Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring
Taro blight is a major disease affecting taro cultivation. Traditional methods for disease prevention rely on manual identification, which is limited by subjectivity and scope. An unmanned aerial vehicle (UAV) was utilized to capture spectral images of natural taro fields, facilitating the efficient...
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| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/5/1189 |
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| author | Yushuai Wang Yuxin Chen Zhou Shu Shaolong Zhu Weijun Zhang Tao Liu Chengming Sun |
| author_facet | Yushuai Wang Yuxin Chen Zhou Shu Shaolong Zhu Weijun Zhang Tao Liu Chengming Sun |
| author_sort | Yushuai Wang |
| collection | DOAJ |
| description | Taro blight is a major disease affecting taro cultivation. Traditional methods for disease prevention rely on manual identification, which is limited by subjectivity and scope. An unmanned aerial vehicle (UAV) was utilized to capture spectral images of natural taro fields, facilitating the efficient monitoring of taro blight. Field survey data were integrated with these images to develop a model for monitoring taro blight severity. The back propagation neural network (BPNN) model showed optimal performance during the early and middle stages of taro formation when hyperspectral parameters were used as input variables. In the early stage, the BPNN model achieved a coefficient of determination (R<sup>2</sup>) of 0.92 and an RMSE of 0.054 on the training set, and it obtained an R<sup>2</sup> of 0.89 with a root mean square error (RMSE) of 0.074 on the validation set. The random forest regression (RFR) model performed best during the early stage of taro formation with multispectral vegetation indices as input variables. The models exhibited robust predictive capabilities across various stages, especially during the early stage of taro formation. The results demonstrate that UAV remote sensing, combined with characteristic parameters and disease indices, presents a precise taro blight monitoring method that can substantially improve disease management in taro cultivation. |
| format | Article |
| id | doaj-art-6a38834d096d41fea8a54792f4a68c18 |
| institution | DOAJ |
| issn | 2073-4395 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-6a38834d096d41fea8a54792f4a68c182025-08-20T03:14:39ZengMDPI AGAgronomy2073-43952025-05-01155118910.3390/agronomy15051189Integration of UAV Remote Sensing and Machine Learning for Taro Blight MonitoringYushuai Wang0Yuxin Chen1Zhou Shu2Shaolong Zhu3Weijun Zhang4Tao Liu5Chengming Sun6Cultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaCultivation and Construction Site of National Key Laboratory for Crop Genetics and Physiology in Jiangsu Province, Yangzhou University, Yangzhou 225009, ChinaTaro blight is a major disease affecting taro cultivation. Traditional methods for disease prevention rely on manual identification, which is limited by subjectivity and scope. An unmanned aerial vehicle (UAV) was utilized to capture spectral images of natural taro fields, facilitating the efficient monitoring of taro blight. Field survey data were integrated with these images to develop a model for monitoring taro blight severity. The back propagation neural network (BPNN) model showed optimal performance during the early and middle stages of taro formation when hyperspectral parameters were used as input variables. In the early stage, the BPNN model achieved a coefficient of determination (R<sup>2</sup>) of 0.92 and an RMSE of 0.054 on the training set, and it obtained an R<sup>2</sup> of 0.89 with a root mean square error (RMSE) of 0.074 on the validation set. The random forest regression (RFR) model performed best during the early stage of taro formation with multispectral vegetation indices as input variables. The models exhibited robust predictive capabilities across various stages, especially during the early stage of taro formation. The results demonstrate that UAV remote sensing, combined with characteristic parameters and disease indices, presents a precise taro blight monitoring method that can substantially improve disease management in taro cultivation.https://www.mdpi.com/2073-4395/15/5/1189taro blightUAV remote sensingcharacteristic parametersvegetation indexdisease index |
| spellingShingle | Yushuai Wang Yuxin Chen Zhou Shu Shaolong Zhu Weijun Zhang Tao Liu Chengming Sun Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring Agronomy taro blight UAV remote sensing characteristic parameters vegetation index disease index |
| title | Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring |
| title_full | Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring |
| title_fullStr | Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring |
| title_full_unstemmed | Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring |
| title_short | Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring |
| title_sort | integration of uav remote sensing and machine learning for taro blight monitoring |
| topic | taro blight UAV remote sensing characteristic parameters vegetation index disease index |
| url | https://www.mdpi.com/2073-4395/15/5/1189 |
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