A Machine Learning-Based Approach for Fruit Grading and Classification

A. Diana Andrusia; T. Mary Neebha; A. Trephena Patricia; THUSNAVIS BELLA MARY.I; Helen Dang

doi:10.1201/9781003212201

A Machine Learning-Based Approach for Fruit Grading and Classification

A. Diana Andrusia, T. Mary Neebha, A. Trephena Patricia, THUSNAVIS BELLA MARY.I, Helen Dang

Mathematics and Computer Studies

Research & Scholarship: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Fruit grading is a vital step in the post harvesting stages of any fruit farming. It directly influences the benefits of fruit farmers. Computer vision based automatic methods provide the efficient and precise grading with respect to the appearance of fruits. This chapter deals with a machine learning based grading system for orange fruits. Weighted fruit features such as circularity, cross sectional diameter along with the statistical features are found under feature extraction step. Multi class Support Vector Machine (MSVM) is used to classify the fruits under the four categories. The proposed method results are compared with state of the art methods. The quantitative results show that, the proposed method yields 96.7% of accuracy which is greater than the other state-of-the-art methods. The results are encouraging to perform additional implementation to customize the proposed work in fruit packing industry.

Original language	American English
Title of host publication	Cyber Security and Operations Management for Industry 4.0
DOIs	https://doi.org/10.1201/9781003212201
State	Published - Dec 20 2022

Disciplines

Computer Sciences

Access to Document

10.1201/9781003212201

Cite this

@inbook{74781a80f173457697216bf8759dc20c,

title = "A Machine Learning-Based Approach for Fruit Grading and Classification",

abstract = " Fruit grading is a vital step in the post harvesting stages of any fruit farming. It directly influences the benefits of fruit farmers. Computer vision based automatic methods provide the efficient and precise grading with respect to the appearance of fruits. This chapter deals with a machine learning based grading system for orange fruits. Weighted fruit features such as circularity, cross sectional diameter along with the statistical features are found under feature extraction step. Multi class Support Vector Machine (MSVM) is used to classify the fruits under the four categories. The proposed method results are compared with state of the art methods. The quantitative results show that, the proposed method yields 96.7% of accuracy which is greater than the other state-of-the-art methods. The results are encouraging to perform additional implementation to customize the proposed work in fruit packing industry. ",

author = "Andrusia, {A. Diana} and Neebha, {T. Mary} and Patricia, {A. Trephena} and MARY.I, {THUSNAVIS BELLA} and Helen Dang",

note = "A. Diana Andrusia, T. Mary Neebha, A. Trephena Patricia, I. Thusnavis Bella Mary, Hien Dang (2022). A Machine Learning-Based Approach for Fruit Grading and Classification. In Ahmed A Elngar, N. Thillaiarasu, Mohamed Elhoseny, K. Martin Sagayam (Eds.). Cyber Security and Operations Management for Industry 4.0 (pp.), CRC Press, Dec 20, 2022",

year = "2022",

month = dec,

day = "20",

doi = "10.1201/9781003212201",

language = "American English",

booktitle = "Cyber Security and Operations Management for Industry 4.0",

}

TY - CHAP

T1 - A Machine Learning-Based Approach for Fruit Grading and Classification

AU - Andrusia, A. Diana

AU - Neebha, T. Mary

AU - Patricia, A. Trephena

AU - MARY.I, THUSNAVIS BELLA

AU - Dang, Helen

N1 - A. Diana Andrusia, T. Mary Neebha, A. Trephena Patricia, I. Thusnavis Bella Mary, Hien Dang (2022). A Machine Learning-Based Approach for Fruit Grading and Classification. In Ahmed A Elngar, N. Thillaiarasu, Mohamed Elhoseny, K. Martin Sagayam (Eds.). Cyber Security and Operations Management for Industry 4.0 (pp.), CRC Press, Dec 20, 2022

PY - 2022/12/20

Y1 - 2022/12/20

N2 - Fruit grading is a vital step in the post harvesting stages of any fruit farming. It directly influences the benefits of fruit farmers. Computer vision based automatic methods provide the efficient and precise grading with respect to the appearance of fruits. This chapter deals with a machine learning based grading system for orange fruits. Weighted fruit features such as circularity, cross sectional diameter along with the statistical features are found under feature extraction step. Multi class Support Vector Machine (MSVM) is used to classify the fruits under the four categories. The proposed method results are compared with state of the art methods. The quantitative results show that, the proposed method yields 96.7% of accuracy which is greater than the other state-of-the-art methods. The results are encouraging to perform additional implementation to customize the proposed work in fruit packing industry.

AB - Fruit grading is a vital step in the post harvesting stages of any fruit farming. It directly influences the benefits of fruit farmers. Computer vision based automatic methods provide the efficient and precise grading with respect to the appearance of fruits. This chapter deals with a machine learning based grading system for orange fruits. Weighted fruit features such as circularity, cross sectional diameter along with the statistical features are found under feature extraction step. Multi class Support Vector Machine (MSVM) is used to classify the fruits under the four categories. The proposed method results are compared with state of the art methods. The quantitative results show that, the proposed method yields 96.7% of accuracy which is greater than the other state-of-the-art methods. The results are encouraging to perform additional implementation to customize the proposed work in fruit packing industry.

U2 - 10.1201/9781003212201

DO - 10.1201/9781003212201

M3 - Chapter

BT - Cyber Security and Operations Management for Industry 4.0

ER -