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Bektaş Ü, Isak MA, Bozkurt T, Dönmez D, İzgü T, Tütüncü M, Simsek Ö. Genotype-specific responses to in vitro drought stress in myrtle ( Myrtus communis L.): integrating machine learning techniques. PeerJ 2024; 12:e18081. [PMID: 39391827 PMCID: PMC11466237 DOI: 10.7717/peerj.18081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 08/20/2024] [Indexed: 10/12/2024] Open
Abstract
Background Myrtle (Myrtus communis L.), native to the Mediterranean region of Türkiye, is a valuable plant with applications in traditional medicine, pharmaceuticals, and culinary practices. Understanding how myrtle responds to water stress is essential for sustainable cultivation as climate change exacerbates drought conditions. Methods This study investigated the performance of selected myrtle genotypes under in vitro drought stress by employing tissue culture techniques, rooting trials, and acclimatization processes. Genotypes were tested under varying polyethylene glycol (PEG) concentrations (1%, 2%, 4%, and 6%). Machine learning (ML) algorithms, including Gaussian process (GP), support vector machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were utilized to model and predict micropropagation and rooting efficiency. Results The research revealed a genotype-dependent response to drought stress. Black-fruited genotypes exhibited higher micropropagation rates compared to white-fruited ones under stress conditions. The application of ML models successfully predicted micropropagation and rooting efficiency, providing insights into genotype performance. Conclusions The findings suggest that selecting drought-tolerant genotypes is crucial for enhancing myrtle cultivation. The results underscore the importance of genotype selection and optimization of cultivation practices to address climate change impacts. Future research should explore the molecular mechanisms of stress responses to refine breeding strategies and improve resilience in myrtle and similar economically important crops.
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Affiliation(s)
- Ümit Bektaş
- Faculty of Agriculture, Department of Horticulture, Erciyes University, Kayseri, Turkey
| | - Musab A. Isak
- Graduate School of Natural and Applied Sciences, Agricultural Sciences and Technologies Department, Erciyes University, Kayseri, Turkey
| | - Taner Bozkurt
- Tekfen Agricultural Research Production and Marketing Inc., Adana, Turkey
| | - Dicle Dönmez
- Biotechnology Research and Application Center, Çukurova University, Adana, Turkey
| | - Tolga İzgü
- Institute of BioEconomy, National Research Council of Italy, Florence, Italy
| | - Mehmet Tütüncü
- Department of Horticulture, Ondokuz Mayis University Samsun, Samsun, Turkey
| | - Özhan Simsek
- Faculty of Agriculture, Department of Horticulture, Erciyes University, Kayseri, Turkey
- Graduate School of Natural and Applied Sciences, Agricultural Sciences and Technologies Department, Erciyes University, Kayseri, Turkey
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Nasti L, Vecchiato G, Heuret P, Rowe NP, Palladino M, Marcati P. A Reinforcement Learning approach to study climbing plant behaviour. Sci Rep 2024; 14:18222. [PMID: 39107370 PMCID: PMC11303795 DOI: 10.1038/s41598-024-62147-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 05/14/2024] [Indexed: 08/10/2024] Open
Abstract
A plant's structure is the result of constant adaptation and evolution to the surrounding environment. From this perspective, our goal is to investigate the mass and radius distribution of a particular plant organ, namely the searcher shoot, by providing a Reinforcement Learning (RL) environment, that we call Searcher-Shoot, which considers the mechanics due to the mass of the shoot and leaves. We uphold the hypothesis that plants maximize their length, avoiding a maximal stress threshold. To do this, we explore whether the mass distribution along the stem is efficient, formulating a Markov Decision Process. By exploiting this strategy, we are able to mimic and thus study the plant's behavior, finding that shoots decrease their diameters smoothly, resulting in an efficient distribution of the mass. The strong accordance between our results and the experimental data allows us to remark on the strength of our approach in the analysis of biological systems traits.
