Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Monocular Depth Estimation using a Multi-grid Attention-based Model
Volume-4 | Issue-3
Speedy Image Crowd Counting by Light Weight Convolutional Neural Network
Volume-3 | Issue-3
Construction of Efficient Smart Voting Machine with Liveness Detection Module
Volume-3 | Issue-3
An Economical Robotic Arm for Playing Chess Using Visual Servoing
Volume-2 | Issue-3
Triplet loss for Chromosome Classification
Volume-4 | Issue-1
Unstructured Noise Removal for Industrial Sensor Imaging Unit by Hybrid Adaptive Median Algorithm
Volume-3 | Issue-4
Real Time Sign Language Recognition and Speech Generation
Volume-2 | Issue-2
Analysis of Artificial Intelligence based Image Classification Techniques
Volume-2 | Issue-1
Design of ANN Based Machine Learning Method for Crop Prediction
Volume-3 | Issue-3
A REVIEW ON IOT BASED MEDICAL IMAGING TECHNOLOGY FOR HEALTHCARE APPLICATIONS
Volume-1 | Issue-1
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT
Volume-1 | Issue-1
Diabetic Retinopathy Detection Using Machine Learning
Volume-4 | Issue-1
Accurate Segmentation for Low Resolution Satellite images by Discriminative Generative Adversarial Network for Identifying Agriculture Fields
Volume-3 | Issue-4
Deep Learning based Handwriting Recognition with Adversarial Feature Deformation and Regularization
Volume-3 | Issue-4
State of Art Survey on Plant Leaf Disease Detection
Volume-4 | Issue-2
Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data
Volume-3 | Issue-4
OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry
Volume-3 | Issue-4
VIRTUAL RESTORATION OF DAMAGED ARCHEOLOGICAL ARTIFACTS OBTAINED FROM EXPEDITIONS USING 3D VISUALIZATION
Volume-1 | Issue-2
Two-Stage Frame Extraction in Video Analysis for Accurate Prediction of Object Tracking by Improved Deep Learning
Volume-3 | Issue-4
Volume - 4 | Issue - 4 | december 2022
Published
16 December, 2022
The day-to-day lives of people depend on the food consumed. Even though food is required regularly, people don’t often think of the struggle the farmers face in delivering the food to the market. There are much more criteria to be considered when it comes to the problems affecting the farmers and the fields. One of the most important criteria is the protection of farm fields. Animal intruding the field leads to crop damage, and of course some severe problems that affect the regular profit. Farm fields near mountain slopes are often intruded by wild elephants and wild pigs, that destroy most of the crops and pull down the profit as well as the investment. There are several old methods to protect the field like thorn fences, but those aren’t quite beneficial. The other problem is the classification of animal entering the field. The security features can be adapted only based on the animal that is entering. If the animal intruder is anonymous, preventive measures cannot be immediately taken. The proposed model uses a setup like fence, where cameras are mounted to capture animal movements using OpenCV python. Once any movement is detected, an alert sound goes on, so that people could be aware that some intrusion has occurred. Using image processing by CNN, classification of animal is done by training and testing the dataset. Precautions along with messages to people who could provide help can be implemented as an additional feature to this proposed work. This structure is considered beneficial to be implemented in military bases to capture movements and alert the soldiers.
KeywordsOpenCV Grayscale reading image processing CNN ANN Alert signals datasets movement capturing
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