A Novel Method for Identifying Volume Parameters and Monitoring Apple Disease Using Image Processing

Rajaee, Meraj; Jalali, Mina

doi:10.48301/jear.2024.450170.1023

A Novel Method for Identifying Volume Parameters and Monitoring Apple Disease Using Image Processing

Document Type : Original Article

Authors

Meraj Rajaee ¹

Mina Jalali ²

¹ Department of Electrical Engineering, National University of Skills, Tehran, Iran.

² Department of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran.

10.48301/jear.2024.450170.1023

Abstract

The identification and diagnosis of plant diseases have long been considered. This research presents a system for diagnosing the volume and type of apple diseases and the spoilage percentage of rotten apples. To estimate the volume of apples, the method of immersion in water to change the volume of the container was used, ensuring more accurate volume estimation. For disease detection and spoilage analysis, a chamber with constant lighting conditions and a halogen lamp was used. Four images were taken with a camera for better analysis. The volume of apples was calculated through two approximations of the cylinder and incomplete cone. The average error rate in this system was 5%. Also, in the present research, a novel method for feature selection was identified using a combination of the weight feature and the calculated volume of hollow apples. To calculate the percentage of failure of each apple, first, the type of failure was identified. Then, the ratio of loss of each apple relative to the whole apple was calculated and compared with the number obtained from the desired region method, which was accurate. In this study, three major diseases of apples were studied, and an algorithm was written to distinguish these three types of infections from healthy apples. The results showed that the proposed method had the necessary efficiency to calculate the volume and percentage of failure and diagnose the type of apple diseases. In addition, the system's accuracy compared to previous studies increased by up to 95%.

Keywords

Apple Volume

Image Processing

Raspberry Pi 3

OpenCV

Python

Subjects

Electrical Engineering

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