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Affiliation(s)
- Lucia Nasti
- Gran Sasso Science Institute, L'Aquila, Italy.
| | | | - Patrick Heuret
- AMAP, Univ Montpellier, CIRAD, CNRS, INRAe, IRD, Montpellier, France
| | - Nicholas P Rowe
- AMAP, Univ Montpellier, CIRAD, CNRS, INRAe, IRD, Montpellier, France
| | - Michele Palladino
- Gran Sasso Science Institute, L'Aquila, Italy
- DISIM, Department of Information Engineering, Computer Science and Mathematics, University of L'Aquila, Via Vetoio, 67100, L'Aquila, Italy
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Fitzpatrick TB. B Vitamins: An Update on Their Importance for Plant Homeostasis. ANNUAL REVIEW OF PLANT BIOLOGY 2024; 75:67-93. [PMID: 38424064 DOI: 10.1146/annurev-arplant-060223-025336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
B vitamins are a source of coenzymes for a vast array of enzyme reactions, particularly those of metabolism. As metabolism is the basis of decisions that drive maintenance, growth, and development, B vitamin-derived coenzymes are key components that facilitate these processes. For over a century, we have known about these essential compounds and have elucidated their pathways of biosynthesis, repair, salvage, and degradation in numerous organisms. Only now are we beginning to understand their importance for regulatory processes, which are becoming an important topic in plants. Here, I highlight and discuss emerging evidence on how B vitamins are integrated into vital processes, from energy generation and nutrition to gene expression, and thereby contribute to the coordination of growth and developmental programs, particularly those that concern maintenance of a stable state, which is the foundational tenet of plant homeostasis.
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Isak MA, Bozkurt T, Tütüncü M, Dönmez D, İzgü T, Şimşek Ö. Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation. PLoS One 2024; 19:e0305111. [PMID: 38870239 PMCID: PMC11175477 DOI: 10.1371/journal.pone.0305111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Accepted: 05/24/2024] [Indexed: 06/15/2024] Open
Abstract
This study investigates the influence of cadmium (Cd) stress on the micropropagation of Goji Berry (Lycium barbarum L.) across three distinct genotypes (ERU, NQ1, NQ7), employing an array of machine learning (ML) algorithms, including Multilayer Perceptron (MLP), Support Vector Machines (SVM), Random Forest (RF), Gaussian Process (GP), and Extreme Gradient Boosting (XGBoost). The primary motivation is to elucidate genotype-specific responses to Cd stress, which poses significant challenges to agricultural productivity and food safety due to its toxicity. By analyzing the impacts of varying Cd concentrations on plant growth parameters such as proliferation, shoot and root lengths, and root numbers, we aim to develop predictive models that can optimize plant growth under adverse conditions. The ML models revealed complex relationships between Cd exposure and plant physiological changes, with MLP and RF models showing remarkable prediction accuracy (R2 values up to 0.98). Our findings contribute to understanding plant responses to heavy metal stress and offer practical applications in mitigating such stress in plants, demonstrating the potential of ML approaches in advancing plant tissue culture research and sustainable agricultural practices.
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Affiliation(s)
- Musab A. Isak
- Department of Agricultural Science and Technology, Graduate School of Natural and Applied Sciences Erciyes University, Kayseri, Türkiye
| | - Taner Bozkurt
- Tekfen Agricultural Research Production and Marketing Inc., Adana, Türkiye
| | - Mehmet Tütüncü
- Department of Horticulture, Faculty of Agriculture, Ondokuz Mayıs University, Samsun, Türkiye
| | - Dicle Dönmez
- Biotechnology Research and Application Center, Çukurova University, Adana, Türkiye
| | - Tolga İzgü
- Institute of BioEconomy, National Research Council of Italy (CNR), Florence, Italy
| | - Özhan Şimşek
- Department of Agricultural Science and Technology, Graduate School of Natural and Applied Sciences Erciyes University, Kayseri, Türkiye
- Department of Horticulture, Faculty of Agriculture, Erciyes University, Kayseri, Türkiye
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Kashyap GR, Sridhara S, Manoj KN, Gopakkali P, Das B, Jha PK, Prasad PVV. Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:1179-1197. [PMID: 38676745 DOI: 10.1007/s00484-024-02661-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/06/2024] [Accepted: 03/15/2024] [Indexed: 04/29/2024]
Abstract
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.
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Affiliation(s)
- Girish R Kashyap
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Shankarappa Sridhara
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India.
| | - Konapura Nagaraja Manoj
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Pradeep Gopakkali
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Bappa Das
- ICAR-Central Coastal Agricultural Research Institute, Old Goa, Goa, 403402, India
| | - Prakash Kumar Jha
- Department of Plant and Soil Sciences, Mississippi State University, Mississippi, MS, USA
| | - P V Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA
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Opara IK, Opara UL, Okolie JA, Fawole OA. Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review. PLANTS (BASEL, SWITZERLAND) 2024; 13:1200. [PMID: 38732414 PMCID: PMC11085577 DOI: 10.3390/plants13091200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.
