IoT-based Smart Farming and Crop Disease Detection

Empower modern agriculture with this IoT-enabled smart farming kit that combines real-time environmental monitoring with advanced crop disease prediction. The system uses soil moisture, temperature, humidity, and light sensors to monitor crop conditions, transmitting data continuously to the cloud. In parallel, farmers or researchers can capture leaf images of suspect plants, which are analyzed using a deep learning model trained on Google Colab to detect early signs of disease such as blight, rust, or leaf spot. Once detected, the system not only sends alerts but also automates irrigation schedules based on plant health and soil status—optimizing water usage and improving yield quality.

What You’ll Receive with This Project Kit:

All Essential Project Components:
Includes soil moisture sensors, DHT11/22 sensors, light sensors, ESP32 or Raspberry Pi, Wi-Fi module, irrigation control relays, and image capture modules.

Fully Functional Source Code:
Clean, optimized code for data collection, cloud upload, camera interfacing, and disease detection model inference—integrated with a model trained via Google Colab.

Step-by-Step Working Methods:
A visual build guide with wiring diagrams, cloud dashboard configuration, ML model training tutorial on Colab, and mobile/web control interface setup.

100-Page Detailed Project Report (PDF):
Covers smart farming innovations, related work, detailed methodology, image dataset details, CNN architecture for disease detection, performance graphs, and scope for real-time deployments.

Advanced Crop Monitoring Dashboard:
Access data through a custom web dashboard that visualizes field conditions, disease risk levels, and irrigation history, giving farmers full control from anywhere.