Project-If is an automated agricultural monitoring system designed to solve the bottleneck of manual data collection in vertical and hydroponic farming. By leveraging Computer Vision and Machine Learning, the system tracks plant maturation in real-time, providing high-precision data for optimizing nutrient delivery and crop yields.
This project was developed with space-habitat agriculture in mind, where low-maintenance, high-reliability autonomous monitoring is critical for life-support systems in orbital or planetary environments.
- 📷 Real-Time Growth Tracking: Uses high-resolution camera feeds to monitor lettuce plant development without human intervention.
- 📏 Pixel-to-Metric Scaling: Implemented a custom calibration algorithm that converts raw image pixels into precise metric measurements (mm/cm).
- 📊 Nutrient Optimization: Analyzes how different water solutions and nutrient profiles impact growth rates.
- 📈 Efficiency Gain: Achieved an 85% increase in data collection efficiency compared to manual measurement techniques.
- Languages:
Python🐍 - Computer Vision:
OpenCV(Image processing, contour detection, and scaling) - Machine Learning: Automated analysis of growth-pattern variances
- Domain Focus: Vertical Farming, Hydroponics, and Space Agriculture
By quantifying growth-pattern variances across diverse hydroponic solutions, the system identified specific water conditions that accelerated lettuce maturation by 18%.
The system's architected automated plant-phenotyping enables real-time tracking for low-maintenance space-habitat agricultural systems. This is essential for environments where high-resolution image processing must be handled autonomously to optimize nutrient delivery.
Manvith Kothapalli Computer Science @ University of Washington LinkedIn | GitHub
Aspire to Inspire: Building intelligent systems to solve "unsolvable" problems—from Earth to orbit.