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

Effective Tomato Spotted Wilt Virus Resistance Assessment Using Non-Destructive Imaging and Machine Learning

Horticulturae, Journal Year: 2025, Volume and Issue: 11(2), P. 132 - 132

Published: Jan. 26, 2025

There is a growing need to establish breed reassessment system responding tomato spotted wilt virus (TSWV) mutations. Conventional visual survey methods allow for assessing TSWV severity and disease incidence, while enzyme-linked Immunosorbent Assay (ELISA) data analysis can replace validate surveys. This study proposes non-destructive evaluation technique using an open software platform based on image processing machine learning. Many studies have evaluated resistance the TSWV. However, as strains that destroy emerge, identify new genetic resources with variants needed. Evaluation techniques images learning strength respond quickly accurately emergence of variants. viruses rely empirical judgment The accuracy training model Support Vector Machine (SVM), Logistic Regression (LR), neural networks (NNs) was excellent, in following order: NNs (0.86), LR (0.81), SVM (0.65). Meanwhile, validation good, order NN (0.84), (0.79), (0.71). NNs’ prediction performance verified through ELISA analysis, showing causal relationship between two sets R² 0.86 statistical significance. Imaging NN-based assessment technologies show significant potential key tools resource systems ensure rapid accurate response strains.

Language: Английский