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Hamdy S, Charrier A, Corre LL, Rasti P, Rousseau D. Toward robust and high-throughput detection of seed defects in X-ray images via deep learning. PLANT METHODS 2024; 20:63. [PMID: 38711143 DOI: 10.1186/s13007-024-01195-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND The detection of internal defects in seeds via non-destructive imaging techniques is a topic of high interest to optimize the quality of seed lots. In this context, X-ray imaging is especially suited. Recent studies have shown the feasibility of defect detection via deep learning models in 3D tomography images. We demonstrate the possibility of performing such deep learning-based analysis on 2D X-ray radiography for a faster yet robust method via the X-Robustifier pipeline proposed in this article. RESULTS 2D X-ray images of both defective and defect-free seeds were acquired. A deep learning model based on state-of-the-art object detection neural networks is proposed. Specific data augmentation techniques are introduced to compensate for the low ratio of defects and increase the robustness to variation of the physical parameters of the X-ray imaging systems. The seed defects were accurately detected (F1-score >90%), surpassing human performance in computation time and error rates. The robustness of these models against the principal distortions commonly found in actual agro-industrial conditions is demonstrated, in particular, the robustness to physical noise, dimensionality reduction and the presence of seed coating. CONCLUSION This work provides a full pipeline to automatically detect common defects in seeds via 2D X-ray imaging. The method is illustrated on sugar beet and faba bean and could be efficiently extended to other species via the proposed generic X-ray data processing approach (X-Robustifier). Beyond a simple proof of feasibility, this constitutes important results toward the effective use in the routine of deep learning-based automatic detection of seed defects.
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Affiliation(s)
- Sherif Hamdy
- GEVES, Station Nationale d'Essais de Semences, 25 Georges Morel, 49070, Beaucouze, France
| | - Aurélie Charrier
- GEVES, Station Nationale d'Essais de Semences, 25 Georges Morel, 49070, Beaucouze, France
| | - Laurence Le Corre
- GEVES, Station Nationale d'Essais de Semences, 25 Georges Morel, 49070, Beaucouze, France
| | - Pejman Rasti
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAe IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49100, Angers, France
- Centre d'Études et de Recherche pour l'Aide à la Décision (CERADE), École d'ingénieurs (ESAIP), 49100, Angers, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAe IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49100, Angers, France.
- IRHS, INRAE, Institut Agro, Univ. Angers, SFR4207 QuaSaV, 42 Georges Morel CS 60057, 49071, Beaucouze, France.
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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. PLANTS (BASEL, SWITZERLAND) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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Solimani F, Cardellicchio A, Nitti M, Lako A, Dimauro G, Renò V. A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping. INFORMATION 2023. [DOI: 10.3390/info14040214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
Abstract
Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.
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Affiliation(s)
- Firozeh Solimani
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Angelo Cardellicchio
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Massimiliano Nitti
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
| | - Alfred Lako
- Faculty of Civil Engineering, Polytechnic University of Tirana, Bulevardi Dëshmorët e Kombit Nr. 4, 1000 Tiranë, Albania
| | - Giovanni Dimauro
- Department of Computer Science, University of Bari, Via E. Orabona, 4, 70125 Bari, Italy
| | - Vito Renò
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122 D/O, 70126 Bari, Italy
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Xue Q, Miao P, Miao K, Yu Y, Li Z. An online automatic sorting system for defective et using deep learning. CHINESE HERBAL MEDICINES 2023. [PMID: 37538869 PMCID: PMC10394327 DOI: 10.1016/j.chmed.2023.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
Objective To establish a deep-learning architecture based on faster region-based convolutional neural networks (Faster R-CNN) algorithm for detection and sorting of red ginseng (Ginseng Radix et Rhizoma Rubra) with internal defects automatically on an online X-ray machine vision system. Methods A Faster R-CNN based classifier was trained with around 20 000 samples with mean average precision value (mAP) of 0.95. A traditional image processing method based on feedforward neural network (FNN) obtained a bad performance with the accuracy, recall and specificity of 69.0%, 68.0%, and 70.0%, respectively. Therefore, the Faster R-CNN model was saved to evaluate the model performance on the defective red ginseng online sorting system. Results An independent set of 2 000 red ginsengs were used to validate the performance of the Faster R-CNN based online sorting system in three parallel tests, achieving accuracy of 95.8%, 95.2% and 96.2%, respectively. Conclusion The overall results indicated that the proposed Faster R-CNN based classification model has great potential for non-destructive detection of red ginseng with internal defects.
