A Synergistic Integration of IoT, Machine Learning, and Flutter Technology for Precise Crop Management

被引：0

作者：

Chakraborty, Anirban ^{[1
]}

Bhattacharjee, Aneesh ^{[1
]}

Ghadge, Harshad ^{[1
]}

Mawley, Dev ^{[1
]}

Paulzagde, Aniket ^{[1
]}

Jaronde, Pravin ^{[1
]}

机构：

[1] GH Raisoni Coll Engn, Dept Informat Technol, Nagpur 440016, Maharashtra, India

来源：

SMART TRENDS IN COMPUTING AND COMMUNICATIONS, VOL 4, SMARTCOM 2024 | 2024年 / 948卷

关键词：

IoT; Smart agriculture; Machine learning; Crop disease detection; CNN; Flutter technology; Sensor networks; Real-time environmental monitoring; Blynk Cloud;

D O I：

10.1007/978-981-97-1329-5_24

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This research paper presents "AutoAgro," a pioneering agricultural framework amalgamating IoT, machine learning, and Flutter technology to revolutionize contemporary farming. Through a network of IoT sensors, real-time environmental parameters vital for agriculture are meticulously captured. Leveraging a sophisticated machine learning model, this data is meticulously analyzed, enabling accurate prediction and early diagnosis of plant diseases. The integration of Flutter technology in our Android interface offers an intuitive platform for farmers, featuring multilingual support and instant disease alerts, fostering proactive management strategies. The paper meticulously details the development, implementation, and performance evaluation of this holistic system, showcasing its potential to optimize crop yield, mitigate losses, and promote sustainable farming practices. Through rigorous experimentation and analysis, this study delves into the transformative impact of intelligent technologies on the agricultural landscape, providing valuable insights for researchers, practitioners, and stakeholders in the field.

引用

页码：307 / 316

页数：10

共 17 条

[1] An IoT based Smart Irrigation Management System using Reinforcement Learning modeled through a Markov Decision Process
Campoverde, Luis Miguel Samaniego
Tropea, Mauro
De Rango, Floriano
[J]. PROCEEDINGS OF THE 2021 IEEE/ACM 25TH INTERNATIONAL SYMPOSIUM ON DISTRIBUTED SIMULATION AND REAL TIME APPLICATIONS (DS-RT 2021), 2021,
[2] Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
Chiu, Mang Tik
Xu, Xingqian
Wei, Yunchao
Huang, Zilong
Schwing, Alexander G.
Brunner, Robert
Khachatrian, Hrant
Karapetyan, Hovnatan
Dozier, Ivan
Rose, Greg
Wilson, David
Tudor, Adrian
Hovakimyan, Naira
Huang, Thomas S.
Shi, Honghui
[J]. 2020 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2020, : 2825 - 2835
[3] IoT based Multi-point Pesticide Spraying Machine
Choudhary, Swapna
Kalbande, Kamlesh
Dhote, Nitin
[J]. PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2021), 2021, : 432 - 436
[4] Gaikwad M, 2022, AIP Conf Proc, V2424
[5] Approximating CNN Computation for Plant Disease Detection
Hazarika, Anakhi
Sistla, Pranav
Venkatesh, Vineet
Choudhury, Nikumani
[J]. 2022 IEEE 46TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2022), 2022, : 1117 - 1122
[6] Kalbande Kamlesh, 2021, Proceedings of the 5th International Conference on Trends in Electronics and Informatics (ICOEI 2021), P96, DOI 10.1109/ICOEI51242.2021.9452946
[7] Kalbande K, 2023, 2023 2 INT C INN TEC, P1, DOI [10.1109/INOCON57975.2023.10101109, DOI 10.1109/INOCON57975.2023.10101109]
[8] Kanhekar V, 2022, 2022 INT C EDG COMP, DOI [10.1109/icecaa55415.2022.9936366, DOI 10.1109/ICECAA55415.2022.9936366]
[9] Real Time Image Processing System for Crop Segmentation
Kawade, Sharayu Sharad
Akant, Kalyani
[J]. 2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2021,
[10] Khekare G., 2022, IoT and Cloud Computing for Societal Good, P37, DOI [10.1007/978-3-030-73885-34, DOI 10.1007/978-3-030-73885-34]

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