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Key Elements for Plant Nutrition

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Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...

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相关实验视频

Updated: Jan 10, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

从传统的机器学习到微调大型语言模型:对基于传感器的土壤湿度预测进行审查.

Md Babul Islam^1,2, Antonio Guerrieri², Raffaele Gravina¹

¹DIMES, University of Calabria, Via P. Bucci, 87036 Rende, CS, Italy.

Sensors (Basel, Switzerland)

|November 27, 2025

概括

此摘要是机器生成的。

在智能农业中,准确地预测土壤湿度对于高效的水资源管理至关重要. 本综述提供了一个结构化的分类学,并分析了人工智能模型,强调了未来的方向,如TinyML和可解释的AI,以改善农业.

关键词:

深度学习是一种深度学习.微调的LLM法学课程混合型混合型混合型混合型机器学习是机器学习.审查审查审查审查审查审查智能农业智能农业土壤湿度预测预测

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Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

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Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

相关实验视频

Last Updated: Jan 10, 2026

In Situ Soil Moisture Sensors in Undisturbed Soils

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Published on: December 21, 2019

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

科学领域:

农业科学农业科学
计算机科学计算机科学
环境科学环境科学

背景情况:

智能农业 (SA) 集成物联网,人工智能和传感,以提高农业生产力和可持续性.
持续监测土壤湿度 (SM) 对作物生长,水资源管理和明智的灌决策至关重要.
现有的审查缺乏结构化的框架,忽视了最近的AI进步,如联合学习 (FL) 和大型语言模型 (LLM).

研究的目的:

为预测土壤水分 (SM) 提出一个新的分类学.
综合审查现有的SM预测方法,包括传统,深度学习和混合模型.
确定人工智能驱动的智能农业SM预测的新兴研究方向.

主要方法:

使用PRISMA方法的系统文献审查,分析了68项同行评审研究 (2017-2025年).
基于传感器类型,输入特征,人工智能技术,数据持续时间和评估指标的研究分类.
开发一种新的分类学来结构SM预测领域.

主要成果:

分析应用到SM预测的各种AI技术,从传统的机器学习到先进的深度学习模型.
确定影响SM预测准确性的关键因素,包括传感器数据和AI模型选择.
目前SM预测研究的趋势和差距的概述.

结论:

拟议的分类为理解和推进SM预测研究提供了一个结构化的框架.
未来的研究应该专注于TinyML用于边缘部署,可解释的AI (XAI) 为了透明度,以及对隐私意识的FL.
准确可靠的SM预测系统对于智能农业的持续发展至关重要.