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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Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review

Forests, Journal Year: 2025, Volume and Issue: 16(3), P. 449 - 449

Published: March 2, 2025

Forests play a key role in carbon sequestration and oxygen production. They significantly contribute to peaking neutrality goals. Accurate estimation of forest stocks is essential for precise understanding the capacity ecosystems. Remote sensing technology, with its wide observational coverage, strong timeliness, low cost, stock research. However, challenges data acquisition processing include variability, signal saturation dense forests, environmental limitations. These factors hinder accurate estimation. This review summarizes current state research on from two aspects, namely remote methods, highlighting both advantages limitations various sources models. It also explores technological innovations cutting-edge field, focusing deep learning techniques, optical vegetation thickness impact forest–climate interactions Finally, discusses including issues related quality, model adaptability, stand complexity, uncertainties process. Based these challenges, paper looks ahead future trends, proposing potential breakthroughs pathways. The aim this study provide theoretical support methodological guidance researchers fields.

Language: Английский

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Climate Change in Agriculture: Impacts, Adaptation, and Mitigation

Sustainable development and biodiversity, Journal Year: 2025, Volume and Issue: unknown, P. 281 - 311

Published: Jan. 1, 2025

Language: Английский

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Hybrid CNN-LSTM Model with Custom Activation and Loss Functions for Predicting Fan Actuator States in Smart Greenhouses

AgriEngineering, Journal Year: 2025, Volume and Issue: 7(4), P. 118 - 118

Published: April 10, 2025

Smart greenhouses rely on precise environmental control to optimize crop yields and resource efficiency. In this study, we propose a novel hybrid Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) architecture predict fan actuator states based data. The model integrates CNNs for spatial feature extraction LSTMs temporal dependency modeling, enhanced by custom activation function loss tailored the problem’s characteristics. was trained evaluated comprehensive dataset containing 37,923 samples with 13 features, collected from smart greenhouse. Experimental results demonstrate superior performance of CNN-LSTM model, achieving an accuracy 0.9992, precision 0.9989, recall 0.9996, F1 score significantly outperforming traditional machine learning methods such as Random Forest Gradient Boosting, well standalone CNN LSTM architectures. high underscores model’s reliability in identifying positive states, critical greenhouse management. This study highlights importance architectures handling complex spatiotemporal data, offering potential applications beyond greenhouses, healthcare monitoring predictive maintenance. Despite strengths, limitations include computational complexity limited interpretability, necessitating future work optimization explainability. These findings establish foundation integrating deep into agricultural systems, advancing automation efficiency mechanisms.

Language: Английский

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Comparative Analysis of Machine Learning Algorithms for Optimal Land Use and Land Cover Classification: Guiding Method Selection for Resource-Limited Settings in Tiaty, Baringo County, Kenya

Journal of Geoscience and Environment Protection, Journal Year: 2025, Volume and Issue: 13(04), P. 393 - 414

Published: Jan. 1, 2025

Language: Английский

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Harnessing Machine Learning for Comparative Analysis of Nanomaterials in Agro-Environmental Applications

Journal of Condensed Matter, Journal Year: 2025, Volume and Issue: 3(02), P. 39 - 43

Published: May 4, 2025

This article explores the transformative potential of integrating nanomaterials (NM) and machine learning (ML) to address critical global challenges, particularly in agriculture sustainability climate change mitigation. By conducting a comparative analysis various their applications environmental protection, we demonstrate how ML techniques can optimize properties functionalities these materials. In agriculture, are used developing nanofertilizers, nanopesticides, nanosensors, which enhance crop yield, pest control, soil health monitoring. applications, nanofilters help mitigate change-related issues. research underscores value combining NM advance sustainable agro-environmental solutions, highlighting role interdisciplinary approaches creating smarter, more efficient technologies. leveraging advanced algorithms AI, improve specificity, sensitivity, accuracy nanomaterials, offering innovative solutions challenges such as food security conservation.

Language: Английский

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Data-Driven Farming: Harnessing Big Data for Agriculture

Published: Jan. 1, 2025

Language: Английский

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Machine-Learning-Based Frameworks for Reliable and Sustainable Crop Forecasting

Sustainability, Journal Year: 2025, Volume and Issue: 17(10), P. 4711 - 4711

Published: May 20, 2025

Fueled by scientific innovations and data-driven approaches, accurate agriculture has arisen as a transformative sector in contemporary agriculture. The present investigation provides summary of modern improvements machine-learning (ML) strategies utilized for crop prediction, accompanied performance exploration models. It examines the amalgamation sophisticated technologies, cooperative objectives, methodologies designed to address obstacles conventional study possibilities intricacies precision analyzing various models deep learning, machine ensemble reinforcement learning. Highlighting significance worldwide collaboration data-sharing activities elucidates evolving landscape farming industry indicates prospective advancements sector.

Language: Английский

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Rural Cassava Farmers’ Agro-climatic Information Needs in Osun State, Nigeria

Journal of Agricultural Extension, Journal Year: 2024, Volume and Issue: 28(3), P. 60 - 69

Published: July 25, 2024

This research assessed the rural cassava farmers’ agro-climatic information needs in Osun State, Nigeria. A multi-stage sampling procedure was used selecting 210 respondents. Data were analysed percentage and mean. Results revealed that radio/television ( = 1.56) personal experience with nature 1.41) most frequent sources of information. Many (65.2%) displayed high knowledge issues. Three-quarters (75.00%) respondents favourably disposed to Furthermore, best climate change adaptive varieties planting materials for region =4.34) appropriate timing beat adverse effects =4.19) topped list needed information.There is a significant relationship between age (r= 0.972), years formal schooling 0.073), perception 0.854) their needs. It concluded prominent Climatic mitigating measures represent farmers should be provided by both governmental non-governmental agencies through

Language: Английский

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Sowing the Seeds of Precision: Innovations in Wireless Sensor Networks for Agricultural Environmental Monitoring

International Journal on AdHoc Networking Systems, Journal Year: 2024, Volume and Issue: 14(2/3), P. 01 - 17

Published: July 29, 2024

Wireless Sensor Networks (WSNs) are used in precision agriculture to provide real-time environmental parameter monitoring that is essential crop productivity. This study looks at the most current advancements WSN technology and its application vital factors including temperature, humidity, soil moisture, light intensity agricultural contexts, with an emphasis on region of Bardhaman District, West Bengal, India. For sustainable long-term sensor network functioning this area, into several placement procedures, creative data aggregation strategies, energy-efficient protocols. To improve accuracy decision-making abilities, contemporary analytics techniques like machine learning fusion also used. The results highlight how well WSNs work District maximise sustainability discusses issues deployment, connectivity power management, suggests solutions specific region's environment. goal future utility even more, boosting resilience productivity farming operations District.

Language: Английский

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