ArticleScientific reports2025
Enhancing precision agriculture through cloud based transformative crop recommendation model.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- GNN-ML-FRL: a graph-enhanced meta-adaptive federated learning framework for scalable pest identification and ernvironmental modeling.Scientific reports · 2026Article
- Fuel-driven hybrid deep learning model for forecasting finger millet prices.Scientific reports · 2026Article
- Towards enhancing the performance of crop prediction system for precision agriculture using feature correlation square-based nearest neighbor classifier.Scientific reports · 2026Article
- Smart crop recommendation: fusing nutrient and climate data with Krill Herd Optimization and explainable AI.Frontiers in artificial intelligence · 2026Article
- Reinforcement learning based dynamic vegetation index formulation for rice crop stress detection using satellite and mobile imagery.Scientific reports · 2025Article
- ImMLPro platform for accessible machine learning and statistical analysis in digital agriculture and beyond.Scientific reports · 2025Article
- Optimizing Stroke Risk Prediction: A Primary Dataset-Driven Ensemble Classifier With Explainable Artificial Intelligence.Health science reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Modern agriculture relies more on technology to boost food production. It aims to improve both the quality and quantity of food. This paper introduces a novel TCRM (Transformative Crop Recommendation Model). It uses advanced machine learning and cloud platforms to give personalized crop recommendations. Unlike traditional methods, TCRM uses real-time data. It includes environmental and agronomic factors to optimize recommendations. The system has SMS alerts for remote farmers. It outperforms baseline algorithms like Logistic Regression, KNN(k-nearest neighbor), and AdaBoost. TCRM empowers farmers with actionable insights, reducing resource wastage while boosting yield. By offering region-specific recommendations, it enhances profitability and promotes sustainable agricultural practices. The model has 94% accuracy, 94.46% precision, and 94% recall. Its F1 score is 93.97%. The fivefold cross-validation score is 97.67%. These findings show that the model can improve precision farming. It can make agriculture more sustainable and efficient.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.