This project is an AI-powered plant disease prediction tool utilizing Convolutional Neural Networks (CNN). It is specialized for identifying diseases in maize, potato, tomato, and rice crops, helping farmers and agricultural professionals detect and manage crop diseases early.

Mission Manager (middleware) developed for the AFarCloud EU Research Project

OpenCV script to spot plants affected by Fusarium wilt.

Gui for select image pixel reference for iTree3DMap

C++ algorithm to estimate percent canopy missing of a stem tree using PCL

Open Source Sensor Node for Smart Agriculture

This project develops C++ and Python standard-language software for embedding in irrigation controllers for human-supervised fully-automated distributed systems.

A Computer Vision methdology to monitor large mammalian herbivores using thermal UAV imagery

ISOXML library for python

3D Mapping of a tree for dendrometric feature estimation

Bachelor's Thesis Project for Telekom Innovation Laboratories at ELTE University

This web application uses Machine Learning to recommend crop, fertilizer, pesticide and storage process based on various variables. Algorithm used is SVM for multi-classification

Yellow Sticky Traps Dataset with improved annotations. Based on: "Raw data from Yellow Sticky Traps with insects for training of deep learning Convolutional Neural Network for object detection" by A.T. Nieuwenhuizen et. al.

Alignment between the coordinate system of a point cloud and the global coordinate system of pcl

[IROS24] Offical Code for "FruitNeRF: A Unified Neural Radiance Field based Fruit Counting Framework" - Inegrated into Nerfstudio