Fuel Sales Forecasting with SARIMA-GARCH and Rolling Window
Volume-5 | Issue-3

An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3

A Comprehensive Review on Advanced Driver Assistance System
Volume-4 | Issue-2

Nepali Image Captioning: Generating Coherent Paragraph-Length Descriptions Using Transformer
Volume-6 | Issue-1

A Novel Approach based on PSO and Coloured Petri Net for improving Services in the Emergency Department
Volume-5 | Issue-1

Credit Risk Analysis using Explainable Artificial Intelligence
Volume-6 | Issue-3

Implications of Tokenizers in BERT Model for Low-Resource Indian Language
Volume-4 | Issue-4

Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3

Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3

Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3

An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4

An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3

Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3

Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4

Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4

Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4

Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4

Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4

Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4

Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3

Home / Archives / Volume-6 / Issue-3 / Article-7

Volume - 6 | Issue - 3 | september 2024

Crop Pest Detection using Convolutional Neural Network Open Access
Devika T  , Santhiyakumari N, Nagaraj J, Arun S K, Sam Sundhar T, Siva Sakthi K  131
Pages: 314-323
Cite this article
T, Devika, Santhiyakumari N, Nagaraj J, Arun S K, Sam Sundhar T, and Siva Sakthi K. "Crop Pest Detection using Convolutional Neural Network." Journal of Soft Computing Paradigm 6, no. 3 (2024): 314-323
Published
05 October, 2024
Abstract

Pests in plants can cause significant losses in agricultural production. As a result, various technologies are used nowadays to improve agriculture's efficiency and make it more sustainable. This research highlights the contribution of machine learning algorithms and image recognition technologies for pest identification. Farmers can use the system to recognize pests and take the necessary actions to reduce them. Convolutional Neural Networks (CNN) is used in this study for image recognition tasks, including pest identification in agricultural fields. The algorithm is trained using the Agricultural Pests Dataset acquired from Kaggle. The experiment results showed that the CNN performed better than the other state-of-the-art machine learning models, with a much lower false rejection rate of 0.12% and an accuracy of 99%.

Keywords

Machine learning algorithm Crop Growth Pest Detection CNN Image processing

×

Currently, subscription is the only source of revenue. The subscription resource covers the operating expenses such as web presence, online version, pre-press preparations, and staff wages.

To access the full PDF, please complete the payment process.

Subscription Details

Category Fee
Article Access Charge
15 USD
Open Access Fee Nil
Annual Subscription Fee
200 USD
After payment,
please send an email to irojournals.contact@gmail.com / journals@iroglobal.com requesting article access.
Subscription form: click here