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Deep learning-driven IoT solution for smart tomato farming.

Akshit Saxena1, Aayushi Agarwal1, Bhavya Nagrath1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.

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Summary
This summary is machine-generated.

This study introduces an IoT smart greenhouse for tomato farming, using sensors and AI to monitor crop conditions and classify ripeness. The system offers real-time data and optimized deep learning for sustainable agriculture.

Keywords:
Deep learningGreenhouseInternet of things (IoT)Precision Agriculture(PA)Tomato productionWireless sensor networks (WSN)

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Area of Science:

  • Agricultural Engineering
  • Computer Science
  • Environmental Science

Background:

  • Increasing global food demand and climate change necessitate sustainable agricultural practices.
  • Precision agriculture (PA) offers solutions for optimizing crop production efficiency.

Purpose of the Study:

  • To develop an Internet of Things (IoT)-based smart greenhouse platform for tomato cultivation.
  • To integrate environmental sensing and deep learning for real-time monitoring and ripeness classification.

Main Methods:

  • Utilized ESP32 wireless sensors for real-time soil moisture, temperature, and humidity data collection.
  • Employed a Raspberry Pi with a Pi Camera and YOLOv8 model for tomato ripeness classification (green, half-ripened, fully ripened).
  • Implemented model optimization techniques (quantization, pruning, TensorRT) to enhance inference speed.

Main Results:

  • Achieved a 35% improvement in inference speed with 52.8% classification accuracy in the initial stage.
  • Measured daily energy consumption: 8.91 Wh for ESP32 sensors and 78 Wh for Raspberry Pi.
  • Demonstrated a functional prototype for real-time monitoring and AI-driven ripeness assessment.

Conclusions:

  • The developed platform provides practical insights into environmental monitoring and AI-based crop assessment for smart farming.
  • The study lays the groundwork for scalable multi-node systems and edge AI integration in greenhouses.
  • Future enhancements include Edge TPU, LoRa, and automated control systems for a fully autonomous greenhouse.