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Yasmin J, Ahmed MR, Wakholi C, Lohumi S, Mukasa P, Kim G, Kim J, Lee H, Cho BK. Near-infrared hyperspectral imaging for online measurement of the viability detection of naturally aged watermelon seeds. FRONTIERS IN PLANT SCIENCE 2022; 13:986754. [PMID: 36420027 PMCID: PMC9676662 DOI: 10.3389/fpls.2022.986754] [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: 07/05/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
The viability status of seeds before sowing is important to farmers as it allows them to make yield predictions. Monitoring the seed quality in a rapid and nondestructive manner may create a perfect solution, especially for industrial sorting applications. However, current offline laboratory-based strategies employed for the monitoring of seed viability are time-consuming and thus cannot satisfy industrial needs where there is a substantial number of seeds to be analyzed. In this study, we describe a prototype online near-infrared (NIR) hyperspectral imaging system that can be used for the rapid detection of seed viability. A wavelength range of 900-1700 nm was employed to obtain spectral images of three different varieties of naturally aged watermelon seed samples. The partial least square discriminant analysis (PLS-DA) model was employed for real-time viability prediction for seed samples moving through a conveyor unit at a speed of 49 mm/sec. A suction unit was further incorporated to develop the online system and it was programmatically controlled to separate the detected viable seeds from nonviable ones. For an external validation sample set showed classification accuracy levels of 91.8%, 80.7%, and 77.8% in relation to viability for the three varieties of watermelon seed with healthy seedling growth. The regression coefficients of the classification model distinguished some chemical differences in viable and nonviable seed which was verified by the chromatographic analysis after the detection of the proposed online system. The results demonstrated that the developed online system with the viability prediction model has the potential to be used in the seed industry for the quality monitoring of seeds.
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Affiliation(s)
- Jannat Yasmin
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Mohammed Raju Ahmed
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Collins Wakholi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Santosh Lohumi
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Perez Mukasa
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Geonwoo Kim
- Department of Bio-Industrial Machinery Engineering, College of Agriculture and Life Science, Gyeongsang National University, Jinju-si, Gyeongsangnam-do, South Korea
- Institute of Smart Farm, Gyeongsang National University, Jinju-si, Gyeongsangnam-do, South Korea
| | - Juntae Kim
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
| | - Hoonsoo Lee
- Department of Biosystems Engineering, College of Agriculture, Life & Environment Science, Chungbuk National University, Cheongju, Chungbuk, South Korea
| | - Byoung-Kwan Cho
- Department of Biosystems Machinery Engineering, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
- Department of Smart Agricultural Systems, College of Agricultural and Life Science, Chungnam National University, Daejeon, South Korea
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Xue Q, Miao P, Miao K, Yu Y, Li Z. X‐ray‐based machine vision technique for detection of internal defects of sterculia seeds. J Food Sci 2022; 87:3386-3395. [DOI: 10.1111/1750-3841.16237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/25/2022] [Accepted: 06/09/2022] [Indexed: 11/30/2022]
Affiliation(s)
- Qilong Xue
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
| | - Peiqi Miao
- Tianjin Modern Innovative TCM Technology Co. Ltd Tianjin China
| | - Kunhong Miao
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
| | - Yang Yu
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
| | - Zheng Li
- College of Pharmaceutical Engineering of Traditional Chinese Medicine Tianjin University of Traditional Chinese Medicine Tianjin China
- State Key Laboratory of Component Traditional Chinese Medicine Tianjin China
- Haihe Laboratory of Modern Chinese Medicine Tianjin China
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Abstract
A serious problem of vegetable production is the quality of sown seeds. In this regard, assessment of seed quality before sowing and storage is of great practical interest. The modern level of scientific research requires the use of instrumental automated methods of seed quality evaluation, allowing to obtain more information and in a shorter time. The material for the study was a variety of samples from the collection of Brassica oleracea L., var. capitata, Raphanus sativus L., var. radicula, and Lepidium sativum L. seeds from the Federal Scientific Center of Vegetable Breeding and the Timofeev Selection Station. Digital X-ray images of seeds were obtained using a mobile X-ray diagnostic device PRDU-02. Automatic analysis of digital X-ray images was performed in the software “VideoTesT-Morphology 5.2.” The following latent defects of cabbage seeds of economic importance were revealed and identified: irregular darkening, significant “patterning” with deep separation of embryo parts, “angularity of seeds” leading to the loss of their viability. Automatic analysis of digital X-ray images of seeds confirmed the informativeness of brightness indices of digital X-ray images, as well as shape indices. Their connection with sowing qualities of the studied seeds was established.
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Nehoshtan Y, Carmon E, Yaniv O, Ayal S, Rotem O. Robust seed germination prediction using deep learning and RGB image data. Sci Rep 2021; 11:22030. [PMID: 34764422 PMCID: PMC8586350 DOI: 10.1038/s41598-021-01712-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Accepted: 11/02/2021] [Indexed: 11/19/2022] Open
Abstract
Achieving seed germination quality standards poses a real challenge to seed companies as they are compelled to abide by strict certification rules, while having only partial seed separation solutions at their disposal. This discrepancy results with wasteful disqualification of seed lots holding considerable amounts of good seeds and further translates to financial losses and supply chain insecurity. Here, we present the first-ever generic germination prediction technology that is based on deep learning and RGB image data and facilitates seed classification by seed germinability and usability, two facets of germination fate. We show technology competence to render dozens of disqualified seed lots of seven vegetable crops, representing different genetics and production pipelines, industrially appropriate, and to adequately classify lots by utilizing available crop-level image data, instead of lot-specific data. These achievements constitute a major milestone in the deployment of this technology for industrial seed sorting by germination fate for multiple crops.
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Affiliation(s)
| | | | | | | | - Or Rotem
- Seed-X LTD, 5691000, Magshimim, Israel.
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