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Affiliation(s)
- Ikechukwu Kingsley Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- Department of Food Science, Stellenbosch University, Stellenbosch 7600, South Africa
| | - Umezuruike Linus Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- UNESCO International Centre for Biotechnology, Nsukka 410001, Enugu State, Nigeria
| | - Jude A. Okolie
- Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Olaniyi Amos Fawole
- Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg 2006, South Africa
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Aasim M, Yıldırım B, Say A, Ali SA, Aytaç S, Nadeem MA. Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H 2O 2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.). PLANT MOLECULAR BIOLOGY 2024; 114:33. [PMID: 38526768 DOI: 10.1007/s11103-024-01427-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/14/2024] [Indexed: 03/27/2024]
Abstract
Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.
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Affiliation(s)
- Muhammad Aasim
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey.
| | - Buşra Yıldırım
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey
| | - Ahmet Say
- Department of Agricultural Biotechnology, Faculty of Agriculture, Erciyes University, Kayseri, Turkey
| | - Seyid Amjad Ali
- Department of Information Systems and Technologies, Bilkent University, Ankara, Turkey
| | - Selim Aytaç
- Institute of Hemp Researches, Ondokuz Mayis University, Samsun, Turkey
| | - Muhammad Azhar Nadeem
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey
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Jafari M, Daneshvar MH. Machine learning-mediated Passiflora caerulea callogenesis optimization. PLoS One 2024; 19:e0292359. [PMID: 38266002 PMCID: PMC10807783 DOI: 10.1371/journal.pone.0292359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/19/2023] [Indexed: 01/26/2024] Open
Abstract
Callogenesis is one of the most powerful biotechnological approaches for in vitro secondary metabolite production and indirect organogenesis in Passiflora caerulea. Comprehensive knowledge of callogenesis and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. In the present investigation, the callogenesis responses (i.e., callogenesis rate and callus fresh weight) of P. caerulea were predicted based on different types and concentrations of plant growth regulators (PGRs) (i.e., 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), 1-naphthaleneacetic acid (NAA), and indole-3-Butyric Acid (IBA)) as well as explant types (i.e., leaf, node, and internode) using multilayer perceptron (MLP). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and explant types for maximizing callogenesis responses. Furthermore, sensitivity analysis was conducted to assess the importance of each input variable on the callogenesis responses. The results showed that MLP had high predictive accuracy (R2 > 0.81) in both training and testing sets for modeling all studied parameters. Based on the results of the optimization process, the highest callogenesis rate (100%) would be obtained from the leaf explant cultured in the medium supplemented with 0.52 mg/L IBA plus 0.43 mg/L NAA plus 1.4 mg/L 2,4-D plus 0.2 mg/L BAP. The results of the sensitivity analysis showed the explant-dependent impact of the exogenous application of PGRs on callogenesis. Generally, the results showed that a combination of MLP and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
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Affiliation(s)
- Marziyeh Jafari
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, Iran
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
| | - Mohammad Hosein Daneshvar
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Iran
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Mardoc E, Sow MD, Déjean S, Salse J. Genomic data integration tutorial, a plant case study. BMC Genomics 2024; 25:66. [PMID: 38233804 PMCID: PMC10792847 DOI: 10.1186/s12864-023-09833-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 01/19/2024] Open
Abstract
BACKGROUND The ongoing evolution of the Next Generation Sequencing (NGS) technologies has led to the production of genomic data on a massive scale. While tools for genomic data integration and analysis are becoming increasingly available, the conceptual and analytical complexities still represent a great challenge in many biological contexts. RESULTS To address this issue, we describe a six-steps tutorial for the best practices in genomic data integration, consisting of (1) designing a data matrix; (2) formulating a specific biological question toward data description, selection and prediction; (3) selecting a tool adapted to the targeted questions; (4) preprocessing of the data; (5) conducting preliminary analysis, and finally (6) executing genomic data integration. CONCLUSION The tutorial has been tested and demonstrated on publicly available genomic data generated from poplar (Populus L.), a woody plant model. We also developed a new graphical output for the unsupervised multi-block analysis, cimDiablo_v2, available at https://forgemia.inra.fr/umr-gdec/omics-integration-on-poplar , and allowing the selection of master drivers in genomic data variation and interplay.
