NAGARJUNA COLLEGE OF ENGINEERING AND TECHNOLOGY

NAAC Accredited with “A+” grade

(An Autonomous College Under VTU, Belagavi)

Title of the Project: Virtual Plant Care Assistant Team No: 06

Branch: ISE Project Associate: Mrs. Sumitha B S
Name and USN: Anusha V (1NC22IS006)
Bhavya T S (1NC22IS008)
Radhika R M (1NC22IS042)
Yashwanth J A (1NC22IS402)
Abstract Results Applications
Virtual Plant Care Assistance aims to provide an automated solution •Early detection of plant diseases.
for plant health monitoring and disease detection using advanced
image processing techniques. By analyzing images of plants, this •Home gardening assistance for plant health.
system identifies healthy and unhealthy areas, offering real-time •Precision agriculture and automated monitoring.
feedback to users. This innovative approach combines computer vision
with color segmentation and feature detection, enhancing the precision •Research in plant pathology and image-based analysis.
of plant care in domestic and agricultural settings. Image Acquisition Colour Segmentation
•Integration with IoT for real-time health assessment.
Methodology
•Educational tools for botany and agricultural studies.
Image Brief Literature Review
Acquisition Recent studies highlight the use of image Objectives
processing in monitoring plant health, Feature Detection • Develop an automated system to monitor plant health.
Preprocessing
focusing on HSV color space and masking to • Utilize image processing for identifying healthy and diseased
detect changes in leaf color, such as green plant regions.
(healthy) and brown (diseased) areas. • Compute accuracy metrics for detection performance.
Colour Machine learning, particularly CNNs, has • Facilitate early intervention to improve agricultural productivity.
Segmentation
improved plant disease detection but requires
extensive datasets. IoT-based systems Conclusion
Feature Disease Result Final Mask
monitor environmental factors but often lack The Virtual Plant Care Assistance system demonstrates a practical
Detection
visual analysis integration. approach to automating plant health monitoring using advanced
Accuracy
This project simplifies these approaches by image processing techniques. By providing accurate and
Calculation using color segmentation and key point actionable feedback, this system bridges the gap between
detection for accessible and efficient plant technology and plant care, offering benefits to both domestic users
Results & care. and agricultural professionals.
Feedback Final Result