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Safdar LB, Dugina K, Saeidan A, Yoshicawa GV, Caporaso N, Gapare B, Umer MJ, Bhosale RA, Searle IR, Foulkes MJ, Boden SA, Fisk ID. Reviving grain quality in wheat through non-destructive phenotyping techniques like hyperspectral imaging. Food Energy Secur 2023; 12:e498. [PMID: 38440412 PMCID: PMC10909436 DOI: 10.1002/fes3.498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 03/06/2024] Open
Abstract
A long-term goal of breeders and researchers is to develop crop varieties that can resist environmental stressors and produce high yields. However, prioritising yield often compromises improvement of other key traits, including grain quality, which is tedious and time-consuming to measure because of the frequent involvement of destructive phenotyping methods. Recently, non-destructive methods such as hyperspectral imaging (HSI) have gained attention in the food industry for studying wheat grain quality. HSI can quantify variations in individual grains, helping to differentiate high-quality grains from those of low quality. In this review, we discuss the reduction of wheat genetic diversity underlying grain quality traits due to modern breeding, key traits for grain quality, traditional methods for studying grain quality and the application of HSI to study grain quality traits in wheat and its scope in breeding. Our critical review of literature on wheat domestication, grain quality traits and innovative technology introduces approaches that could help improve grain quality in wheat.
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Affiliation(s)
- Luqman B. Safdar
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
- International Flavour Research Centre (Adelaide), School of Agriculture, Food and Wine and Waite Research InstituteUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | - Kateryna Dugina
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
| | - Ali Saeidan
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
| | - Guilherme V. Yoshicawa
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | | | - Brighton Gapare
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - M. Jawad Umer
- Cotton Research InstituteChinese Academy of Agricultural SciencesAnyangChina
| | - Rahul A. Bhosale
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - Iain R. Searle
- School of Biological SciencesUniversity of AdelaideAdelaideSouth AustraliaAustralia
| | - M. John Foulkes
- Division of Plant and Crop Sciences, School of BiosciencesUniversity of NottinghamLoughboroughUK
| | - Scott A. Boden
- Plant Research Centre, School of Agriculture, Food and WineUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
| | - Ian D. Fisk
- International Flavour Research Centre, Division of Food, Nutrition and DieteticsUniversity of NottinghamLoughboroughUK
- International Flavour Research Centre (Adelaide), School of Agriculture, Food and Wine and Waite Research InstituteUniversity of AdelaideGlen OsmondSouth AustraliaAustralia
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Sun W, He Q, Liu J, Xiao X, Wu Y, Zhou S, Ma S, Wang R. Dynamic monitoring of maize grain quality based on remote sensing data. Front Plant Sci 2023; 14:1177477. [PMID: 37426960 PMCID: PMC10325687 DOI: 10.3389/fpls.2023.1177477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/31/2023] [Indexed: 07/11/2023]
Abstract
Remote sensing data have been widely used to monitor crop development, grain yield, and quality, while precise monitoring of quality traits, especially grain starch and oil contents considering meteorological elements, still needs to be improved. In this study, the field experiment with different sowing time, i.e., 8 June, 18 June, 28 June, and 8 July, was conducted in 2018-2020. The scalable annual and inter-annual quality prediction model for summer maize in different growth periods was established using hierarchical linear modeling (HLM), which combined hyperspectral and meteorological data. Compared with the multiple linear regression (MLR) using vegetation indices (VIs), the prediction accuracy of HLM was obviously improved with the highest R 2, root mean square error (RMSE), and mean absolute error (MAE) values of 0.90, 0.10, and 0.08, respectively (grain starch content (GSC)); 0.87, 0.10, and 0.08, respectively (grain protein content (GPC)); and 0.74, 0.13, and 0.10, respectively (grain oil content (GOC)). In addition, the combination of the tasseling, grain-filling, and maturity stages further improved the predictive power for GSC (R 2 = 0.96). The combination of the grain-filling and maturity stages further improved the predictive power for GPC (R 2 = 0.90). The prediction accuracy developed in the combination of the jointing and tasseling stages for GOC (R 2 = 0.85). The results also showed that meteorological factors, especially precipitation, had a great influence on grain quality monitoring. Our study provided a new idea for crop quality monitoring by remote sensing.
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Affiliation(s)
- Weiwei Sun
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Qijin He
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
- Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, China
| | - Jiahong Liu
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Xiao Xiao
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Yaxin Wu
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Sijia Zhou
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Selimai Ma
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
| | - Rongwan Wang
- College of Resources and Environmental Sciences, China Agricultural University, Beijing, China
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Lysov M, Pukhkiy K, Vasiliev E, Getmanskaya A, Turlapov V. Ensuring Explainability and Dimensionality Reduction in a Multidimensional HSI World for Early XAI-Diagnostics of Plant Stress. Entropy (Basel) 2023; 25:e25050801. [PMID: 37238556 DOI: 10.3390/e25050801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 05/08/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023]
Abstract
This work is mostly devoted to the search for effective solutions to the problem of early diagnosis of plant stress (given an example of wheat and its drought stress), which would be based on explainable artificial intelligence (XAI). The main idea is to combine the benefits of two of the most popular agricultural data sources, hyperspectral images (HSI) and thermal infrared images (TIR), in a single XAI model. Our own dataset of a 25-day experiment was used, which was created via both (1) an HSI camera Specim IQ (400-1000 nm, 204, 512 × 512) and (2) a TIR camera Testo 885-2 (320 × 240, res. 0.1 °C). The HSI were a source of the k-dimensional high-level features of plants (k ≤ K, where K is the number of HSI channels) for the learning process. Such combination was implemented as a single-layer perceptron (SLP) regressor, which is the main feature of the XAI model and receives as input an HSI pixel-signature belonging to the plant mask, which then automatically through the mask receives a mark from the TIR. The correlation of HSI channels with the TIR image on the plant's mask on the days of the experiment was studied. It was established that HSI channel 143 (820 nm) was the most correlated with TIR. The problem of training the HSI signatures of plants with their corresponding temperature value via the XAI model was solved. The RMSE of plant temperature prediction is 0.2-0.3 °C, which is acceptable for early diagnostics. Each HSI pixel was represented in training by a number (k) of channels (k ≤ K = 204 in our case). The number of channels used for training was minimized by a factor of 25-30, from 204 to eight or seven, while maintaining the RMSE value. The model is computationally efficient in training; the average training time was much less than one minute (Intel Core i3-8130U, 2.2 GHz, 4 cores, 4 GB). This XAI model can be considered a research-aimed model (R-XAI), which allows the transfer of knowledge about plants from the TIR domain to the HSI domain, with their contrasting onto only a few from hundreds of HSI channels.
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Affiliation(s)
- Maxim Lysov
- Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhny Novgorod, Russia
| | - Konstantin Pukhkiy
- Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhny Novgorod, Russia
| | - Evgeny Vasiliev
- Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhny Novgorod, Russia
| | - Alexandra Getmanskaya
- Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhny Novgorod, Russia
| | - Vadim Turlapov
- Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhny Novgorod, Russia
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Zhang M, Chen T, Gu X, Chen D, Wang C, Wu W, Zhu Q, Zhao C. Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods. Front Plant Sci 2023; 14:1073346. [PMID: 36968402 PMCID: PMC10030857 DOI: 10.3389/fpls.2023.1073346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.
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Affiliation(s)
- Mingzheng Zhang
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
| | - Tian’en Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Xiaohe Gu
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Dong Chen
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Cong Wang
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Wenbiao Wu
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
| | - Qingzhen Zhu
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Chunjiang Zhao
- School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China
- Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China
- Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China
- Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China
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