This repository contains a preliminary jupyter notebook and sample data to create Management Zones in a context of precision agriculture.

I worked on a precision agriculture project in which we used various sensors to save agricultural data(temperature,humidity,soil moisture,pH). In this repository , I have included 2 databases which contain the realtime and modified values. The two python codes are for modifying the database and visualizing the results respectively.

A simple console app that will take bitmaps for spectral band 3 and band 4 and generate a NDVI image

Geoinformation web application for precision agriculture

Proyek Penelitian Tugas Akhir S1 Teknik Elektro UIN SGD Bandung untuk Proyek Penelitian Capstone kolaborasi KR Sistem Instrumentasi Cerdas PRMC BRIN dengan Fakultas Pertanian Unpad yang Didanai oleh RIIM LPDP 2024

Arduino firmware for OpenVRT communication.

Second project for Big Data course held at Roma Tre University

A web app based on classification algorithm (KNN) which recommends the best suitable crop using the soil nutritional data and weather data. The model has 99.96% accuracy and it is very beneficial for farmers to make more informed decision about which crop to grow.

This repository contains all the work that had been done in ONE LAB Egypt project 'Descriptive analysis on PA using ML'.

Embedded Systems Course Project

Green Fields, Smart Yields: Precision Agriculture Empowered by LoRa Wireless Sensor Networks!

Gui for select image pixel reference for iTree3DMap

Detection models and Python scripts for insect detection on yellow sticky traps.

szenzor modalitások

The system is designed to segment crops from the background in images collected by Unmanned Aerial Vehicles.

Drip is a low-cost, efficient precision irrigation system was designed to service the needs of farmers in developing nations. It received Third Place in Embedded Systems & Fist Place in Research & Innovation at the 2018 Internal Science and Engineering Fair and Best in Fair at the 2017 Thames Valley Science Fair.

A neural network-based crop recommendation system leveraging soil and environmental data. Achieved 98% accuracy through hyperparameter tuning and evaluation of two architectures with 2 and 5 hidden layers.

Variable Rate Technology using Electron 🌾

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