Crop Sentry: AI-Enhanced Protection for Agriculture Field

Across the nation, there is a wide range of conflicts between people and animals, such as the threat posed by monkeys in urban areas, wild pigs stealing crops, birds, and other animals protecting agricultural fields. One of the biggest problems facing the world today is finding practical solutions for the conflict between humans and animals. In the suggested work, intrusion detection is implemented using a wireless sensor network-based technique. To distinguish between humans and animals, a Convolutional Neural Network (CNN), is utilized to analyze the features of the images and then the support vector machine (SVM) and other classifier methods are applied for performing classification. The approach suggested in this proposed study may detect human and animal incursion extremely in an efficient way to enhance the accuracy of detection compared to the standard manually extraction as per the findings of several experiments being conducted in the agricultural field.

Authors

Publication Details

Published
2024-12-13
DOI
https://doi.org/10.1109/mpcit62449.2024.10892761
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Crop Sentry: AI-Enhanced Protection for Agriculture Field

N. Rashmi, S. I. De los Santos G.
Smart Agriculture and AI
article

Crop Sentry: AI-Enhanced Protection for Agriculture Field

N. Rashmi, S. I. De los Santos G.
article en

Abstract

Across the nation, there is a wide range of conflicts between people and animals, such as the threat posed by monkeys in urban areas, wild pigs stealing crops, birds, and other animals protecting agricultural fields. One of the biggest problems facing the world today is finding practical solutions for the conflict between humans and animals. In the suggested work, intrusion detection is implemented using a wireless sensor network-based technique. To distinguish between humans and animals, a Convolutional Neural Network (CNN), is utilized to analyze the features of the images and then the support vector machine (SVM) and other classifier methods are applied for performing classification. The approach suggested in this proposed study may detect human and animal incursion extremely in an efficient way to enhance the accuracy of detection compared to the standard manually extraction as per the findings of several experiments being conducted in the agricultural field.

Zero hunger
Openalex Percentile: Top 84%
Smart Agriculture and AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.