Precision farming for sustainability: An agricultural intelligence model

IF 7.7 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Computers and Electronics in Agriculture Pub Date : 2024-09-06 DOI:10.1016/j.compag.2024.109386
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Abstract

Digital cultivation is emerging as one of the most promising fields that helped in creating an ecosystem for smart farming. Precision farming, modernized techniques, and creating smart agriculture supply chains are the need of the hour for high-quality yield. Artificial Intelligence (AI) helps create a framework and plays an important role in making decisions by analysing various data points. There are countries where more than 70% of the population depends on agriculture for their living, technological advancements help to improve crop yields and get better farming results through sustainable ways. Each stage in agriculture, starting from preparation of land, crop selection, type of fertilizer to use, to the kind of watering needed for the crops; can be monitored and regulated by technological advancement. Farmers can also make decisions and implement the best practices in their field by using AI and allied technologies. Disruptive technologies such as blockchain, the Internet of Things, remote sensing, imaging technologies, and drones can transform the primitive way of agriculture. Market analysis and user demands can also be foreseen, which helps farmers to get better yields. Another important sector where technology can play a big role in disease control and pest management. Artificial Intelligence-based farming creates high productivity and better yield, increasing individual farmers’ profit. In this study, the authors would like to throw light on AI and allied technologies, which can make agricultural productivity increase significantly. In a post-pandemic situation, high-yield and more productive farming will have a major impact. An agricultural intelligence framework model for self-sustained farming is proposed in this work. The proposed framework will help achieve self-sustained growth with increased economic stability. An end-to-end supply chain ensures customers are provided with quality products and farmers are not financially looted. Technology-driven farming will also push the next generation to take up agricultural jobs. The various advancements and strategies we propose in this study aim to build a better ecosystem for transforming Artificial Intelligence into agricultural intelligence.

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精准农业促进可持续发展:农业智能模型
数字种植正在成为最有前途的领域之一,有助于创建智能农业生态系统。精准农业、现代化技术和创建智能农业供应链是实现高产的当务之急。人工智能(AI)有助于创建一个框架,并通过分析各种数据点在决策中发挥重要作用。在一些国家,70% 以上的人口以农业为生,技术进步有助于提高作物产量,并通过可持续的方式获得更好的农业成果。农业的每个阶段,从整地、作物选择、肥料种类到作物所需的浇水,都可以通过技术进步进行监测和调节。农民还可以利用人工智能和相关技术做出决策,并在自己的田地里实施最佳做法。区块链、物联网、遥感、成像技术和无人机等颠覆性技术可以改变原始的农业生产方式。还可以预见市场分析和用户需求,帮助农民提高产量。在另一个重要领域,技术可以在疾病控制和病虫害管理方面发挥巨大作用。以人工智能为基础的农业可以创造更高的生产力和更好的产量,增加农民的个人收益。在本研究中,作者希望介绍人工智能和相关技术,这些技术可以显著提高农业生产率。在大流行后的形势下,高产和更高产的农业将产生重大影响。本作品提出了一个用于自我维持农业的农业智能框架模型。拟议的框架将有助于实现自我持续增长,提高经济稳定性。端到端供应链可确保为客户提供优质产品,农民不会受到经济掠夺。技术驱动型农业还将推动下一代从事农业工作。我们在本研究中提出的各种进步和战略旨在建立一个更好的生态系统,将人工智能转化为农业智能。
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来源期刊
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture 工程技术-计算机:跨学科应用
CiteScore
15.30
自引率
14.50%
发文量
800
审稿时长
62 days
期刊介绍: Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.
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