欧盟农场的肥料成本预测:通过人工神经网络的机器学习方法

IF 1.8 Q2 AGRICULTURE, MULTIDISCIPLINARY Open Agriculture Pub Date : 2023-01-01 DOI:10.1515/opag-2022-0191
V. Martinho
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引用次数: 0

摘要

摘要机器学习方法是人工智能方法的一部分,在不同的科学领域和人类生活维度中有多种应用。这些技术出现在数字转型的框架中,智能技术带来了相关贡献,例如提高了经济部门的效率。这对农业等部门应对气候变化带来的挑战尤为重要。另一方面,考虑到相关算法的复杂性,机器学习方法不容易实现。考虑到这一点,本研究的主要目的是通过人工神经网络分析,提出一个预测欧盟农场化肥成本的模型。在当前情况下,这一评估可能会为农民和决策者提供相关信息,其中关注的是确定减轻环境影响的战略,包括农业部门和各自使用化学资源的战略。为了实现这些目标,考虑了2018-2020年期间来自农场会计数据网络的欧盟农业地区统计信息。所获得的结果显示,相对误差在0.040到0.074之间(显示出良好的准确性),以及农业总利用面积和总产量对预测化肥成本的重要性。
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Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
Abstract Machine-learning methodologies are part of the artificial intelligence approaches with several applications in different fields of science and dimensions of human life. These techniques appear in the frameworks of the digital transition, where smart technologies bring relevant contributions, such as improving the efficiency of the economic sectors. This is particularly important for sectors such as agriculture to deal with the challenges created in the context of climate changes. On the other hand, machine-learning approaches are not easy to implement, considering the complexity of the algorithms associated. Taking this into account, the main objective of this research is to present a model to predict fertiliser costs in the European Union (EU) farms through artificial neural network analysis. This assessment may provide relevant information for farmers and policymakers in the current scenario where the concerns are to identify strategies to mitigate the environmental impacts, including those from the agricultural sector and the respective use of chemical resources. To achieve these objectives, statistical information for the EU agricultural regions from the Farm Accountancy Data Network was considered for the period 2018–2020. The findings obtained show relative errors between 0.040 and 0.074 (showing good accuracy) and the importance of the total utilised agricultural area and the total output to predict the fertiliser costs.
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来源期刊
Open Agriculture
Open Agriculture AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
3.80
自引率
4.30%
发文量
61
审稿时长
9 weeks
期刊介绍: Open Agriculture is an open access journal that publishes original articles reflecting the latest achievements on agro-ecology, soil science, plant science, horticulture, forestry, wood technology, zootechnics and veterinary medicine, entomology, aquaculture, hydrology, food science, agricultural economics, agricultural engineering, climate-based agriculture, amelioration, social sciences in agriculuture, smart farming technologies, farm management.
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