Soybean leaf estimation based on RGB images and machine learning methods
Abstract Background RGB photographs are a powerful tool for dynamically estimating crop growth. Leaves are related to crop photosynthesis, transpiration, and nutrient uptake. Traditional blade parameter measurements were labor-intensive and time-consuming. Therefore, based on the phenotypic features...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-06-01
|
| Series: | Plant Methods |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13007-023-01023-z |
| _version_ | 1797801355099766784 |
|---|---|
| author | Xiuni Li Xiangyao Xu Shuai Xiang Menggen Chen Shuyuan He Wenyan Wang Mei Xu Chunyan Liu Liang Yu Weiguo Liu Wenyu Yang |
| author_facet | Xiuni Li Xiangyao Xu Shuai Xiang Menggen Chen Shuyuan He Wenyan Wang Mei Xu Chunyan Liu Liang Yu Weiguo Liu Wenyu Yang |
| author_sort | Xiuni Li |
| collection | DOAJ |
| description | Abstract Background RGB photographs are a powerful tool for dynamically estimating crop growth. Leaves are related to crop photosynthesis, transpiration, and nutrient uptake. Traditional blade parameter measurements were labor-intensive and time-consuming. Therefore, based on the phenotypic features extracted from RGB images, it is essential to choose the best model for soybean leaf parameter estimation. This research was carried out to speed up the breeding procedure and provide a novel technique for precisely estimating soybean leaf parameters. Results The findings demonstrate that using an Unet neural network, the IOU, PA, and Recall values for soybean image segmentation can achieve 0.98, 0.99, and 0.98, respectively. Overall, the average testing prediction accuracy (ATPA) of the three regression models is Random forest > Cat Boost > Simple nonlinear regression. The Random forest ATPAs for leaf number (LN), leaf fresh weight (LFW), and leaf area index (LAI) reached 73.45%, 74.96%, and 85.09%, respectively, which were 6.93%, 3.98%, and 8.01%, respectively, higher than those of the optimal Cat Boost model and 18.78%, 19.08%, and 10.88%, respectively, higher than those of the optimal SNR model. Conclusion The results show that the Unet neural network can separate soybeans accurately from an RGB image. The Random forest model has a strong ability for generalization and high accuracy for the estimation of leaf parameters. Combining cutting-edge machine learning methods with digital images improves the estimation of soybean leaf characteristics. |
| first_indexed | 2024-03-13T04:50:12Z |
| format | Article |
| id | doaj.art-201b5580fa894949ba99fc3ad4b4a243 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-03-13T04:50:12Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-201b5580fa894949ba99fc3ad4b4a2432023-06-18T11:15:42ZengBMCPlant Methods1746-48112023-06-0119111610.1186/s13007-023-01023-zSoybean leaf estimation based on RGB images and machine learning methodsXiuni Li0Xiangyao Xu1Shuai Xiang2Menggen Chen3Shuyuan He4Wenyan Wang5Mei Xu6Chunyan Liu7Liang Yu8Weiguo Liu9Wenyu Yang10College of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityCollege of Agronomy, Sichuan Agricultural UniversityAbstract Background RGB photographs are a powerful tool for dynamically estimating crop growth. Leaves are related to crop photosynthesis, transpiration, and nutrient uptake. Traditional blade parameter measurements were labor-intensive and time-consuming. Therefore, based on the phenotypic features extracted from RGB images, it is essential to choose the best model for soybean leaf parameter estimation. This research was carried out to speed up the breeding procedure and provide a novel technique for precisely estimating soybean leaf parameters. Results The findings demonstrate that using an Unet neural network, the IOU, PA, and Recall values for soybean image segmentation can achieve 0.98, 0.99, and 0.98, respectively. Overall, the average testing prediction accuracy (ATPA) of the three regression models is Random forest > Cat Boost > Simple nonlinear regression. The Random forest ATPAs for leaf number (LN), leaf fresh weight (LFW), and leaf area index (LAI) reached 73.45%, 74.96%, and 85.09%, respectively, which were 6.93%, 3.98%, and 8.01%, respectively, higher than those of the optimal Cat Boost model and 18.78%, 19.08%, and 10.88%, respectively, higher than those of the optimal SNR model. Conclusion The results show that the Unet neural network can separate soybeans accurately from an RGB image. The Random forest model has a strong ability for generalization and high accuracy for the estimation of leaf parameters. Combining cutting-edge machine learning methods with digital images improves the estimation of soybean leaf characteristics.https://doi.org/10.1186/s13007-023-01023-zSoybeanLeaf parametersEstimationRGBMachine learning |
| spellingShingle | Xiuni Li Xiangyao Xu Shuai Xiang Menggen Chen Shuyuan He Wenyan Wang Mei Xu Chunyan Liu Liang Yu Weiguo Liu Wenyu Yang Soybean leaf estimation based on RGB images and machine learning methods Plant Methods Soybean Leaf parameters Estimation RGB Machine learning |
| title | Soybean leaf estimation based on RGB images and machine learning methods |
| title_full | Soybean leaf estimation based on RGB images and machine learning methods |
| title_fullStr | Soybean leaf estimation based on RGB images and machine learning methods |
| title_full_unstemmed | Soybean leaf estimation based on RGB images and machine learning methods |
| title_short | Soybean leaf estimation based on RGB images and machine learning methods |
| title_sort | soybean leaf estimation based on rgb images and machine learning methods |
| topic | Soybean Leaf parameters Estimation RGB Machine learning |
| url | https://doi.org/10.1186/s13007-023-01023-z |
| work_keys_str_mv | AT xiunili soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT xiangyaoxu soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT shuaixiang soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT menggenchen soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT shuyuanhe soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT wenyanwang soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT meixu soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT chunyanliu soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT liangyu soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT weiguoliu soybeanleafestimationbasedonrgbimagesandmachinelearningmethods AT wenyuyang soybeanleafestimationbasedonrgbimagesandmachinelearningmethods |