Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture

Miller, Tymoteusz; Mikiciuk, Grzegorz; Kisiel, Anna; Mikiciuk, Małgorzata; Paliwoda, Dominika; Sas-Paszt, Lidia; Cembrowska-Lech, Danuta; Krzemińska, Adrianna; Kozioł, Agnieszka; Brysiewicz, Adam

doi:10.3390/agriculture13081622

Cited by 9 publications

(5 citation statements)

References 65 publications

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“…The negative impact observed in the ANN model underscores the need for careful consideration of storage parameters to mitigate potential risks associated with the proliferation of undesirable microorganisms [57]. The integration of ANN modeling with a focus on the day of storage provides valuable insights into the dynamic nature of microbiological responses in kombucha fresh cheese [58,59]. Figure 13 illustrates the impact of input variables on the relative importance of TP, DPPH, FRAP, and ABTS.…”

Section: The Accuracy Of the Modelmentioning

confidence: 99%

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

Lončar,

Pezo,

Iličić

et al. 2024

Foods

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In this study, an Artificial Neural Network (ANN) model is used to solve the complex task of producing fresh cheese with the desired quality parameters. The study focuses on kombucha fresh cheese samples fortified with ground wild thyme, supercritical fluid extract of wild thyme, ground sage and supercritical fluid extract of sage and optimizes the parameters of chemical composition, antioxidant potential and microbiological profile. The ANN models demonstrate robust generalization capabilities and accurately predict the observed results based on the input parameters. The optimal neural network model (MLP 6-10-16) with 10 neurons provides high r2 values (0.993 for training, 0.992 for testing, and 0.992 for validation cycles). The ANN model identified the optimal sample, a supercritical fluid extract of sage, on the 20th day of storage, showcasing specific favorable process parameters. These parameters encompass dry matter, fat, ash, proteins, water activity, pH, antioxidant potential (TP, DPPH, ABTS, FRAP), and microbiological profile. These findings offer valuable insights into producing fresh cheese efficiently with the desired quality attributes. Moreover, they highlight the effectiveness of the ANN model in optimizing diverse parameters for enhanced product development in the dairy industry.

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Section: The Accuracy Of the Modelmentioning

confidence: 99%

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

Lončar,

Pezo,

Iličić

et al. 2024

Foods

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“…The observed changes in the compositions of microbial genera in response to drought and rehydration likely represented microbial shifts in the whole community [45]. A few studies have also employed machine learning methods for the identification of featured microbial taxa in response to drought [18,46]. The employment of machine learning approaches increased the efficiency in handling high-dimensional microbial data [46].…”

Section: Specific Microbial Taxa In Response To Water Availabilitymentioning

confidence: 99%

“…A few studies have also employed machine learning methods for the identification of featured microbial taxa in response to drought [18,46]. The employment of machine learning approaches increased the efficiency in handling high-dimensional microbial data [46]. However, one drawback of this study was the lack of follow-up studies validating the roles of the identified microbial taxa in response to drought.…”

Section: Specific Microbial Taxa In Response To Water Availabilitymentioning

confidence: 99%

Coordination of Root Traits and Rhizosphere Microbial Community in Tea (Camellia sinensis L.) Plants under Drought and Rehydration

Zhang,

Han,

Zhu

et al. 2023

Forests

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Soil drought and rehydration have an immense impact on plant physiology and productivity, whereas the response of plant–microbe interactions to varied water availability remains largely elusive. In this study, two tea (Camellia sinensis L.) cultivars, Longjing43 and Yingshuang, were subjected to drought followed by rehydration. Soil drought significantly induced the elongation of taproots in the Yingshuang cultivar after two weeks of drought. Moreover, the four-week drought significantly reduced the root dry mass and root nitrogen, phosphorus, and potassium concentrations in both tea cultivars. Two-week rehydration recovered the root potassium concentration in the two tea cultivars, revealing the rapid response of root potassium levels to water conditions. Drought and rehydration also resulted in shifts in rhizosphere microbial diversity. A four-week drought reduced microbial alpha diversity in Longjing43 but not in the Yingshuang cultivar, and rehydration was effective in restoring alpha diversity in Longjing43. The rhizosphere microbial community tended to recover to the initial stages after rehydration in Longjing43 but not in the other cultivar. In addition, 18 microbial genera were identified as the featured microbial taxa in response to varied water availability, and a rare genus Ignavibacterium was significantly increased in the Longjing43 cultivar by rehydration after a four-week drought. Furthermore, root nitrogen, phosphorus, potassium levels, and dry mass were positively correlated with the microbial alpha diversity, while the taproot length was negatively correlated, suggesting the crucial role of plant–microbe interactions in response to drought and rehydration. Moreover, the root phosphorus concentration and taproot length also had significant effects on microbial beta diversity, further confirming their effects on the community structure of the rhizosphere microbiome. Overall, this study provides insights into the effects of drought on plant–microbe interactions in the rhizosphere of tea plants. These findings are important for harnessing the roles of the tea rhizosphere microbiome under drought.

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“…Thereby, they aid in the development of more effective soil management strategies, including irrigation practices and the selection of drought-tolerant crops. Marker taxa hold significant potential for application in Synthetic Communities (SynComs), providing an innovative approach for early intervention under challenging environmental conditions, like droughts, to enhance plant resilience and growth [ 10 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning decodes soil microbiome’s response to drought stress

Hagen,

Dass,

Westhues

et al. 2024

Environmental Microbiome

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Background Extreme weather events induced by climate change, particularly droughts, have detrimental consequences for crop yields and food security. Concurrently, these conditions provoke substantial changes in the soil bacterial microbiota and affect plant health. Early recognition of soil affected by drought enables farmers to implement appropriate agricultural management practices. In this context, interpretable machine learning holds immense potential for drought stress classification of soil based on marker taxa. Results This study demonstrates that the 16S rRNA-based metagenomic approach of Differential Abundance Analysis methods and machine learning-based Shapley Additive Explanation values provide similar information. They exhibit their potential as complementary approaches for identifying marker taxa and investigating their enrichment or depletion under drought stress in grass lineages. Additionally, the Random Forest Classifier trained on a diverse range of relative abundance data from the soil bacterial micobiome of various plant species achieves a high accuracy of 92.3 % at the genus rank for drought stress prediction. It demonstrates its generalization capacity for the lineages tested. Conclusions In the detection of drought stress in soil bacterial microbiota, this study emphasizes the potential of an optimized and generalized location-based ML classifier. By identifying marker taxa, this approach holds promising implications for microbe-assisted plant breeding programs and contributes to the development of sustainable agriculture practices. These findings are crucial for preserving global food security in the face of climate change.

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Machine Learning Approaches for Forecasting the Best Microbial Strains to Alleviate Drought Impact in Agriculture

Cited by 9 publications

References 65 publications

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

Modeling and Optimization of Herb-Fortified Fresh Kombucha Cheese: An Artificial Neural Network Approach for Enhancing Quality Characteristics

Coordination of Root Traits and Rhizosphere Microbial Community in Tea (Camellia sinensis L.) Plants under Drought and Rehydration

Interpretable machine learning decodes soil microbiome’s response to drought stress

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