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

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

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.

Code used in the project "Leveraging data assimilation and monitoring data for improvement of crop growth estimates in protected environments". It includes tomato models implementations, and scripts for data preparation, model calibration, assimilation, sensitivity analysis, visualization strategies and other assessments performed.

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

ETa and Crop Coefficient predictions via Machine Learning models

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.

A collection of literature on the use of association rule mining methods in smart agriculture

AI-Models for Smart Irrigation System

And IoT project on Smart Agricolture

Smart Farming research papers collection

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

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.

MicroPython MQTT & code example for Eduponics mini ESP32 learning kit

IOT Based Smart Agriculture System

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

Prediction of the harvest using satellite images

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