Harvest Stage Recognition and Potential Fruit Damage Indicator for Berries Based on Hidden Markov Models and the
Marcos Orchard1,2, Carlos Muñoz-Poblete3, Juan Ignacio Huircan4
1Department of Electrical Engineering, Universidad de La Frontera, Temuco 4811230, Chile. morchard@ing.uchile.cl.
Sensors (Basel, Switzerland)
|October 17, 2019
Summary
This study introduces a novel berry harvesting monitoring system using temperature and vibration sensors. It accurately tracks berry handling stages and estimates damage potential without needing basket weight data.
Area of Science:
- Agricultural Engineering
- Sensor Technology
- Data Science
Background:
- Berry harvesting involves multiple stages, each with potential for fruit damage.
- Current monitoring methods may lack efficiency or require extensive data.
- Real-time tracking of post-harvest handling is crucial for quality control.
Purpose of the Study:
- To develop an efficient monitoring system for berry harvesting stages.
- To estimate potential berry damage during handling using sensor data.
- To validate a novel approach independent of basket weight.
Main Methods:
- Utilized hidden Markov models (HMMs) to characterize harvesting process stages.
- Employed the Viterbi algorithm for state trajectory inference.
- Integrated temperature and vibration sensor data for real-time analysis.
Main Results:
- Successfully identified distinct berry harvesting stages (picking, waiting, transport, arrival).
- Developed a real-time potential damage indicator based on estimated state trajectories.
- Demonstrated system effectiveness without requiring basket weight information.
Conclusions:
- The proposed sensor-based system offers an efficient and effective method for monitoring berry harvesting.
- This technology enables real-time damage assessment, improving post-harvest quality management.
- The methodology provides a valuable alternative to existing, potentially less efficient, industry practices.
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