Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images
Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1502863/full |
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| author | Jianliang Wang Jianliang Wang Chen Chen Senpeng Huang Senpeng Huang Hui Wang Yuanyuan Zhao Yuanyuan Zhao Jiacheng Wang Jiacheng Wang Zhaosheng Yao Zhaosheng Yao Chengming Sun Chengming Sun Tao Liu Tao Liu |
| author_facet | Jianliang Wang Jianliang Wang Chen Chen Senpeng Huang Senpeng Huang Hui Wang Yuanyuan Zhao Yuanyuan Zhao Jiacheng Wang Jiacheng Wang Zhaosheng Yao Zhaosheng Yao Chengming Sun Chengming Sun Tao Liu Tao Liu |
| author_sort | Jianliang Wang |
| collection | DOAJ |
| description | Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses the challenges of monitoring agricultural progress and the time-consuming and labor-intensive nature of the monitoring process. By integrating Unmanned aerial vehicle (UAV) image analysis technology and deep learning techniques, we proposed a method for precise monitoring of agricultural progress in rice-wheat rotation areas. The proposed method was initially used to extract color, texture, and convolutional features from RGB images for model construction. Then, redundant features were removed through feature correlation analysis. Additionally, activation layer features suitable for agricultural progress classification were proposed using the deep learning framework, enhancing classification accuracy. The results showed that the classification accuracies obtained by combining Color+Texture, Color+L08CON, Color+ResNet50, and Color+Texture+L08CON with the random forest model were 0.91, 0.99, 0.98, and 0.99, respectively. In contrast, the model using only color features had an accuracy of 85.3%, which is significantly lower than that of the multi-feature combination models. Color feature extraction took the shortest processing time (0.19 s) for a single image. The proposed Color+L08CON method achieved high accuracy with a processing time of 1.25 s, much faster than directly using deep learning models. This method effectively meets the need for real-time monitoring of agricultural progress. |
| format | Article |
| id | doaj-art-de87960363cf46f1b845b3dbbc5dbac9 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-de87960363cf46f1b845b3dbbc5dbac92025-01-09T06:11:08ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15028631502863Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB imagesJianliang Wang0Jianliang Wang1Chen Chen2Senpeng Huang3Senpeng Huang4Hui Wang5Yuanyuan Zhao6Yuanyuan Zhao7Jiacheng Wang8Jiacheng Wang9Zhaosheng Yao10Zhaosheng Yao11Chengming Sun12Chengming Sun13Tao Liu14Tao Liu15Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, ChinaJiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, ChinaZhenjiang Agricultural Science Research Institute of Jiangsu Hilly Area, Jurong, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, ChinaJiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, ChinaInstitute of Agricultural Sciences, Lixiahe Region in Jiangsu, Yangzhou, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, ChinaJiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, ChinaJiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, ChinaJiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, ChinaJiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, ChinaJiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College of Yangzhou University, Yangzhou, ChinaJiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, ChinaReal-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield of rice and wheat. However, the current monitoring methods mainly rely on manual recording and observation, leading to low monitoring efficiency. This study addresses the challenges of monitoring agricultural progress and the time-consuming and labor-intensive nature of the monitoring process. By integrating Unmanned aerial vehicle (UAV) image analysis technology and deep learning techniques, we proposed a method for precise monitoring of agricultural progress in rice-wheat rotation areas. The proposed method was initially used to extract color, texture, and convolutional features from RGB images for model construction. Then, redundant features were removed through feature correlation analysis. Additionally, activation layer features suitable for agricultural progress classification were proposed using the deep learning framework, enhancing classification accuracy. The results showed that the classification accuracies obtained by combining Color+Texture, Color+L08CON, Color+ResNet50, and Color+Texture+L08CON with the random forest model were 0.91, 0.99, 0.98, and 0.99, respectively. In contrast, the model using only color features had an accuracy of 85.3%, which is significantly lower than that of the multi-feature combination models. Color feature extraction took the shortest processing time (0.19 s) for a single image. The proposed Color+L08CON method achieved high accuracy with a processing time of 1.25 s, much faster than directly using deep learning models. This method effectively meets the need for real-time monitoring of agricultural progress.https://www.frontiersin.org/articles/10.3389/fpls.2024.1502863/fullUAV imageagricultural progressdeep learningrice-wheat rotationclassification |
| spellingShingle | Jianliang Wang Jianliang Wang Chen Chen Senpeng Huang Senpeng Huang Hui Wang Yuanyuan Zhao Yuanyuan Zhao Jiacheng Wang Jiacheng Wang Zhaosheng Yao Zhaosheng Yao Chengming Sun Chengming Sun Tao Liu Tao Liu Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images Frontiers in Plant Science UAV image agricultural progress deep learning rice-wheat rotation classification |
| title | Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images |
| title_full | Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images |
| title_fullStr | Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images |
| title_full_unstemmed | Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images |
| title_short | Monitoring of agricultural progress in rice-wheat rotation area based on UAV RGB images |
| title_sort | monitoring of agricultural progress in rice wheat rotation area based on uav rgb images |
| topic | UAV image agricultural progress deep learning rice-wheat rotation classification |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1502863/full |
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