NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning
The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex t...
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| Format: | Article |
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/15/1/63 |
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| author | Jianliang Wang Chen Chen Jiacheng Wang Zhaosheng Yao Ying Wang Yuanyuan Zhao Yi Sun Fei Wu Dongwei Han Guanshuo Yang Xinyu Liu Chengming Sun Tao Liu |
| author_facet | Jianliang Wang Chen Chen Jiacheng Wang Zhaosheng Yao Ying Wang Yuanyuan Zhao Yi Sun Fei Wu Dongwei Han Guanshuo Yang Xinyu Liu Chengming Sun Tao Liu |
| author_sort | Jianliang Wang |
| collection | DOAJ |
| description | The Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex to operate, thus limiting their applicability in small-scale farms and developing countries. To address these limitations, this study proposes an NDVI estimation method based on low-cost RGB (red, green, and blue) UAV (unmanned aerial vehicle) imagery combined with deep learning techniques. This study utilizes field data from five major crops (cotton, rice, maize, rape, and wheat) throughout their whole growth periods. RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. The results indicate that the model, optimized with CFs, significantly enhanced NDVI estimation accuracy. Specifically, the R<sup>2</sup> values for maize, rape, and wheat during their whole growth periods reached 0.99, while those for rice and cotton were 0.96 and 0.93, respectively. Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R<sup>2</sup> increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. This method leverages deep learning to capture structural changes in crop populations, optimizing conventional image features and improving NDVI estimation accuracy. This study presents an NDVI estimation approach applicable to the whole growth period of common crops, particularly those with significant population variations, and provides a valuable reference for estimating other vegetation indices using low-cost UAV-acquired RGB images. |
| format | Article |
| id | doaj-art-27108922ffe64e45a702ffd01d14c855 |
| institution | Kabale University |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj-art-27108922ffe64e45a702ffd01d14c8552025-01-24T13:16:33ZengMDPI AGAgronomy2073-43952024-12-011516310.3390/agronomy15010063NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep LearningJianliang Wang0Chen Chen1Jiacheng Wang2Zhaosheng Yao3Ying Wang4Yuanyuan Zhao5Yi Sun6Fei Wu7Dongwei Han8Guanshuo Yang9Xinyu Liu10Chengming Sun11Tao Liu12Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaZhenjiang Agricultural Science Research Institute of Jiangsu Hilly Area, Jurong 212005, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaPrecision Agriculture Lab, School of Life Sciences, Technical University of Munich, 85354 Freising, GermanyJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou 225009, ChinaThe Normalized Difference Vegetation Index (NDVI) is an important remote sensing index that is widely used to assess vegetation coverage, monitor crop growth, and predict yields. Traditional NDVI calculation methods often rely on multispectral or hyperspectral imagery, which are costly and complex to operate, thus limiting their applicability in small-scale farms and developing countries. To address these limitations, this study proposes an NDVI estimation method based on low-cost RGB (red, green, and blue) UAV (unmanned aerial vehicle) imagery combined with deep learning techniques. This study utilizes field data from five major crops (cotton, rice, maize, rape, and wheat) throughout their whole growth periods. RGB images were used to extract conventional features, including color indices (CIs), texture features (TFs), and vegetation coverage, while convolutional features (CFs) were extracted using the deep learning network ResNet50 to optimize the model. The results indicate that the model, optimized with CFs, significantly enhanced NDVI estimation accuracy. Specifically, the R<sup>2</sup> values for maize, rape, and wheat during their whole growth periods reached 0.99, while those for rice and cotton were 0.96 and 0.93, respectively. Notably, the accuracy improvement in later growth periods was most pronounced for cotton and maize, with average R<sup>2</sup> increases of 0.15 and 0.14, respectively, whereas wheat exhibited a more modest improvement of only 0.04. This method leverages deep learning to capture structural changes in crop populations, optimizing conventional image features and improving NDVI estimation accuracy. This study presents an NDVI estimation approach applicable to the whole growth period of common crops, particularly those with significant population variations, and provides a valuable reference for estimating other vegetation indices using low-cost UAV-acquired RGB images.https://www.mdpi.com/2073-4395/15/1/63UAVRGB imagedeep learningNDVIwhole growth period |
| spellingShingle | Jianliang Wang Chen Chen Jiacheng Wang Zhaosheng Yao Ying Wang Yuanyuan Zhao Yi Sun Fei Wu Dongwei Han Guanshuo Yang Xinyu Liu Chengming Sun Tao Liu NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning Agronomy UAV RGB image deep learning NDVI whole growth period |
| title | NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning |
| title_full | NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning |
| title_fullStr | NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning |
| title_full_unstemmed | NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning |
| title_short | NDVI Estimation Throughout the Whole Growth Period of Multi-Crops Using RGB Images and Deep Learning |
| title_sort | ndvi estimation throughout the whole growth period of multi crops using rgb images and deep learning |
| topic | UAV RGB image deep learning NDVI whole growth period |
| url | https://www.mdpi.com/2073-4395/15/1/63 |
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