土壤定点养分管理的精准农业技术:综述

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Niharika Vullaganti, Billy G. Ram, Xin Sun
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引用次数: 0

摘要

在不断增长的人口不断增长的粮食需求中,农业集约化往往依赖于过度的化学和化肥施用。虽然这种方法最初能提高作物产量,但由于土壤退化和食品质量受损,影响了长期的可持续性。因此,在提高作物生产的同时优先考虑土壤健康,对可持续粮食生产至关重要。定点养分管理(SSNM)是提高作物产量、保持土壤健康和减少环境污染的关键策略。尽管具有潜力,但由于现有的研究差距,SSNM技术在农民领域的应用仍然有限。本文对过去11年(2013-2024)在SSNM领域的研究进行了批判性的分析和介绍,指出了差距和未来的研究方向。一项对97份相关研究出版物的综合研究揭示了以下几个关键发现:a)电化学传感和光谱是SSNM研究中被广泛探索的两个领域;b)尽管SSNM中有许多技术,但每种技术都有自己的局限性,阻止任何一种技术都是理想的;c)模型和预处理技术的选择显著影响养分预测的准确性;d)没有单一传感器或传感器组合可以预测所有的土壤性质,因为适用性是高度属性特异性的。本文旨在为精准农业研究人员、技术人员和农民提供关于SSNM研究、实施、局限性、挑战和未来研究方向的详细见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Precision agriculture technologies for soil site-specific nutrient management: A comprehensive review
Amidst the growing food demands of an increasing population, agricultural intensification frequently depends on excessive chemical and fertilizer applications. While this approach initially boosts crop yields, it effects long-term sustainability through soil degradation and compromised food quality. Thus, prioritizing soil health while enhancing crop production is essential for sustainable food production. Site-Specific Nutrient Management (SSNM) emerges as a critical strategy to increase crop production, maintain soil health, and reduce environmental pollution. Despite its potential, the application of SSNM technologies remain limited in farmers' fields due to existing research gaps. This review critically analyzes and presents research conducted in SSNM in the past 11 years (2013–2024), identifying gaps and future research directions. A comprehensive study of 97 relevant research publications reveals several key findings: a) Electrochemical sensing and spectroscopy are the two widely explored areas in SSNM research, b) Despite numerous technologies in SSNM, each has its own limitation, preventing any single technology from being ideal, c) The selection of models and preprocessing techniques significantly impacts nutrient prediction accuracy, d) No single sensor or sensor combination can predict all soil properties, as suitability is highly attribute-specific. This review provides researchers, and technical personnel in precision agriculture, and farmers with detailed insights into SSNM research, its implementation, limitations, challenges, and future research directions.
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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