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Affiliation(s)
- Emile Mardoc
- UCA-INRAE UMR 1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Mamadou Dia Sow
- UCA-INRAE UMR 1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000, Clermont-Ferrand, France
| | - Sébastien Déjean
- Institut de Mathématiques de Toulouse, UMR 5219, Université de Toulouse, CNRS, Université Paul Sabatier, Toulouse, France
| | - Jérôme Salse
- UCA-INRAE UMR 1095 Genetics, Diversity and Ecophysiology of Cereals (GDEC), 5 Chemin de Beaulieu, 63000, Clermont-Ferrand, France.
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Damásio M, Barbosa M, Deus J, Fernandes E, Leitão A, Albino L, Fonseca F, Silvestre J. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. PLANTS (BASEL, SWITZERLAND) 2023; 12:4142. [PMID: 38140469 PMCID: PMC10747955 DOI: 10.3390/plants12244142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).
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Affiliation(s)
- Miguel Damásio
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Miguel Barbosa
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - João Deus
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Eduardo Fernandes
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - André Leitão
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Luís Albino
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Filipe Fonseca
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - José Silvestre
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
- GREEN-IT Bioresources4sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
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Pepe M, Hesami M, de la Cerda KA, Perreault ML, Hsiang T, Jones AMP. A journey with psychedelic mushrooms: From historical relevance to biology, cultivation, medicinal uses, biotechnology, and beyond. Biotechnol Adv 2023; 69:108247. [PMID: 37659744 DOI: 10.1016/j.biotechadv.2023.108247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/29/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023]
Abstract
Psychedelic mushrooms containing psilocybin and related tryptamines have long been used for ethnomycological purposes, but emerging evidence points to the potential therapeutic value of these mushrooms to address modern neurological, psychiatric health, and related disorders. As a result, psilocybin containing mushrooms represent a re-emerging frontier for mycological, biochemical, neuroscience, and pharmacology research. This work presents crucial information related to traditional use of psychedelic mushrooms, as well as research trends and knowledge gaps related to their diversity and distribution, technologies for quantification of tryptamines and other tryptophan-derived metabolites, as well as biosynthetic mechanisms for their production within mushrooms. In addition, we explore the current state of knowledge for how psilocybin and related tryptamines are metabolized in humans and their pharmacological effects, including beneficial and hazardous human health implications. Finally, we describe opportunities and challenges for investigating the production of psychedelic mushrooms and metabolic engineering approaches to alter secondary metabolite profiles using biotechnology integrated with machine learning. Ultimately, this critical review of all aspects related to psychedelic mushrooms represents a roadmap for future research efforts that will pave the way to new applications and refined protocols.
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Affiliation(s)
- Marco Pepe
- Department of Plant Agriculture, University of Guelph, Ontario N1G 2W1, Guelph, Canada
| | - Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Ontario N1G 2W1, Guelph, Canada
| | - Karla A de la Cerda
- School of Environmental Sciences, University of Guelph, Ontario N1G 2W1, Guelph, Canada
| | - Melissa L Perreault
- Departments of Biomedical Sciences, University of Guelph, Guelph, Ontario, Canada
| | - Tom Hsiang
- School of Environmental Sciences, University of Guelph, Ontario N1G 2W1, Guelph, Canada
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Rezaei H, Mirzaie-asl A, Abdollahi MR, Tohidfar M. Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis. PLoS One 2023; 18:e0293754. [PMID: 37922261 PMCID: PMC10624318 DOI: 10.1371/journal.pone.0293754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/18/2023] [Indexed: 11/05/2023] Open
Abstract
The important feature of petunia in tissue culture is its unpredictable and genotype-dependent callogenesis, posing challenges for efficient regeneration and biotechnology applications. To address this issue, machine learning (ML) can be considered a powerful tool to analyze callogenesis data, extract key parameters, and predict optimal conditions for petunia callogenesis, facilitating more controlled and productive tissue culture processes. The study aimed to develop a predictive model for callogenesis in petunia using ML algorithms and to optimize the concentrations of phytohormones to enhance callus formation rate (CFR) and callus fresh weight (CFW). The inputs for the model were BAP, KIN, IBA, and NAA, while the outputs were CFR and CFW. Three ML algorithms, namely MLP, RBF, and GRNN, were compared, and the results revealed that GRNN (R2≥83) outperformed MLP and RBF in terms of accuracy. Furthermore, a sensitivity analysis was conducted to determine the relative importance of the four phytohormones. IBA exhibited the highest importance, followed by NAA, BAP, and KIN. Leveraging the superior performance of the GRNN model, a genetic algorithm (GA) was integrated to optimize the concentration of phytohormones for maximizing CFR and CFW. The genetic algorithm identified an optimized combination of phytohormones consisting of 1.31 mg/L BAP, 1.02 mg/L KIN, 1.44 mg/L NAA, and 1.70 mg/L IBA, resulting in 95.83% CFR. To validate the reliability of the predicted results, optimized combinations of phytohormones were tested in a laboratory experiment. The results of the validation experiment indicated no significant difference between the experimental and optimized results obtained through the GA. This study presents a novel approach combining ML, sensitivity analysis, and GA for modeling and predicting callogenesis in petunia. The findings offer valuable insights into the optimization of phytohormone concentrations, facilitating improved callus formation and potential applications in plant tissue culture and genetic engineering.
