Librería que hace análisis estadístico de datos de humedad y temperatura de un cultivo hidropónico. (media, desviación estándar, y cálculo de veces dentro de rango de condiciones óptimas para el cultivo)

IOT Based Smart Agriculture System

The project is about smart agriculture and is a system that allows people to monitor the status of their cultivated areas by automatically managing irrigation. The network consists of a border router, an undefined number of scattered nodes throughout the lands and a central server (the Master) that coordinates and manages all nodes.

Remote Control, Monitoring and Data collection System for Agricultural lands. Using telegram bots and AWS cloud services.

PlantPulse helps users monitor their crop fields in real-time using IoT, and recommending the most suitable crops using an ML model. 🚜🌾💧

lightWay - choose the right way × 1st Place @ PoliHack 2021 (Juniors)

Academic application ReactNative | Firebase

And IoT project on Smart Agricolture

Embrace limited and imperfect training datasets in plant disease recognition using deep learning.

Official code of the paper "Deep-Wide Learning Assistance for Insect Pest Classification"

Docker environment for analyzing Argentina agriculture production data.

Este projeto implementa um sistema de irrigação automática utilizando um ESP32 para monitorar a umidade do solo e controlar um relé, garantindo a eficiência no uso da água. Os dados coletados pelos sensores são exibidos em um dashboard desenvolvido com Flask, permitindo o monitoramento remoto das condições do solo e do ambiente.

A smart irrigation control system prototype that optimises water usage based on real-time weather forecasts and sensor data.

AI-Models for Smart Irrigation System

NRF52 BLE Agriculture example

The open source code for the paper "A Hybrid Multi-stage Model based on YOLO and Modified Inception Network for Rice Leaf Disease Analysis" Hybrid_Multi_Stage_DeepLearing_Approach

The main objective of this internship was to approach in general the topic of the use of classical Machine Learning and Deep Learning techniques for intelligent agriculture with a focus on the optimization and improvement of agricultural production.

ETa and Crop Coefficient predictions via Machine Learning models