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Key Elements for Plant Nutrition02:35

Key Elements for Plant Nutrition

Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the atmosphere, the...

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Related Experiment Video

Updated: May 28, 2026

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects
15:25

Tomato Analyzer: A Useful Software Application to Collect Accurate and Detailed Morphological and Colorimetric Data from Two-dimensional Objects

Published on: March 16, 2010

Tomato Ripeness Detection and Localization Based on the Intelligent Inspection Robot Platform.

Xinrui Li1, Long Liang1, Yubo Liu2

  • 1College of Smart Agriculture, Nanjing Agricultural University, Nanjing 211800, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an intelligent robot for tomato ripeness detection and field inspection, overcoming limitations of manual labor and existing robotic systems. The robot achieves high accuracy in ripeness recognition and precise localization for efficient greenhouse management.

Keywords:
UWBYOLOv8nintelligent inspection robotlightweight designprecision agriculturetomato ripeness detection

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Area of Science:

  • Agricultural Robotics
  • Computer Vision
  • Precision Agriculture

Background:

  • Tomato field inspection and ripeness detection in China rely heavily on manual labor.
  • Existing robotic solutions face challenges with functionality, environmental adaptability, cost, and positioning accuracy.

Purpose of the Study:

  • To develop an intelligent tomato inspection robot integrating real-time ripeness recognition and precise spatial localization.
  • To address limitations of manual labor and current robotic systems in greenhouse agriculture.

Main Methods:

  • A Raspberry Pi 5 core controller and a lightweight, modular architecture were used for robot construction.
  • A YOLOv8n-based detection model was trained on a multi-dimensional tomato ripeness image dataset.
  • An Ultra-Wideband (UWB) positioning system was integrated for spatial localization.

Main Results:

  • The YOLOv8n model achieved high performance with mAP@0.5 of 87.8% and mAP@0.5:0.95 of 72.7%.
  • Field inspection achieved high detection precisions: 82.22% (immature), 92.66% (half-ripened), and 100% (fully ripe).
  • The UWB system achieved a Root Mean Square Error of 0.231 m, with positioning errors within 0.24 m.

Conclusions:

  • The intelligent robot offers a practical, low-cost, and flexible solution for tomato inspection in greenhouse environments.
  • The integrated system addresses challenges of labor-intensive inspection and delayed harvesting, improving plantation management.
  • The robot demonstrates efficient operation, inspecting a 50-m row in 10 min with a 2-hour battery life.