Machine Learning Driven Approach towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing

Aifeng Ren*, Adnan Zahid, Ahmed Zoha, Syed Aziz Shah, Muhammad Ali Imran, Akram Alomainy, Qammer H. Abbasi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Citations (Scopus)
114 Downloads (Pure)


In agriculture science, accurate information of moisture content (MC) in fruits and vegetables in an automated fashion can be vital for astute quality and grading evaluation. This demands for a viable, feasible and cost-effective technique for the defect recognition using timely detection of MC in fruits and vegetables to maintain a healthy sensory characteristic of fruits. Here we propose a non-invasive machine learning (ML) driven technique to monitor variations of MC in fruits using the terahertz (THz) waves with Swissto12 material characterization kit (MCK) in the frequency range of 0.75 THz to 1.1 THz. In this regard, multi-domain features are extracted from time-, frequency-, and time-frequency domains, and applied three ML algorithms such as support vector machine (SVM), k-nearest neighbour (KNN) and Decision Tree (D-Tree) for the precise assessment of MC in both apple and mango slices. The results illustrated that the performance of SVM exceeded other classifiers results using 10-fold validation and leave-one-observation-out-cross-validation techniques. Moreover, all three classifiers exhibited 100% accuracy for day 1 and 4 with 80% MC value (freshness) and 2% MC value (staleness) of both fruits' slices, respectively. Similarly, for day 2 and 3, an accuracy of 95% was achieved with intermediate MC values in both fruits' slices. This study will pave a new direction for the real-time quality evaluation of fruits in a non-invasive manner by incorporating ML with THz sensing at a cellular level. It also has a strong potential to optimize economic benefits by the timely detection of fruits quality in an automated fashion.

Original languageEnglish
Pages (from-to)2075-2083
Number of pages9
JournalIEEE Sensors Journal
Issue number4
Early online date25 Oct 2019
Publication statusPublished - 15 Feb 2020


  • fruits
  • Keywords Classification
  • machine learning
  • moisture content
  • terahertz sensing

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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