Machine Learning in Sustainable Agriculture: Systematic Review and Research Perspectives

Agriculture, Journal Year: 2025, Volume and Issue: 15(4), P. 377 - 377

Published: Feb. 11, 2025

Machine learning (ML) has revolutionized resource management in agriculture by analyzing vast amounts of data and creating precise predictive models. Precision improves agricultural productivity profitability while reducing costs environmental impact. However, ML implementation faces challenges such as managing large volumes adequate infrastructure. Despite significant advances applications sustainable agriculture, there is still a lack deep systematic understanding several areas. Challenges include integrating sources adapting models to local conditions. This research aims identify trends key players associated with use agriculture. A review was conducted using the PRISMA methodology bibliometric analysis capture relevant studies from Scopus Web Science databases. The study analyzed literature between 2007 2025, identifying 124 articles that meet criteria for certainty assessment. findings show quadratic polynomial growth publication on notable increase up 91% per year. most productive years were 2024, 2022, 2023, demonstrating growing interest field. highlights importance multiple improved decision making, soil health monitoring, interaction climate, topography, properties land crop patterns. Furthermore, evolved weather advanced technologies like Internet Things, remote sensing, smart farming. Finally, agenda need deepening expansion predominant concepts, farming, develop more detailed specialized explore new maximize benefits sustainability.

Language: Английский

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Simplifying Field Traversing Efficiency Estimation Using Machine Learning and Geometric Field Indices

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(3), P. 75 - 75

Published: March 10, 2025

Enhancing agricultural machinery field efficiency offers substantial benefits for farm management by optimizing the available resources, thereby reducing cost, maximizing productivity, and supporting sustainability. Field is influenced several unpredictable stochastic factors that are difficult to determine due inherent variability in configurations operational conditions. This study aimed simplify estimation training machine learning regression algorithms on data generated from a information system covering combination of different areas shapes, working patterns, machine-related parameters. The gradient-boosting regression-based model was most effective, achieving high mean R2 value 0.931 predicting efficiency, taking into account only basic geometric indices. developed showed also strong predictive performance indicative fields located Europe North America, considerably computational time an average 73.4% compared corresponding analytical approach. Overall, results this highlight potential simplifying prediction without requiring detailed knowledge plethora variables associated with operations. can be particularly valuable farmers who need make informed decisions about resource allocation planning.

Language: Английский

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Development of an Improved GWO Algorithm for Solving Optimal Paths in Complex Vertical Farms with Multi-Robot Multi-Tasking

Agriculture, Journal Year: 2024, Volume and Issue: 14(8), P. 1372 - 1372

Published: Aug. 15, 2024

As the global population grows, achieving Zero Hunger by 2030 presents a significant challenge. Vertical farming technology offers potential solution, making path planning of agricultural robots in vertical farms research priority. This study introduces Farming System Multi-Robot Trajectory Planning (VFSMRTP) model. To optimize this model, we propose Elitist Preservation Differential Evolution Grey Wolf Optimizer (EPDE-GWO), an enhanced version (GWO) incorporating elite preservation and differential evolution. The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle (DBO), Particle Swarm Optimization (PSO). experimental results demonstrate that reduces length 24.6%, prevents premature convergence, exhibits strong search capabilities. Thanks to DE EP strategies, requires fewer iterations reach optimal stability robustness, consistently finds solution at high frequency. These attributes are particularly context farming, where optimizing robotic essential for maximizing operational efficiency, reducing energy consumption, improving scalability operations.

Language: Английский

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Improving Efficiency in Agricultural UGVs Through Enhanced Pathfinding Techniques

Published: Sept. 20, 2024

Language: Английский

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