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Affiliation(s)
- Hamed Rezaei
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Asghar Mirzaie-asl
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Mohammad Reza Abdollahi
- Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
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Sahu SK, Waseem M, Aslam MM. Editorial: Bioinformatics, big data and agriculture: a challenge for the future. FRONTIERS IN PLANT SCIENCE 2023; 14:1271305. [PMID: 37908837 PMCID: PMC10614287 DOI: 10.3389/fpls.2023.1271305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 10/04/2023] [Indexed: 11/02/2023]
Affiliation(s)
- Sunil Kumar Sahu
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - Muhammad Waseem
- School of Tropical Agriculture and Forestry (School of Agriculture and Rural Affairs, School of Rural Revitalization), Hainan University, Haikou, Hainan, China
- Hainan Yazhou Bay Seed Laboratory, Sanya Nanfan Researh, Sanya, China
- Fang Zhiyuan Academician Team Innovation Center of Hainan Province, Haikou, China
| | - Mehtab Muhammad Aslam
- School of Life Sciences and State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, China
- College of Agriculture, Food and Natural Resources (CAFNR), Division of Plant Sciences & Technology, University of Missouri, Columbia, MO, United States
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14
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Varghese R, Cherukuri AK, Doddrell NH, Doss CGP, Simkin AJ, Ramamoorthy S. Machine learning in photosynthesis: Prospects on sustainable crop development. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 335:111795. [PMID: 37473784 DOI: 10.1016/j.plantsci.2023.111795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 07/10/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023]
Abstract
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.
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Affiliation(s)
- Ressin Varghese
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
| | | | - C George Priya Doss
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Andrew J Simkin
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK; School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India.
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Cui Z, Li K, Kang C, Wu Y, Li T, Li M. Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset. PLANTS (BASEL, SWITZERLAND) 2023; 12:3280. [PMID: 37765444 PMCID: PMC10534746 DOI: 10.3390/plants12183280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Efficient image recognition is important in crop and forest management. However, it faces many challenges, such as the large number of plant species and diseases, the variability of plant appearance, and the scarcity of labeled data for training. To address this issue, we modified a SOTA Cross-Domain Few-shot Learning (CDFSL) method based on prototypical networks and attention mechanisms. We employed attention mechanisms to perform feature extraction and prototype generation by focusing on the most relevant parts of the images, then used prototypical networks to learn the prototype of each category and classify new instances. Finally, we demonstrated the effectiveness of the modified CDFSL method on several plant and disease recognition datasets. The results showed that the modified pipeline was able to recognize several cross-domain datasets using generic representations, and achieved up to 96.95% and 94.07% classification accuracy on datasets with the same and different domains, respectively. In addition, we visualized the experimental results, demonstrating the model's stable transfer capability between datasets and the model's high visual correlation with plant and disease biological characteristics. Moreover, by extending the classes of different semantics within the training dataset, our model can be generalized to other domains, which implies broad applicability.
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Affiliation(s)
| | | | | | | | | | - Mingyang Li
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.C.); (K.L.); (C.K.); (Y.W.); (T.L.)
