Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding

International Journal of Molecular Sciences, Journal Year: 2022, Volume and Issue: 23(19), P. 11156 - 11156

Published: Sept. 22, 2022

Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances phenomics, enviromics, together with the other "omics" approaches are paving ways elucidating detailed complex biological mechanisms that motivate functions response to environmental trepidations. These have provided researchers precise tools evaluate important agronomic traits larger-sized germplasm at reduced time interval early growth stages. However, big data relationships within impede understanding of behind genes driving agronomic-trait formations. AI brings huge computational power many new strategies future breeding. The present review will encompass how applications technology, utilized current breeding practice, assist solve problem high-throughput phenotyping gene functional analysis, advances technologies bring opportunities make envirotyping widely Furthermore, methods, linking genotype phenotype remains massive challenge impedes optimal application field phenotyping, genomics, enviromics. In this review, we elaborate on be preferred tool increase accuracy genotyping, data; moreover, explore developing challenges multiomics computing integration. Therefore, integration can allow rapid identification eventually accelerate crop-improvement programs.

Language: Английский

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Logistics and Agri-Food: Digitization to Increase Competitive Advantage and Sustainability. Literature Review and the Case of Italy

Sustainability, Journal Year: 2022, Volume and Issue: 14(2), P. 787 - 787

Published: Jan. 11, 2022

This paper examines the current challenges faced by logistics with a focus on agri-food sector. After outlining context, review of literature relationship between and strategic management in gaining increasing competitiveness sector is conducted. In particular, flow as follows: after examining aforementioned managerial problem its broader repercussions, proceeds to address two main research questions. First, how which tools can digitization contribute improving supply chain sustainability logistics? Second, what are implications consequences this for terms efficiency, effectiveness, cost reduction, optimization? Finally, presents Italy case study, chosen both peculiar internal differences logistical infrastructures entrepreneurial Northern Southern regions (which could be at least partially overcome use new technologies frameworks) importance domestic economy (accounting about 25% country’s GDP), should have positive effects value creation sustainability.

Language: Английский

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Evaluation of Random Forests (RF) for Regional and Local-Scale Wheat Yield Prediction in Southeast Australia

Sensors, Journal Year: 2022, Volume and Issue: 22(3), P. 717 - 717

Published: Jan. 18, 2022

Wheat accounts for more than 50% of Australia's total grain production. The capability to generate accurate in-season yield predictions is important across all components the agricultural value chain. literature on wheat prediction has motivated need novel works evaluating machine learning techniques such as random forests (RF) at multiple scales. This research applied a Random Forest Regression (RFR) technique build regional and local-scale models pixel level three southeast Australian wheat-growing paddocks, each located in Victoria (VIC), New South Wales (NSW) Australia (SA) using 2018 maps from data supplied by collaborating farmers. Time-series Normalized Difference Vegetation Index (NDVI) derived Planet's high spatio-temporal resolution imagery, meteorological variables were used train, test validate Python libraries (a) regional-scale three-paddock composite (b) individual paddocks. region-wide RF model paddocks performed well (R2 = 0.86, RMSE 0.18 t ha-1). VIC 0.89, 0.15 ha-1) NSW 0.87, 0.07 well, but moderate performance was seen SA 0.45, 0.25 Generally, values underpredicted low overpredicted. study demonstrated feasibility applying modeling satellite imagery yielded 'big data' prediction.

Language: Английский

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A Survey of Machine Learning for Big Data Processing

Journal on big data, Journal Year: 2022, Volume and Issue: 4(2), P. 97 - 111

Published: Jan. 1, 2022

Today's world is a data-driven one, with data being produced in vast amounts as result of the rapid growth technology that permeates every aspect our lives. New processing techniques must be developed and refined over time to gain meaningful insights from this continuous volume various forms. Machine learning technologies provide promising solutions potential methods for large quantities gaining value it. This study conducts literature review on application machine big processing. It provides general overview algorithms techniques, brief introduction data, discussion related works have used variety sectors process data. The also discusses challenges issues associated usage

Language: Английский

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Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

Frontiers in Plant Science, Journal Year: 2023, Volume and Issue: 14

Published: Aug. 10, 2023

Agriculture is the most critical sector for food supply on earth, and it also responsible supplying raw materials other industrial productions. Currently, growth in agricultural production not sufficient to keep up with growing population, which may result a shortfall world’s inhabitants. As result, increasing crucial developing nations limited land resources. It essential select suitable crop specific region increase its rate. Effective forecasting that area based historical data, including environmental cultivation areas, amount, required. However, data such are publicly available. such, this paper, we take case study of country, Bangladesh, whose economy relies agriculture. We first gather preprocess from relevant research institutions Bangladesh then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), effectively predict major crops (three different kinds rice, potato, wheat). KRR designed after investigating five existing traditional (Support Vector Regression, Naïve Bayes, Regression) (Random CatBoost) algorithms. consider four classical evaluation metrics, i.e., mean absolute error, square error (MSE), root MSE, R 2 , evaluate performance proposed over models. shows 0.009 99% Aus; 0.92 90% Aman; 0.246 Boro; 0.062 wheat; 0.016 potato prediction. The Diebold–Mariano test conducted check robustness model, KRR. In cases, 1% 5% significance compared benchmark ML Lastly, design recommender system suggests next season. believe paradigm will help farmers personnel leverage proper production.

