Smart farming using artificial intelligence: A review

人工智能 机器学习 计算机科学 深度学习 农业 精准农业 农业工程 作物产量 农学 工程类 生态学 生物
作者
Yaganteeswarudu Akkem,Saroj Kr. Biswas,Aruna Varanasi
出处
期刊:Engineering Applications of Artificial Intelligence [Elsevier BV]
卷期号:120: 105899-105899 被引量:209
标识
DOI:10.1016/j.engappai.2023.105899
摘要

Smart farming with artificial intelligence provides an efficient solution to today’s agricultural sustainability challenges. Machine learning, Deep learning, and time series analysis are essential in smart farming. Crop selection, crop yield prediction, soil compatibility classification, water management, and many other processes are involved in agriculture. Machine learning algorithms are used for crop selection and management, Deep learning techniques are used for crop selection and forecasting crop production, and time series analysis is used for demand forecasting of crops, commodity price prediction, and crop yield production forecasting. Crops are chosen using machine learning algorithms and deep learning algorithms based on soil, soil compatibility classification, and other factors. In the agriculture industry, this article offers a thorough review of machine learning and deep learning techniques. Crop data sets can be used to classify soil fertility, crop selection, and many other aspects using machine learning algorithms. Deep learning algorithms can be applied to farming data to do time series analysis and crop selection. Because there is more need for food due to the growing population, crop production forecasting is one of the crucial tasks. Therefore, future crop production must be predicted in order to overcome food insufficiency. In this article, several time series algorithms were reviewed. Suggesting appropriate crop recommendations using machine and deep learning by estimating crop yield by using time series analysis will reduce food insufficiency in the future.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
沈静完成签到,获得积分10
1秒前
3秒前
loski发布了新的文献求助10
3秒前
marina完成签到 ,获得积分20
4秒前
7秒前
时尚俊驰发布了新的文献求助10
7秒前
8秒前
文献发布了新的文献求助30
9秒前
12秒前
我嘞个豆完成签到,获得积分10
12秒前
爱笑晓曼发布了新的文献求助10
13秒前
wdy111应助sc采纳,获得20
13秒前
敏感初露发布了新的文献求助10
13秒前
隐形曼青应助机智思真采纳,获得10
16秒前
思源应助时尚俊驰采纳,获得10
16秒前
可爱的函函应助敏感初露采纳,获得10
16秒前
17秒前
爆米花应助橙子采纳,获得10
20秒前
量子星尘发布了新的文献求助10
22秒前
阿满完成签到 ,获得积分10
23秒前
王馨雨完成签到,获得积分10
24秒前
在水一方应助袁涛采纳,获得10
24秒前
爱笑晓曼完成签到,获得积分10
27秒前
28秒前
29秒前
nuoran发布了新的文献求助10
30秒前
30秒前
乐乐宝完成签到,获得积分10
31秒前
32秒前
彭于晏应助阿钉采纳,获得10
33秒前
孙燕应助阿钉采纳,获得10
33秒前
整齐小松鼠应助阿钉采纳,获得10
33秒前
jszhoucl发布了新的文献求助10
34秒前
一定行发布了新的文献求助10
34秒前
jxlu发布了新的文献求助10
35秒前
37秒前
橙子发布了新的文献求助10
37秒前
科研通AI5应助科研通管家采纳,获得10
38秒前
李健应助科研通管家采纳,获得10
38秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989378
求助须知:如何正确求助?哪些是违规求助? 3531442
关于积分的说明 11254002
捐赠科研通 3270126
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173