Simple Vision System for Apple Varieties Classification

Aulia Muhammad Taufiq Nasution, Syakir Almas Amrullah

Abstract


Abstract

Apple fruits have their own specific physical signatures among varieties, which are influenced by many preharvest factors. Manual grading techniques have many shortcomings, i.e., subjectivity, inconsistency, physical and psychological fatigue, and the level of experience of the inspecting personnel. This reported work was aimed at designing and testing a computer-based simple vision system for recognizing and grading several variants of apple fruits based on their specific signatures. Images of apple fruits from training samples were processed using consecutive image processing algorithms to extract their signature parameters, i.e., the color and morphological parameters. The extracted Hue color channels and contour vector were memorized as reference data and were compared to the ones from the testing data. The decision was then made by using the K-Nearest Neighbors algorithm. Results show that recognition rates based on color features solely were spread between 84–97%, while they were only 5–77% when based on the morphology feature solely. Changing the rotation of the training data significantly increases the recognition rate (based on the morphology feature (i.e., from 5 to 69%). Meanwhile, combining both color and morphology features may greatly improve the recognition rate.

Keywords: apple’s varieties classification; color signatures; combined color-morphology signatures; morphology signature; vision system

 

Abstrak

Setiap jenis buah apel memiliki penciri fisik spesifik, yang dipengaruhi oleh berbagai faktor pra-panen. Teknik klasifikasi manual memiliki banyak kelemahan, antara lain adalah subjektifitas, ketidakkonsistenan, kelelahan fisik dan psikologis, serta tingkat pengalaman dari petugas yang melakukannya. Tujuan studi ini adalah melakukan proses desain dan pengujian suatu sistem visi sederhana berbasis komputer untuk mengenali dan mengklasifikasi berbagai jenis buah apel berdasarkan penciri spesifiknya. Citra buah apel dari sampel latih diproses dengan berbagai algoritma untuk mengekstraksi berbagai parameter pencirinya, yaitu parameter warna dan bentuk. Informasi histogram kanal warna Hue dan vektor kontur hasil ekstraksi kemudian disimpan sebagai data referensi dan digunakan sebagai pembanding terhadap parameter serupa dari citra data uji. Keputusan diambil menggunakan algoritma K-Nearest Neighbors. Hasil menunjukkan bahwa laju pengenalan berbasis fitur tunggal warna berkisar antara 84–97%, sementara berbasis fitur tunggal morfologi berkisar antara 5–77%. Perubahan orientasi sampel sebagai data training akan meningkatkan laju pengenalan berbasis fitur tunggal morfologi secara signifikan, yaitu dari 5% menjadi 69%. Penggabungan dua fitur penciri warna dan morfologi dapat meningkatkan laju pengenalan lebih baik lagi.

Kata Kunci: klasifikasi jenis buah apel; penciri warna; penciri morfologi, gabungan penciri warna dan morfologi; sistem visi


Keywords


apple’s varieties classification; color signatures; combined color-morphology signatures; morphology signature; vision system; klasifikasi jenis buah apel; penciri warna; penciri morfologi, gabungan penciri warna dan morfologi; sistem visi

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