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通过基于云的变革性作物推荐模型提升精准农业水平。

Enhancing precision agriculture through cloud based transformative crop recommendation model.

作者信息

Singh Gurpreet, Sharma Sandeep

机构信息

Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, Punjab, India.

出版信息

Sci Rep. 2025 Mar 17;15(1):9138. doi: 10.1038/s41598-025-93417-3.

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.

摘要

现代农业更多地依赖技术来提高粮食产量。其目标是提高粮食的质量和数量。本文介绍了一种新颖的TCRM(变革性作物推荐模型)。它使用先进的机器学习和云平台来提供个性化的作物推荐。与传统方法不同,TCRM使用实时数据。它包括环境和农艺因素以优化推荐。该系统为偏远地区的农民提供短信提醒。它优于逻辑回归、KNN(k近邻)和AdaBoost等基线算法。TCRM为农民提供可行的见解,减少资源浪费,同时提高产量。通过提供特定地区的推荐,它提高了盈利能力,促进了可持续农业实践。该模型的准确率为94%,精确率为94.46%,召回率为94%。其F1分数为93.97%。五折交叉验证分数为97.67%。这些发现表明该模型可以改善精准农业。它可以使农业更具可持续性和效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7d09/11914076/c1a98ded6c37/41598_2025_93417_Fig1_HTML.jpg

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