IRO Journals

Journal of Artificial Intelligence and Capsule Networks

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3

Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3

Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3

Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4

QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3

Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4

Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1

Artificial Bee Colony Optimization Algorithm for Enhancing Routing in Wireless Networks
Volume-3 | Issue-1

Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4

Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3

Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1

Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3

Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4

Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4

Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4

Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4

Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis
Volume-3 | Issue-1

An Efficient Machine Learning based Model for Classification of Wearable Clothing
Volume-3 | Issue-4

Home / Archives / Volume-1 / Issue-1 / Article-2

Volume - 1 | Issue - 1 | september 2019

PEST INFESTATION IDENTIFICATION IN COCONUT TREES USING DEEP LEARNING
Dr. Abraham Chandy  242  188
Pages: 10-18
Cite this article
Chandy, D. A. (2019). PEST INFESTATION IDENTIFICATION IN COCONUT TREES USING DEEP LEARNING. Journal of Artificial Intelligence and Capsule Networks, 1(1), 10-18. doi:10.36548/jaicn.2019.1.002
Published
September, 2019
Abstract

In this paper, we propose a precision agriculture technique to detect various pests in coconut trees with the help of NVIDIA Tegra System on Chip (SoC) along with a camera interfaced drone. The drone flies across the coconut farm and captures the images and processes the data using deep learning algorithm to identify the unhealthy and pest affected trees. The deep learning algorithm uses a set of sample pest database. The Artificial Intelligence (AI) machine learning algorithm is also capable of unsupervised learning from the images that are unstructured. The data is transferred directly to the farmer's smart phone with the help of wi-fi. This helps in timely treatment of pest infected trees and to improve the yield of the trees.

Keywords

Artificial Intelligence Deep Learning System-on-chip Pest control Image processing

Full Article PDF Download Article PDF 
×

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.

Subscription Payment Details

townscript (INR / USD): click here

Subscription Fee

Annual Subscription 15,000 INR / 200 USD
Subscription form: click here