribution of these important perennial legumes in the context of pasture management and as well as management of reclamation ground cover landscapes. This study provided evidence of successful application of TensorFlow model for identification of herbaceous mimosa from digital images with final accuracy of 95 % or more. The complexity of ground images of multiple objects in this study is suspected to induce fluctuations in validation accuracy. Such fluctuation of the validation accuracy, however, was shown to decline over time as the accuracy increased with more processing epochs involved. Despite the downside of intensive data preparation and heavy computing resources, the approach tested in this study is promising toward the next step of the technology application for identification of herbaceous mimosa patches from images acquired using Unmanned Aerial Vehicle (UAV). |
| first_indexed | 2024-03-08T14:20:28Z |
| format | Article |
| id | doaj.art-b970686debac4a2cb8adbda3b5bce30e |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-04-24T19:48:46Z |
| publishDate | 2024-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-b970686debac4a2cb8adbda3b5bce30e2024-03-25T04:18:14ZengElsevierSmart Agricultural Technology2772-37552024-03-017100400Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital imagesT. Setiyono0T. Gentimis1F. Rontani2D. Duron3G. Bortolon4R. Adhikari5B. Acharya6K.J. Han7W.D. Pitman8School of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, LA, USA; Corresponding author.Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA, USASchool of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, LA, USASchool of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, LA, USASchool of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, LA, USASchool of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, LA, USASchool of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, LA, USASchool of Plant, Environmental, and Soil Sciences, Louisiana State University, Baton Rouge, LA, USAHill Farm Research Station, Louisiana State University Agricultural Center, Homer, LA, USAThis study explored the use of TensorFlow image recognition model to identify herbaceous mimosa (Mimosa strigillosa) from digital images. There is a demand for such technology toward digital mapping of the spatial distribution of these important perennial legumes in the context of pasture management and as well as management of reclamation ground cover landscapes. This study provided evidence of successful application of TensorFlow model for identification of herbaceous mimosa from digital images with final accuracy of 95 % or more. The complexity of ground images of multiple objects in this study is suspected to induce fluctuations in validation accuracy. Such fluctuation of the validation accuracy, however, was shown to decline over time as the accuracy increased with more processing epochs involved. Despite the downside of intensive data preparation and heavy computing resources, the approach tested in this study is promising toward the next step of the technology application for identification of herbaceous mimosa patches from images acquired using Unmanned Aerial Vehicle (UAV).http://www.sciencedirect.com/science/article/pii/S2772375524000054Herbaceous mimosaSunshine mimosaPowderpuffTensorFlowMachine learningDeep learning |
| spellingShingle | T. Setiyono T. Gentimis F. Rontani D. Duron G. Bortolon R. Adhikari B. Acharya K.J. Han W.D. Pitman Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images Smart Agricultural Technology Herbaceous mimosa Sunshine mimosa Powderpuff TensorFlow Machine learning Deep learning |
| title | Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images |
| title_full | Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images |
| title_fullStr | Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images |
| title_full_unstemmed | Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images |
| title_short | Application of TensorFlow model for identification of herbaceous mimosa (Mimosa strigillosa) from digital images |
| title_sort | application of tensorflow model for identification of herbaceous mimosa mimosa strigillosa from digital images |
| topic | Herbaceous mimosa Sunshine mimosa Powderpuff TensorFlow Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524000054 |
| work_keys_str_mv | AT tsetiyono applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT tgentimis applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT frontani applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT dduron applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT gbortolon applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT radhikari applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT bacharya applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT kjhan applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages AT wdpitman applicationoftensorflowmodelforidentificationofherbaceousmimosamimosastrigillosafromdigitalimages |