Health Evaluation Robot for Basil - HERB

Overview

Human crop monitoring is inefficient due to high cost and low speed, as specially trained people must individually check each plant for problems. To address this issue, research on autonomous crop monitoring has grown exponentially in recent years. Development of each monitoring system must address three, related issues: 1). the physical means of navigating the planted area to collect information, 2). the method of detection and, 3). the method of analysis that can lead to identification of deficiency. Monitoring systems for greenhouses are especially lacking. We designed and built a robotic system tailored to the application of machine learning on nutrient deficiency detection in hydroponic basil in greenhouses. The robot successfully navigated by following testing on lines on the ground and the imaging system successfully moved, detected, captured, and sent images of QR codes from a Raspberry Pi to a computer for analysis. Initial trials of images taken manually of hydroponically grown basil produced promising results for the use of Top Projected Canopy Area (TPCA) as a measure of nutrient deficiency. This flexible design is capable of collecting imaging data for other methods and analysis, like textural features, and was designed to operate seamlessly in most greenhouse environments. The analysis methods described here serve as a proof of concept for this approach, and could eventually be expanded to a variety of crops.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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