TY  - JOUR
TI  - Weight and Diameter Estimation Using Image Processing and Machine Learning Techniques on Apple Images
AB  - Classification of table fruits according to size is traditionally hand made. But human factors are the cause of faulty classifications. Automatically performing this process with the machines is important in terms of speeding up the process, reducing costs, and minimizing errors. In this study, weight and diameter estimations were made on "Starking" type apples using image processing techniques. Firstly 50 photographs were taken with NIR camera and 830nm long pass filter. Afterwards, edge detection algorithms and morphological operations were performed on the images to obtain the boundaries of the images. Diameter and area information obtained from the binary image were used as attributes. These attributes were given as input to Linear Regression method and estimated. As a result, 93% of the diameters of the apples and 96.5% of the weights could be estimated
AU  - Adem, Kemal
AU  - HEKİM, Mahmut
AU  - CÖMERT, Onur
PY  - 2017
JO  - Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi
VL  - 9
IS  - 3
SN  - 1308-5514
SP  - 147
EP  - 154
DB  - TRDizin
UR  - http://search/yayin/detay/282074
ER  -