Effectiveness of Generative AI Tool to Determine Fruit Quality: Watermelon Case Study
To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Horticulturae |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2311-7524/11/3/308 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | To select a good quality watermelon, one needs the ability and experience to recognize specific patterns in its visual characteristics. As buyers usually cannot taste the watermelon beforehand, the outer patterns of a good quality watermelon may vary depending on the perspective of the purchaser. As a result, there is a gradual adoption of new generative artificial intelligence (AI) tools in the field of horticulture. These tools are expected to minimize bias in human perception when determining the quality of a watermelon based on its outer characteristics. This study aimed to compare the quality of watermelons selected by generative AI with a panel sensory evaluation test. The results of the two case studies indicate a significant difference in the quality of the generative AI-selected watermelons. As an average, watermelon evaluators favored the watermelons selected by ChatGPT as the best based on the Wilcoxon rank sum test and paired <i>t</i>-test (<i>p</i> < 0.05). In conclusion, watermelons can be selected by ChatGPT with minimal effort, promptly meeting consumer expectations. |
|---|---|
| ISSN: | 2311-7524 |