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Liu Y, Ban S, Wei S, Li L, Tian M, Hu D, Liu W, Yuan T. Estimating the frost damage index in lettuce using UAV-based RGB and multispectral images. FRONTIERS IN PLANT SCIENCE 2024; 14:1242948. [PMID: 38239223 PMCID: PMC10794741 DOI: 10.3389/fpls.2023.1242948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 12/11/2023] [Indexed: 01/22/2024]
Abstract
Introduction The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage is great significant to the determination of the frost tolerance in lettuce. Methods We proposed a high-throughput method to estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images of open-field lettuce suffered from frost damage were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed to select FDI-sensitive features from RGB and multispectral images. Then the models were established for different FDI-sensitive features based on sensor types and different groups according to lettuce colors using multiple linear regression, support vector machine and neural network algorithms, respectively. Results and discussion Digital number of blue and red channels, spectral reflectance at blue, red and near-infrared bands as well as six vegetation indexes (VIs) were found to be significantly related to the FDI of all lettuce groups. The high sensitivity of four modified VIs to frost damage of all lettuce groups was confirmed. The average accuracy of models were improved by 3% to 14% through a combination of multisource features. Color of lettuce had a certain impact on the monitoring of frost damage by FDI prediction models, because the accuracy of models based on green lettuce group were generally higher. The MULTISURCE-GREEN-NN model with R2 of 0.715 and RMSE of 0.014 had the best performance, providing a high-throughput and efficient technical tool for frost damage investigation which will assist the identification of cold-resistant green lettuce germplasm and related breeding.
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Affiliation(s)
- Yiwen Liu
- College of Information Technology, Shanghai Ocean University, Shanghai, China
- Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai, China
| | - Songtao Ban
- Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai, China
| | - Shiwei Wei
- Jinshan Experimental Station, Shanghai Agrobiological Gene Center, Shanghai, China
| | - Linyi Li
- College of Information Technology, Shanghai Ocean University, Shanghai, China
- Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai, China
| | - Minglu Tian
- Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai, China
| | - Dong Hu
- Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai, China
| | - Weizhen Liu
- School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan, China
| | - Tao Yuan
- Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Shanghai, China
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Li L, Qiao J, Yao J, Li J, Li L. Automatic freezing-tolerant rapeseed material recognition using UAV images and deep learning. PLANT METHODS 2022; 18:5. [PMID: 35027060 PMCID: PMC8756653 DOI: 10.1186/s13007-022-00838-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Freezing injury is a devastating yet common damage that occurs to winter rapeseed during the overwintering period which directly reduces the yield and causes heavy economic loss. Thus, it is an important and urgent task for crop breeders to find the freezing-tolerant rapeseed materials in the process of breeding. Existing large-scale freezing-tolerant rapeseed material recognition methods mainly rely on the field investigation conducted by the agricultural experts using some professional equipments. These methods are time-consuming, inefficient and laborious. In addition, the accuracy of these traditional methods depends heavily on the knowledge and experience of the experts. METHODS To solve these problems of existing methods, we propose a low-cost freezing-tolerant rapeseed material recognition approach using deep learning and unmanned aerial vehicle (UAV) images captured by a consumer UAV. We formulate the problem of freezing-tolerant material recognition as a binary classification problem, which can be solved well using deep learning. The proposed method can automatically and efficiently recognize the freezing-tolerant rapeseed materials from a large number of crop candidates. To train the deep learning network, we first manually construct the real dataset using the UAV images of rapeseed materials captured by the DJI Phantom 4 Pro V2.0. Then, five classic deep learning networks (AlexNet, VGGNet16, ResNet18, ResNet50 and GoogLeNet) are selected to perform the freezing-tolerant rapeseed material recognition. RESULT AND CONCLUSION The accuracy of the five deep learning networks used in our work is all over 92%. Especially, ResNet50 provides the best accuracy (93.33[Formula: see text]) in this task. In addition, we also compare deep learning networks with traditional machine learning methods. The comparison results show that the deep learning-based methods significantly outperform the traditional machine learning-based methods in our task. The experimental results show that it is feasible to recognize the freezing-tolerant rapeseed using UAV images and deep learning.
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Affiliation(s)
- Lili Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
| | - Jiangwei Qiao
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan, China
| | - Jian Yao
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
- GongQing Institute of Science and Technology, Jiujiang, Jiangxi, China
| | - Jie Li
- School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, China
| | - Li Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China.
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