Version 1
: Received: 26 June 2023 / Approved: 26 June 2023 / Online: 26 June 2023 (05:03:33 CEST)
How to cite:
Agrahari Baniya, A.; Lee, T.(.; Eklund, P.W.; Aryal, S. Current State, Data Requirements and Generative AI Solution for Learning-based Computer Vision in Horticulture. Preprints2023, 2023061738. https://doi.org/10.20944/preprints202306.1738.v1
Agrahari Baniya, A.; Lee, T.(.; Eklund, P.W.; Aryal, S. Current State, Data Requirements and Generative AI Solution for Learning-based Computer Vision in Horticulture. Preprints 2023, 2023061738. https://doi.org/10.20944/preprints202306.1738.v1
Agrahari Baniya, A.; Lee, T.(.; Eklund, P.W.; Aryal, S. Current State, Data Requirements and Generative AI Solution for Learning-based Computer Vision in Horticulture. Preprints2023, 2023061738. https://doi.org/10.20944/preprints202306.1738.v1
APA Style
Agrahari Baniya, A., Lee, T.(., Eklund, P.W., & Aryal, S. (2023). Current State, Data Requirements and Generative AI Solution for Learning-based Computer Vision in Horticulture. Preprints. https://doi.org/10.20944/preprints202306.1738.v1
Chicago/Turabian Style
Agrahari Baniya, A., Peter Werner Eklund and Sunil Aryal. 2023 "Current State, Data Requirements and Generative AI Solution for Learning-based Computer Vision in Horticulture" Preprints. https://doi.org/10.20944/preprints202306.1738.v1
Abstract
The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.
Keywords
computer vision; deep learning; signal quality; horticulture; datasets; enhancement; generative AI; super-resolution
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
