Research Information System
INTERNATIONAL JOURNAL ON E-LEARNING CORPORATE, GOVERNMENT, HEALTHCARE & HIGHER EDUCATION, vol.5, no.1, pp.1007-1010, 2020 (Scopus)
Fruit diseases are manifested by deformations during or after harvesting the
components in the fruit, when the infestation is caused by spores, fungi,
insects or other contaminants. In early agricultural practices, it is thought that
non-destructive examination is possible with the analysis of pre-harvest fruit
leaves and early diagnosis of the disease, while post-harvest detection and
classification of fruit disease is possible by evaluating simple image processing
techniques. Diseases of rotten or stained fruits without destruction. In this
way, the disease will be identified and classified and the awareness of the
producer for the next harvest will be provided. For this purpose, studies were
carried out with apple and quince fruit, images were determined using still
fruit pictures and machine learning, and disease classification was provided
with labels. Image processing techniques are a system that detects disease
made to a real-time camera and prints it on the screen. Within the scope of this
study, the data set was created and images of 22 apples and 18 quinces were
taken. The image was classified by similarities in the literature review. The
success of the proposed Convolutional Neural Network architecture in
recognizing the disease was evaluated. By comparing the trained network,
AlexNet architecture, with the proposed architecture, it has been determined
that the success of image recognition has increased with the proposed method.