Crop Optimization and Disease Detection using Satellite Imagery & Artificial Intelligence

被引：1

作者：

Chinnasamy, P. ^{[1
]}

Tejaswini, D. ^{[2
]}

Ayyasamy, Ramesh Kumar ^{[3
]}

Dhanasekaran, S. ^{[4
]}

Kumar, B. Santhosh ^{[5
]}

Chandran, Likha ^{[6
]}

机构：

[1] Kalsalingam Acad Res & Educ, Sch Comp, Dept Comp Sci & Engn, Srivilliputtur, Tamil Nadu, India

[2] MLR Inst Technol, Dept Comp Sci & Engn, Hyderabad, India

[3] Univ Tunku Abdul Rahman UTAR, Fac Informat & Commun Technol, Kampar, Malaysia

[4] Kalasalingam Acad Res & Educ Deemed Be Univ, Dept Informat Technol, Srivilliputtur, Tamil Nadu, India

[5] Guru Nanak Inst Technol, Dept Comp Sci & Engn, Ibrahimpatnam 501506, Telangana, India

[6] VNR Vignana Jyothi Inst Engn & Technol, Dept Chem, Hyderabad, India

来源：

2024 SECOND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBER PHYSICAL SYSTEMS AND INTERNET OF THINGS, ICOICI 2024 | 2024年

关键词：

Crop Optimizations; Artificial Intelligence; Agriculture; Disease Detection; Statilite Processing;

D O I：

10.1109/ICOICI62503.2024.10696197

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Small-scale farmers are essential for global food production yet lack access to modern technology and resources. This hinders informed decision-making, leading to reduced yields and economic hardship. Our project addresses this challenge by leveraging satellite data to deliver actionable insights to farmers, empowering them to enhance productivity and make informed decisions. We propose a mobile application that integrates AI with satellite imagery to revolutionize precision agriculture. By providing real-time data on soil health, water usage, and plant disease, the app enables farmers to optimize resource utilization, mitigate risks, and ultimately improve yields. Our project bridges the technological gap for small-scale farmers, transforming traditional practices into a more sustainable and efficient model. We believe this technology holds significant potential for positive socio-economic impacts on the livelihoods of farmers worldwide.

引用

页码：1531 / 1535

页数：5

共 15 条

[1]

Anupriya E., 2023, 2023 INT C COMP COMM, P1, DOI [10.1109/ICCCI56745.2023.10128618, DOI 10.1109/ICCCI56745.2023.10128618]

[2]

Badage A., 2018, Int. Res. J. Eng. Technol, V5, P866

[3]

Chinnasamy P., Engineering, Science, and Sustainability, P236

[4]

Chinnasamy P., 2023, Deep Learning Research Applications for Natural Language Processing, P1, DOI 10.4018/978-1

[5] An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis [J].

Darwish, Ashraf ;

Ezzat, Dalia ;

Hassanien, Aboul Ella .

SWARM AND EVOLUTIONARY COMPUTATION, 2020, 52

[6]

Goswami B., 2022, Data Science in Societal Applications: Concepts and Implications, P107

[7] On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey [J].

Orchi, Houda ;

Sadik, Mohamed ;

Khaldoun, Mohammed .

AGRICULTURE-BASEL, 2022, 12 (01)

[8] Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research [J].

Ouhami, Maryam ;

Hafiane, Adel ;

Es-Saady, Youssef ;

El Hajji, Mohamed ;

Canals, Raphael .

REMOTE SENSING, 2021, 13 (13)

[9] Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review [J].

Patricio, Diego Inacio ;

Rieder, Rafael .

COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 153 :69-81

[10] Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG [J].

Paymode, Ananda S. ;

Malode, Vandana B. .

ARTIFICIAL INTELLIGENCE IN AGRICULTURE, 2022, 6 :23-33

← 1 2 →