IoT-Assisted Crop Monitoring Using Machine Learning Algorithms for Smart Farming

被引：1

作者：

Apat, Shraban Kumar ^{[1
]}

Mishra, Jyotirmaya ^{[1
]}

Raju, K. Srujan ^{[1
,2
]}

Padhy, Neelamadhab ^{[1
]}

机构：

[1] GIET Univ, Comp Sci & Engn, Sch Engn & Technol, Gunupur 765022, Odisha, India

[2] Dept Comp Sci & Engn, CMR Tech Campus, Hyderabad, Telangana, India

来源：

NEXT GENERATION OF INTERNET OF THINGS | 2023年 / 445卷

关键词：

SVM; NB; Crop monitoring; IoT-assisted sensors;

D O I：

10.1007/978-981-19-1412-6_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Agriculture expansion is critical to the economic prosperity of any country. Agriculture employs more than 60% of the Indian population, either directly or indirectly. Nowadays, monitoring the crop is the challenging task in the world. In this article, data has been collected from various sensors to propose an IoT-assisted hybrid machine learning approach for obtaining an effective crop monitoring system. Crop monitoring system here means predicting as well as detecting diseases of crops. This study is about leveraging existing data and applying regression analysis, SVM, and decision tree to predict crop diseases in diverse crops such as rice, ragi, gram, potato, and onion. Among the applied methods, SVM outperforms regression, DT methods. The training and testing accuracy of Gram has 96.29% and 95.67%, respectively.

引用

页码：1 / 11

页数：11

共 50 条

[21] Galaxy Clustering and Classification using Machine Learning Algorithms and XAI [J].

Elvitigala, Amasha ;

Navaratne, Udani ;

Rathnayake, Samadhi ;

Dissanayaka, Dr Kapila .

2024 9TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY RESEARCH, ICITR, 2024,

[22] Identifying the painter using texture features and machine learning algorithms [J].

Narag, Mark Jeremy G. ;

Soriano, Maricor N. .

PROCEEDINGS OF 2019 THE 3RD INTERNATIONAL CONFERENCE ON CRYPTOGRAPHY, SECURITY AND PRIVACY (ICCSP 2019) WITH WORKSHOP 2019 THE 4TH INTERNATIONAL CONFERENCE ON MULTIMEDIA AND IMAGE PROCESSING (ICMIP 2019), 2019, :201-205

[23] A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats [J].

Alsalman, Dheyaaldin .

IEEE ACCESS, 2024, 12 :14719-14730

[24] Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends [J].

Alsharif, Mohammed H. ;

Kelechi, Anabi Hilary ;

Yahya, Khalid ;

Chaudhry, Shehzad Ashraf .

SYMMETRY-BASEL, 2020, 12 (01)

[25] Half-Farmer: A Human-Machine Augmented Learning Framework for Seed Germination Recognition in Smart Farming [J].

Huang, Yunfeng ;

Elewa, Khaled ;

Wang, Tien-Cheng ;

Chen, Tsu-Wei ;

Wu, Fang-Jing .

2022 IEEE 8TH WORLD FORUM ON INTERNET OF THINGS, WF-IOT, 2022,

[26] Detection and Classification of Banana Leaf diseases using Machine Learning and Deep Learning Algorithms [J].

Vidhya, N. P. ;

Priya, R. .

2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON, 2022,

[27] Loan Repayment Prediction Using Logistic Regression Ensemble Learning With Machine Learning Algorithms [J].

Dinh, Thuan Nguyen ;

Thanh, Binh Pham .

2022 9TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE, ISCMI, 2022, :79-85

[28] Rainfall Prediction System Using Machine Learning Fusion for Smart Cities [J].

Rahman, Atta-ur ;

Abbas, Sagheer ;

Gollapalli, Mohammed ;

Ahmed, Rashad ;

Aftab, Shabib ;

Ahmad, Munir ;

Khan, Muhammad Adnan ;

Mosavi, Amir .

SENSORS, 2022, 22 (09)

[29] Face Recognition Based Attendance System Using Machine Learning Algorithms [J].

Damale, Radhika C. ;

Pathak, Bageshree V. .

PROCEEDINGS OF THE 2018 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2018, :414-419

[30] Target detection using supervised machine learning algorithms for GPR data [J].

N. Smitha ;

Vipula Singh .

Sensing and Imaging, 2020, 21

← 1 2 3 4 5 →