Potato plant phenotyping and characterisation utilising machine learning techniques: A state-of-the-art review and current trends

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110304 - 110304

Published: April 7, 2025

Language: Английский

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Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4228 - 4228

Published: April 11, 2025

This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) data-driven insights, optimizing usage. In this study, from an initial pool 48 papers retrieved well-known databases such as Scopus Web Science, etc., a rigorous eligibility criterion was applied, narrowing focus to 11 pertinent studies. highlights major disciplines where technology is being applied: hydroponics, aquaponics, vertical farming, irrigation. Additionally, literature identifies two key sub-applications within these disciplines: simulation prediction quality soil water. also types maturity levels concepts applications. Based on their current implementation, in agriculture can be categorized into functional types: monitoring DTs, which emphasize response environmental control, enable proactive irrigation management forecasting. techniques used framework were identified based These findings underscore transformative role that play enhancing efficiency sustainability Despite technological advancements, challenges remain, including data integration, scalability, cost barriers. Further studies should conducted explore issues practical farming environments.

Language: Английский

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Prediction of fruit shapes in F1 progenies of chili peppers (Capsicum annuum) based on parental image data using elliptic Fourier analysis

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110422 - 110422

Published: April 23, 2025

Language: Английский

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A Review of Artificial Intelligence Techniques for Wheat Crop Monitoring and Management

Agronomy, Journal Year: 2025, Volume and Issue: 15(5), P. 1157 - 1157

Published: May 9, 2025

Artificial intelligence (AI) techniques, particularly machine learning and deep learning, have shown great promise in advancing wheat crop monitoring management. However, the application of AI this domain faces persistent challenges that hinder its full potential. Key limitations include high variability agricultural environments, which complicates data acquisition model generalization; scarcity limited diversity labeled datasets; substantial computational demands associated with training deploying models. Additionally, difficulties ground-truth generation, cloud contamination remote sensing imagery, coarse spatial resolution, “black-box” nature models pose significant barriers. Although strategies such as augmentation, semi-supervised crowdsourcing been explored, they are often insufficient to fully overcome these obstacles. This review provides a comprehensive synthesis recent advancements for applications, critically examines major unresolved challenges, highlights promising directions future research aimed at bridging gap between academic development real-world practices.

Language: Английский

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The Impact of Artificial Intelligence on the Role of Management Accountants

Contributions to finance and accounting, Journal Year: 2025, Volume and Issue: unknown, P. 63 - 82

Published: Jan. 1, 2025

Language: Английский

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