Language: Английский

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Soybean yield prediction by machine learning and climate

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 151(3-4), P. 1709 - 1725

Published: Jan. 6, 2023

Language: Английский

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When Everything Becomes Bigger: Big Data for Big Poultry Production

Animals, Journal Year: 2023, Volume and Issue: 13(11), P. 1804 - 1804

Published: May 30, 2023

In future decades, the demand for poultry meat and eggs is predicted to considerably increase in pace with human population growth. Although this expansion clearly represents a remarkable opportunity sector, it conceals multitude of challenges. Pollution land erosion, competition limited resources between animal nutrition, welfare concerns, limitations on use growth promoters antimicrobial agents, increasing risks effects infectious diseases zoonoses are several topics that have received attention from authorities public. The production must be achieved mainly through optimization increased efficiency. ability generate large amounts data (“big data”) pervasive both modern society farming industry. Information accessibility—coupled availability tools computational power store, share, integrate, analyze automatic flexible algorithms—offers an unprecedented develop maximize farm profitability, reduce socio-environmental impacts, health welfare. A detailed description all applications big analysis would infeasible. Therefore, present work briefly reviews application sensor technologies, such as optical, acoustic, wearable sensors, well infrared thermal imaging optical flow, farming. principles benefits advanced statistical techniques, machine learning deep learning, their developing effective reliable classification prediction models benefit system, also discussed. Finally, recent progress pathogen genome sequencing discussed, highlighting practical epidemiological tracking, reconstruction microorganisms’ dynamics, evolution, spread. objective evaluation effectiveness applied control strategies considered. human-artificial intelligence collaborations livestock sector can frightening because they require farmers employees adapt new roles, challenges, competencies—and unknowns, limitations, open-ended questions inevitable—their overall appear far greater than drawbacks. As more farms companies connect technology, artificial (AI) sensing technologies will begin play role identifying patterns solutions pressing problems farming, thus providing production-based commercial advantages. Moreover, combination diverse sources types become fundamental development predictive able anticipate, rather merely detect, disease occurrence. infrastructures, collection, storage, sharing, analysis—together open standards integration molecular epidemiology—have potential address major challenge producing higher-quality, healthful food larger scale sustainable manner, thereby protecting ecosystems, preserving natural resources, improving health.

Language: Английский

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Assessing the influence of artificial intelligence on agri-food supply chain performance: the mediating effect of distribution network efficiency

Technological Forecasting and Social Change, Journal Year: 2023, Volume and Issue: 200, P. 123149 - 123149

Published: Dec. 21, 2023

Language: Английский

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A Review of Machine Learning Techniques in Agroclimatic Studies

Agriculture, Journal Year: 2024, Volume and Issue: 14(3), P. 481 - 481

Published: March 16, 2024

The interplay of machine learning (ML) and deep (DL) within the agroclimatic domain is pivotal for addressing multifaceted challenges posed by climate change on agriculture. This paper embarks a systematic review to dissect current utilization ML DL in agricultural research, with pronounced emphasis impacts adaptation strategies. Our investigation reveals dominant reliance conventional models uncovers critical gap documentation methodologies. constrains replicability, scalability, adaptability these technologies research. In response challenges, we advocate strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies standardizes model development process but also democratizes expertise, thereby catalyzing advancement incorporation stands significantly enhance research adaptability, overall performance, ushering new era innovation practices tailored mitigate adapt change. underscores untapped potential revolutionizing propelling forward sustainable efficient solutions that are responsive evolving dynamics.

Language: Английский

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IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

Electronics, Journal Year: 2024, Volume and Issue: 13(10), P. 1894 - 1894

Published: May 11, 2024

The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production pose a threat to security. In response these challenges, the integration of IoT AI technologies emerges promising solution, facilitating data-driven decision-making, optimizing resource allocation, enhancing monitoring control systems in operations address challenges promote sustainable farming practices. This study examines intersection precision agriculture (PA), aiming provide comprehensive understanding their combined mutually reinforcing relationship. Employing systematic literature review following Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) 2020 guidelines, we explore synergies transformative potential integrating systems. also aims identify present trends, opportunities utilizing Diverse forms practices are scrutinized discern applications Through critical analysis existing literature, this contributes deeper how can revolutionize PA, resulting improved efficiency, sustainability, productivity sector.

Language: Английский

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