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16
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Jafari M, Daneshvar MH. Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms. BMC Biotechnol 2023; 23:27. [PMID: 37528396 PMCID: PMC10394921 DOI: 10.1186/s12896-023-00796-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/21/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Optimization of indirect shoot regeneration protocols is one of the key prerequisites for the development of Agrobacterium-mediated genetic transformation and/or genome editing in Passiflora caerulea. Comprehensive knowledge of indirect shoot regeneration and optimized protocol can be obtained by the application of a combination of machine learning (ML) and optimization algorithms. MATERIALS AND METHODS In the present investigation, the indirect shoot regeneration responses (i.e., de novo shoot regeneration rate, the number of de novo shoots, and length of de novo shoots) of P. caerulea were predicted based on different types and concentrations of PGRs (i.e., TDZ, BAP, PUT, KIN, and IBA) as well as callus types (i.e., callus derived from different explants including leaf, node, and internode) using generalized regression neural network (GRNN) and random forest (RF). Moreover, the developed models were integrated into the genetic algorithm (GA) to optimize the concentration of PGRs and callus types for maximizing indirect shoot regeneration responses. Moreover, sensitivity analysis was conducted to assess the importance of each input variable on the studied parameters. RESULTS The results showed that both algorithms (RF and GRNN) had high predictive accuracy (R2 > 0.86) in both training and testing sets for modeling all studied parameters. Based on the results of optimization process, the highest de novo shoot regeneration rate (100%) would be obtained from callus derived from nodal segments cultured in the medium supplemented with 0.77 mg/L BAP plus 2.41 mg/L PUT plus 0.06 mg/L IBA. The results of the sensitivity analysis showed the explant-dependent impact of exogenous application of PGRs on indirect de novo shoot regeneration. CONCLUSIONS A combination of ML (GRNN and RF) and GA can display a forward-thinking aid to optimize and predict in vitro culture systems and consequentially cope with several challenges faced currently in Passiflora tissue culture.
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Affiliation(s)
- Marziyeh Jafari
- Department of Horticultural Science, College of Agriculture, Shiraz University, Shiraz, 7144113131, Iran.
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, 6341773637, Iran.
| | - Mohammad Hosein Daneshvar
- Department of Horticultural Sciences, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, 6341773637, Iran
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Huang Y. Improved SVM-Based Soil-Moisture-Content Prediction Model for Tea Plantation. PLANTS (BASEL, SWITZERLAND) 2023; 12:2309. [PMID: 37375934 DOI: 10.3390/plants12122309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/04/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Accurate prediction of soil moisture content in tea plantations plays a crucial role in optimizing irrigation practices and improving crop productivity. Traditional methods for SMC prediction are difficult to implement due to high costs and labor requirements. While machine learning models have been applied, their performance is often limited by the lack of sufficient data. To address the challenges of inaccurate and inefficient soil moisture prediction in tea plantations and enhance predictive performance, an improved support-vector-machine- (SVM) based model was developed to predict the SMC in a tea plantation. The proposed model addresses several limitations of existing approaches by incorporating novel features and enhancing the SVM algorithm's performance, which was improved with the Bald Eagle Search algorithm (BES) method for hyper-parameter optimization. The study utilized a comprehensive dataset comprising soil moisture measurements and relevant environmental variables collected from a tea plantation. Feature selection techniques were applied to identify the most informative variables, including rainfall, temperature, humidity, and soil type. The selected features were then used to train and optimize the SVM model. The proposed model was applied to prediction of soil water moisture in a tea plantation in Guangxi State-owned Fuhu Overseas Chinese Farm. Experimental results demonstrated the superior performance of the improved SVM model in predicting soil moisture content compared to traditional SVM approaches and other machine-learning algorithms. The model exhibited high accuracy, robustness, and generalization capabilities across different time periods and geographical locations with R2, MSE, and RMSE of 0.9435, 0.0194 and 0.1392, respectively, which helps to enhance the prediction performance, especially when limited real data are available. The proposed SVM-based model offers several advantages for tea plantation management. It provides timely and accurate soil moisture predictions, enabling farmers to make informed decisions regarding irrigation scheduling and water resource management. By optimizing irrigation practices, the model helps enhance tea crop yield, minimize water usage, and reduce environmental impact.
