@inproceedings{24812202ceee4f91b4034818de613334,
title = "Estimating melon yield for breeding processes by machine-vision processing of UAV images",
abstract = "Monitoring plants, for yield estimation in melon breeding, is a highly labor-intensive task. An algorithmic pipeline for detection and yield estimation of melons from top-view images of a melon's field is presented. The pipeline developed at the individual melon level includes three main stages: melon recognition, feature extraction, and yield estimation. For each region of interest classified as a melon, the melon features were extracted by fitting an ellipse to the melon contour. A regression model that ties the ellipse features to the melon's weight is presented. The modified R2 value of the regression model was 0.94. Comparing yield estimation to ground truth, the average estimation error was 16%. The yield accuracy is highly dependent on the ellipse estimation accuracy, with promising results of only 4% error for the best ellipse-fitted melons.",
keywords = "Active contour, Breeding, CNN, Machine learning, Melon, Phenotyping, Yield estimation",
author = "A. Kalantar and A. Dashuta and Y. Edan and A. Dafna and A. Gur and I. Klapp",
note = "Publisher Copyright: {\textcopyright} Wageningen Academic Publishers 2019; 12th European Conference on Precision Agriculture, ECPA 2019 ; Conference date: 08-07-2019 Through 11-07-2019",
year = "2019",
month = jan,
day = "1",
doi = "10.3920/978-90-8686-888-9_47",
language = "English",
series = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
publisher = "Wageningen Academic Publishers",
pages = "381--387",
editor = "Stafford, {John V.}",
booktitle = "Precision Agriculture 2019 - Papers Presented at the 12th European Conference on Precision Agriculture, ECPA 2019",
address = "Netherlands",
}