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首页机器学习与大数据驱动的作物产量预测策略:一项实证研究
机器学习与大数据驱动的作物产量预测策略:一项实证研究
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更新于2023-04-29
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本文主要探讨了"使用机器学习和大数据技术预测作物产量的方法"这一关键主题,针对农业领域面临的问题,如水资源短缺、成本控制难度增加以及气候变化带来的生产不确定性,提出了智能化农业解决方案。在这些背景下,机器学习作为一种强大的工具,通过预测、分类、回归和聚类等多种算法来提高作物产量的预测准确性。 文章列举了人工神经网络、支持向量机、线性回归、逻辑回归和决策树等常见机器学习算法,它们在农业生产中的应用旨在帮助农民根据历史数据和环境因素做出更明智的决策。然而,选择适合特定作物的算法是一个挑战,因为不同的算法可能对不同类型的作物数据表现出不同的效果。 作者重点介绍了一种基于大数据计算范式的方法,这种方法将机器学习与大数据结合起来,通过数据挖掘和分析,提取出影响作物产量的关键因素,如土壤质量、气候数据、灌溉管理等。在这个过程中,数据的规范化和预处理是必不可少的,例如,l0范数、l1范数和l2数据 fidelity term 用于确保数据的质量和一致性,而正则化函数和总变分方法则有助于减少模型复杂度,防止过拟合。 研究论文引用了国际计算机工程与技术杂志(IJCET)发表的文章,作者Kodimalar Palanivel和Chellammal Surianarayanan来自印度Bharathidasan大学的计算机科学系,他们作为通讯作者,表明这项工作不仅理论上有深度,还结合了实际应用。论文的发布日期为2019年5月至6月,具有较高的期刊影响因子,反映了其学术价值和影响力。 总结来说,这篇研究论文为农业领域的实践者提供了宝贵的指导,即如何利用机器学习和大数据技术来预测和优化作物产量,以应对现代农业面临的诸多挑战,从而促进农业生产的可持续发展。
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http://www.iaeme.com/IJCET/index.asp 110 editor@iaeme.com
International Journal of Computer Engineering and Technology (IJCET)
Volume 10, Issue 03, May-June 2019, pp. 110-118, Article ID: IJCET_10_03_013
Available online at http://www.iaeme.com/ijcet/issues.asp?JType=IJCET&VType=10&IType=3
Journal Impact Factor (2019): 10.5167 (Calculated by GISI) www.jifactor.com
ISSN Print: 0976-6367 and ISSN Online: 0976–6375
© IAEME Publication
AN APPROACH FOR PREDICTION OF CROP
YIELD USING MACHINE LEARNING AND BIG
DATA TECHNIQUES
Kodimalar Palanivel
Department of Computer Science,
Bharathidasan University Constituent Arts & Science College,
Navalurkuttapattu, Tiruchirappalli, TamilNadu, India
*
Chellammal Surianarayanan
Department of Computer Science,
Bharathidasan University Constituent Arts & Science College,
Navalurkuttapattu, Tiruchirappalli, TamilNadu, India
*Corresponding Author
ABSTRACT
Agriculture is the primary source of livelihood which forms the backbone of our country. Current
challenges of water shortages, uncontrolled cost due to demand-supply, and weather
uncertainty necessitate farmers to be equipped with smart farming. In particular, low
yield of crops due to uncertain climatic changes, poor irrigation facilities, reduction
in soil fertility and traditional farming techniques need to be addressed. Machine
learning is one such technique employed to predict crop yield in agriculture. Various machine learning
techniques such as prediction, classification, regression and clustering are utilized to forecast crop
yield. Artificial neural networks, support vector machines, linear and logistic regression, decision
trees, Naïve Bayes are some of the algorithms used to implement prediction. However, the selection of
the appropriate algorithm from the pool of available algorithms imposes challenge to the researchers
with respect to the chosen crop. In this paper, an investigation has been performed on how various
machine learning algorithms are useful in prediction of crop yield. An approach has been proposed for
prediction of crop yield using machine learning techniques in big data computing paradigm.
Key words: ISTA, IISTA, image restoration, inverse problems, l
0
norm, l
1
norm, l
2
data fidelity term, regularization function, total variation.
Cite this Article: Kodimalar Palanivel and Chellammal Surianarayanan, An
Approach for Prediction of Crop Yield Using Machine Learning and Big Data
Techniques, International Journal of Computer Engineering and Technology 10(3),
2019, pp. 110-118.
http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=10&IType=3
Electronic copy available at: https://ssrn.com/abstract=3555087
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