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Affiliation(s)
- Ying Huang
- Electronic Information School, Wuhan University, Wuhan 430072, China
- School of Automatic Control, Liuzhou Railway Vocational Technical College, Liuzhou 545616, China
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18
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Rezaei H, Mirzaie-asl A, Abdollahi MR, Tohidfar M. Comparative analysis of different artificial neural networks for predicting and optimizing in vitro seed germination and sterilization of petunia. PLoS One 2023; 18:e0285657. [PMID: 37167278 PMCID: PMC10174541 DOI: 10.1371/journal.pone.0285657] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/27/2023] [Indexed: 05/13/2023] Open
Abstract
The process of optimizing in vitro seed sterilization and germination is a complicated task since this process is influenced by interactions of many factors (e.g., genotype, disinfectants, pH of the media, temperature, light, immersion time). This study investigated the role of various types and concentrations of disinfectants (i.e., NaOCl, Ca(ClO)2, HgCl2, H2O2, NWCN-Fe, MWCNT) as well as immersion time in successful in vitro seed sterilization and germination of petunia. Also, the utility of three artificial neural networks (ANNs) (e.g., multilayer perceptron (MLP), radial basis function (RBF), and generalized regression neural network (GRNN)) as modeling tools were evaluated to analyze the effect of disinfectants and immersion time on in vitro seed sterilization and germination. Moreover, non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the selected prediction model. The GRNN algorithm displayed superior predictive accuracy in comparison to MLP and RBF models. Also, the results showed that NSGA‑II can be considered as a reliable multi-objective optimization algorithm for finding the optimal level of disinfectants and immersion time to simultaneously minimize contamination rate and maximize germination percentage. Generally, GRNN-NSGA-II as an up-to-date and reliable computational tool can be applied in future plant in vitro culture studies.
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Affiliation(s)
- Hamed Rezaei
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Asghar Mirzaie-asl
- Department of Plant Biotechnology, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Mohammad Reza Abdollahi
- Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran
| | - Masoud Tohidfar
- Department of Plant Biotechnology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
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Yoosefzadeh Najafabadi M, Hesami M, Eskandari M. Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs. Genes (Basel) 2023; 14:genes14040777. [PMID: 37107535 PMCID: PMC10137951 DOI: 10.3390/genes14040777] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/11/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.
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Affiliation(s)
| | - Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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20
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Das J, Kumar S, Mishra DC, Chaturvedi KK, Paul RK, Kairi A. Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system. Front Genet 2023; 13:1085332. [PMID: 36699447 PMCID: PMC9868961 DOI: 10.3389/fgene.2022.1085332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.
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Affiliation(s)
- Jutan Das
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjeev Kumar
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India,*Correspondence: Sanjeev Kumar,
| | | | | | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Amit Kairi
- ICAR-Indian Agricultural Research Institute, New Delhi, India
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21
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Current status and future prospects in cannabinoid production through in vitro culture and synthetic biology. Biotechnol Adv 2023; 62:108074. [PMID: 36481387 DOI: 10.1016/j.biotechadv.2022.108074] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/27/2022] [Accepted: 11/30/2022] [Indexed: 12/12/2022]
Abstract
For centuries, cannabis has been a rich source of fibrous, pharmaceutical, and recreational ingredients. Phytocannabinoids are the most important and well-known class of cannabis-derived secondary metabolites and display a broad range of health-promoting and psychoactive effects. The unique characteristics of phytocannabinoids (e.g., metabolite likeness, multi-target spectrum, and safety profile) have resulted in the development and approval of several cannabis-derived drugs. While most work has focused on the two main cannabinoids produced in the plant, over 150 unique cannabinoids have been identified. To meet the rapidly growing phytocannabinoid demand, particularly many of the minor cannabinoids found in low amounts in planta, biotechnology offers promising alternatives for biosynthesis through in vitro culture and heterologous systems. In recent years, the engineered production of phytocannabinoids has been obtained through synthetic biology both in vitro (cell suspension culture and hairy root culture) and heterologous systems. However, there are still several bottlenecks (e.g., the complexity of the cannabinoid biosynthetic pathway and optimizing the bioprocess), hampering biosynthesis and scaling up the biotechnological process. The current study reviews recent advances related to in vitro culture-mediated cannabinoid production. Additionally, an integrated overview of promising conventional approaches to cannabinoid production is presented. Progress toward cannabinoid production in heterologous systems and possible avenues for avoiding autotoxicity are also reviewed and highlighted. Machine learning is then introduced as a powerful tool to model, and optimize bioprocesses related to cannabinoid production. Finally, regulation and manipulation of the cannabinoid biosynthetic pathway using CRISPR- mediated metabolic engineering is discussed.
