Edīte Kaufmane, Edgars Edelmers, Kaspars Sudars, Ivars Namatēvs, Arturs Nikulins, Sarmīte Strautiņa, Ieva Kalniņa, Astile Peter. Three-Dimensional Imaging in Agriculture: Challenges and Advancements in the Phenotyping of Japanese Quinces in Latvia. Horticulturae, 9(12), 16 pp. MDPI, 2023.
Bibtex citation:
Bibtex citation:
@article{15863_2023,
author = {Edīte Kaufmane and Edgars Edelmers and Kaspars Sudars and Ivars Namatēvs and Arturs Nikulins and Sarmīte Strautiņa and Ieva Kalniņa and Astile Peter},
title = {Three-Dimensional Imaging in Agriculture: Challenges and Advancements in the Phenotyping of Japanese Quinces in Latvia},
journal = {Horticulturae},
volume = {9},
issue = {12},
pages = {16},
publisher = {MDPI},
year = {2023}
}
author = {Edīte Kaufmane and Edgars Edelmers and Kaspars Sudars and Ivars Namatēvs and Arturs Nikulins and Sarmīte Strautiņa and Ieva Kalniņa and Astile Peter},
title = {Three-Dimensional Imaging in Agriculture: Challenges and Advancements in the Phenotyping of Japanese Quinces in Latvia},
journal = {Horticulturae},
volume = {9},
issue = {12},
pages = {16},
publisher = {MDPI},
year = {2023}
}
Abstract: This study presents an innovative approach to fruit measurement using 3D imaging, focusing on Japanese quince (Chaenomeles japonica) cultivated in Latvia. The research consisted of two phases: manual measurements of fruit parameters (length and width) using a calliper and 3D imaging using an algorithm based on k-nearest neighbors (k-NN), the ingeniously designed “Imaginary Square” method, and object projection analysis. Our results revealed discrepancies between manual measurements and 3D imaging data, highlighting challenges in the precision and accuracy of 3D imaging techniques. The study identified two primary constraints: variability in fruit positioning on the scanning platform and difficulties in distinguishing individual fruits in close proximity. These limitations underscore the need for improved algorithmic capabilities to handle diverse spatial orientations and proximities. Our findings emphasize the importance of refining 3D scanning techniques for better reliability and accuracy in agricultural applications. Enhancements in image processing, depth perception algorithms, and machine learning models are crucial for effective implementation in diverse agricultural scenarios. This research not only contributes to the scientific understanding of 3D imaging in horticulture but also underscores its potential and limitations in advancing sustainable and productive farming practices.
Quartile: Q1