Year
2026
Client
Personal Project
Category
AI / Machine Learning
Product Duration
1 year
AgriAI explores how computer vision and machine learning can support agricultural monitoring. The system brings together plant-image analysis, live detection, and an agricultural decision-support workflow. The current demonstration focuses on the technical pipeline and interface rather than claiming a validated production deployment.
AgriAI combines computer vision with an agricultural monitoring workflow to explore how plant images can be analyzed for potential crop issues. The system is designed around a simple pipeline: capture or provide plant imagery, analyze the image, identify relevant conditions, and present the result through an accessible monitoring interface.
The system brings together the detection workflow and a broader agricultural dashboard. The interface provides a central place for live analysis alongside modules representing areas such as soil, weather, market information, and tasks. These modules describe the intended system scope and workflow rather than claiming a fully validated field deployment.
The Live Feed Analysis view demonstrates the system's computer-vision workflow on plant imagery. The displayed Strawberry Leaf Spot detections use bounding boxes and confidence values to communicate what the model identifies in the image. These confidence values come from a simulated/demo run and should not be interpreted as production accuracy or field validation.