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Brunetto G, Stefanello LO, Kulmann MSDS, Tassinari A, de Souza ROS, Rozane DE, Tiecher TL, Ceretta CA, Ferreira PAA, de Siqueira GN, Parent LÉ. Prediction of Nitrogen Dosage in ‘Alicante Bouschet’ Vineyards with Machine Learning Models. PLANTS 2022; 11:plants11182419. [PMID: 36145819 PMCID: PMC9501305 DOI: 10.3390/plants11182419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 12/02/2022]
Abstract
Vineyard soils normally do not provide the amount of nitrogen (N) necessary for red wine production. Traditionally, the N concentration in leaves guides the N fertilization of vineyards to reach high grape yields and chemical composition under the ceteris paribus assumption. Moreover, the carryover effects of nutrients and carbohydrates stored by perennials such as grapevines are neglected. Where a well-documented database is assembled, machine learning (ML) methods can account for key site-specific features and carryover effects, impacting the performance of grapevines. The aim of this study was to predict, using ML tools, N management from local features to reach high berry yield and quality in ‘Alicante Bouschet’ vineyards. The 5-year (2015–2019) fertilizer trial comprised six N doses (0–20–40–60–80–100 kg N ha−1) and three regimes of irrigation. Model features included N dosage, climatic indices, foliar N application, and stem diameter of the preceding season, all of which were indices of the carryover effects. Accuracy of ML models was the highest with a yield cutoff of 14 t ha−1 and a total anthocyanin content (TAC) of 3900 mg L−1. Regression models were more accurate for total soluble solids (TSS), total titratable acidity (TTA), pH, TAC, and total phenolic content (TPC) in the marketable grape yield. The tissue N ranges differed between high marketable yield and TAC, indicating a trade-off about 24 g N kg−1 in the diagnostic leaf. The N dosage predicted varied from 0 to 40 kg N ha−1 depending on target variable, this was calculated from local features and carryover effects but excluded climatic indices. The dataset can increase in size and diversity with the collaboration of growers, which can help to cross over the numerous combinations of features found in vineyards. This research contributes to the rational use of N fertilizers, but with the guarantee that obtaining high productivity must be with adequate composition.
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Affiliation(s)
- Gustavo Brunetto
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Correspondence: ; Tel.: +55-32208108
| | | | | | - Adriele Tassinari
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | - Danilo Eduardo Rozane
- Fruticulture Department, State University of Paulista “Julio Mesquita Filho”, Registro 11900-000, Brazil
| | - Tadeu Luis Tiecher
- Rio Grande do Sul Federal Institute, Campus Restinga, Porto Alegre 91791-508, Brazil
| | - Carlos Alberto Ceretta
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | | | - Léon Étienne Parent
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Department of Soil and Agri-Food Engineering, Laval University, Québec City, QC G1V 0A6, Canada
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23
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Latif G, Abdelhamid SE, Mallouhy RE, Alghazo J, Kazimi ZA. Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model. PLANTS 2022; 11:plants11172230. [PMID: 36079612 PMCID: PMC9460897 DOI: 10.3390/plants11172230] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/19/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022]
Abstract
Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.
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Affiliation(s)
- Ghazanfar Latif
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
- Department of Computer Sciences and Mathematics, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Québec, QC G7H 2B1, Canada
- Correspondence:
| | - Sherif E. Abdelhamid
- Department of Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USA
| | - Roxane Elias Mallouhy
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
| | - Jaafar Alghazo
- Department of Computer Engineering, Virginia Military Institute, Lexington, VA 24450, USA
| | - Zafar Abbas Kazimi
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
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A Machine-Learning Method to Assess Growth Patterns in Plants of the Family Lemnaceae. PLANTS 2022; 11:plants11151910. [PMID: 35893614 PMCID: PMC9332063 DOI: 10.3390/plants11151910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/19/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022]
Abstract
Numerous new technologies have been implemented in image analysis methods that help researchers draw scientific conclusions from biological phenomena. Plants of the family Lemnaceae (duckweeds) are the smallest flowering plants in the world, and biometric measurements of single plants and their growth rate are highly challenging. Although the use of software for digital image analysis has changed the way scientists extract phenomenological data (also for studies on duckweeds), the procedure is often not wholly automated and sometimes relies on the intervention of a human operator. Such a constraint can limit the objectivity of the measurements and generally slows down the time required to produce scientific data. Herein lies the need to implement image analysis software with artificial intelligence that can substitute the human operator. In this paper, we present a new method to study the growth rates of the plants of the Lemnaceae family based on the application of machine-learning procedures to digital image analysis. The method is compared to existing analogical and computer-operated procedures. The results showed that our method drastically reduces the time consumption of the human operator while retaining a high correlation in the growth rates measured with other procedures. As expected, machine-learning methods applied to digital image analysis can overcome the constraints of measuring growth rates of very small plants and might help duckweeds gain worldwide attention thanks to their strong nutritional qualities and biological plasticity.
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