There are 8 repositories under smart-farming topic.
AgIsoStack++ is the completely free open-source C++ ISOBUS library for everyone
Farmassist is a smart farming app for IoT and AI-powered plant disease detection. It is built with Flutter and uses Firebase as its backend.
π±πΎπ Smart Farm (Iceburg) is a smart project that allows to interact and improve the management of agricultural farms.
A Rust implementation of the ISO11783 (ISOBUS) & J1939 protocols
AN IOT DEVICE CONNECTED WITH ANDROID APP FOR SMART IRRIGATION
π¨π»βπΎ An Expert System for smart farming which provides the farmers with best solutions and hardware matching their needs exactly, with the ability to monitor and control the hardware remotely through the website UI in real-time.
A Smart-Farming solution for farmers to ease the process of farming with the help of IOT and ML . It provides the farmers a way to monitor their farms with IOT smart solutions and early plants disease detection through ML.
An (incomplete) implementation of the ISOBUS standard in Python
A curated collection of 45 high-quality RGB image datasets for computer vision in agriculture. Features datasets for weed detection, disease identification, and crop monitoring, focusing on natural field scenes. Part of our GIL 2025 survey paper.
An Arduino-based soil moisture monitoring system with real-time OLED display and alert mechanisms using a LED and buzzer
Smart Farming research papers collection
AI-based image recognition and soil parameter monitoring to precisely determine growth stage of plants and automate irrigation.
Monitoring Plant Health and Diseases for Sustainable Agriculture using CNN
Doctor Plant | Smart Farming Application | Daily Market Prices | Weather | IoT Based
control system which can monitor and improve the conditions of temperature, humidity and light intensity for a given particular plant
Smart farm project
A modular software architecture for Automatic Plant Phenotyping
Academic Course Project for Application Development Lab (CSE-2216), University of Dhaka
The project aims to create a centralized database of farmers with digital profiles that include all their relevant details. Additionally, the system features an alert system for new schemes and subsidies, as well as a machine learning-based crop recommendation system and disease detection with fertilizer suggestions.
This repository contains the source code associated with the pilot tech radar for the warm-up DBDD group project.
helyOS Core is a microservice and assignment orchestrator developed by the Fraunhofer IVI.
This ΡrojΠ΅ct comΡrisΠ΅d of dΠ΅vΠ΅loΡing of Π° smart HydroΡonic Π od which cΠ°n bΠ΅ usΠ΅d for growing thΠ΅ vΠ΅gΠ΅tΠ°blΠ΅s Π°nd fruits.
Mini rover basado en ESP32 S3 impulsado por dos servos de rotaciΓ³n continua que envΓa los datos de un BME280 y recibe Γ³rdenes de control por MQTT desde NodeRED Dashboard. Cuenta, ademΓ‘s, con panel OLED, bocina y un LED.
PlantPulse helps users monitor their crop fields in real-time using IoT, and recommending the most suitable crops using an ML model. ππΎπ§
Improved weed segmentation in UAV imagery of sorghum fields with a combined deblurring segmentation model
Smart application predicts the exact irrigation water amount
CultivUA empowers urban residents to grow plants sustainably with IoT sensors, real-time dashboards, and a versatile online store. Built with Angular, Laravel, and Arduino, it offers personalized care tips, plant identification, and automated reminders to redefine urban agriculture. π±
The Smart Plant Monitoring System is an IoT project that automates plant care using an ESP32 microcontroller. It monitors soil moisture, temperature, and humidity, automating watering based on sensor data. The system connects to the Blynk app for remote monitoring and control, making it perfect for smart agriculture or home automation.
Python package that facilitates the connection to helyOS core via RabbitMQ.
'CNN_Sorghum_Weed_Classifier' is an artificial intelligence (AI) based software that can differentiate a sorghum sampling image from its associated weeds images.
This project utilizes IoT sensors and an Deep Neural Network (DNN) to analyze real-time soil and environmental conditions, providing accurate crop recommendations for farmers.
This is a flutter firebase application for smart farming this would be a simple version of farmassist
Code repository for Universal Digital Agriculture Watering Assistant (UDAWA) Smart System application interface.
Implementing an smart farming system design to control tempreture, light, humidity, object nearby detection